WO2016103322A1 - Analysis system - Google Patents

Analysis system Download PDF

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Publication number
WO2016103322A1
WO2016103322A1 PCT/JP2014/083913 JP2014083913W WO2016103322A1 WO 2016103322 A1 WO2016103322 A1 WO 2016103322A1 JP 2014083913 W JP2014083913 W JP 2014083913W WO 2016103322 A1 WO2016103322 A1 WO 2016103322A1
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WO
WIPO (PCT)
Prior art keywords
side effect
drug
information
period
prescription
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Application number
PCT/JP2014/083913
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French (fr)
Japanese (ja)
Inventor
淳平 佐藤
島田　和之
徹 久光
Original Assignee
株式会社日立製作所
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Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to PCT/JP2014/083913 priority Critical patent/WO2016103322A1/en
Publication of WO2016103322A1 publication Critical patent/WO2016103322A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage

Definitions

  • the present invention relates to an analysis system that supports analysis of drug side effects.
  • Patent Document 1 includes “a side effect, a name of a drug that can cause the side effect, a“ frequency of occurrence ”of a side effect caused by the drug, a“ clinical test method ”, a“ clinical observation method ”, a“ prevention method ”, and a“ coping method ”.
  • Information storage means for storing side-effect related information such as" Precautions for elderly people, pregnant / child / nursing women, newborns, low birth weight infants, infants / children, etc. ".
  • the information storage means is searched based on the side effect specified by the user and the drug name, the causative drug name causing the side effect is specified and output to the user side, and the side effect related information for the causative drug is also stored in the information storage means And a means capable of obtaining and outputting to the user side. (See summary).
  • an analysis system including a processor and a storage device connected to the processor, wherein the storage device includes case information including a patient's disease and hospitalization period, and a prescription period including a prescription period of the drug for the patient.
  • test information including the patient test result
  • knowledge information including determination conditions for determining whether the test result is a side effect
  • the processor stores the test recorded in the test information
  • a side effect improvement period is calculated from the change in results
  • the prescription period is acquired from the prescription information, and among the prescription drugs, a drug other than a prescription medicine continuously prescribed after the start of the improvement period
  • An analysis system characterized in that side effect-causing drug information indicating that the drug is related to the side effect is calculated, and information on the drug related to the side effect is output.
  • FIG. 1 It is a block diagram which shows the structure of the side effect analysis assistance system of 1st Example of this invention. It is a figure which shows the structural example of the case information table of 1st Example of this invention. It is a figure which shows the structural example of the prescription information table of 1st Example of this invention. It is a figure which shows the structural example of the test
  • FIG. 1 is a block diagram showing the configuration of a side effect analysis support system according to the first embodiment of the present invention.
  • the side effect analysis support system of this embodiment is a computer system that analyzes side effects and effects of each drug for each case, visualizes and outputs the analysis results, and includes a hospital information system 120, a network 140, and an input / output terminal 130. It consists of.
  • the input / output terminal 130 includes an input unit (not shown) such as a keyboard, a mouse, or a touch panel, an output unit (not shown) such as a display, and a communication unit (not shown) that communicates with the data server 100 and the like. And one or more personal computers.
  • a portable terminal such as a PDA, PHS, mobile phone, smartphone, or tablet terminal having an input unit such as a button or a touch panel, an output unit such as a display, and a communication unit that communicates with the data server 100 or the like is input / output terminal 130. It may be used as
  • the input / output terminal 130 is installed in a medical institution (health care provider) such as a hospital or clinic, a national institution such as the Ministry of Health, Labor and Welfare, and used by the user.
  • the data server 100 may be installed in a data center or in each organization. By installing the data server 100 in the data center, privacy information such as personal information of the user and data collected from the user can be managed centrally, so that security such as information leakage prevention can be reliably managed.
  • the data server 100 may be installed in a healthcare provider.
  • the user of the input / output terminal 130 is a doctor, an administrator of each organization, and a manager of each organization.
  • the user operates the input / output terminal 130 to analyze and visualize the side effects and effects of the drug using the information system shown in the present embodiment.
  • the data server 100 includes an accumulated data acquisition unit 119, a control unit 101, an output unit 102, a memory 103, a communication unit 104, a display screen generation unit 105, a case data extraction unit 106, a prescription data extraction unit 107, and an examination that are connected to each other.
  • a database 117, a side effect analysis database 118, and a medical cost calculation unit 151 are configured.
  • the medical cost calculation unit 151 is a processing unit used in the third embodiment, and is not necessary in the first and second embodiments.
  • the integrated database 116 stores data accumulated in the case database 121, the examination information database 122, and the prescription information database 123.
  • the knowledge database 117 includes a side effect knowledge information table 500 (see FIG. 5), a setting information table 600 (see FIG. 6), a disease information table 700 (see FIG. 7), and a drug information table 800 (see FIG. 8). Including. The configuration of these tables will be described later with reference to each drawing.
  • the side effect analysis database 118 includes a side effect case table 900 (see FIG. 9), a side effect drug table 1000 (see FIG. 10), a medicinal effect information table 1100 (see FIG. 11), a side effect risk table 1200 (see FIG. 12), And a medicinal effect evaluation table 1300 (see FIG. 13). The configuration of these tables will be described later with reference to each drawing.
  • the accumulated data acquisition unit 119 acquires and integrates data accumulated in the case database 121, the examination information database 122, and the prescription information database 123 stored in the hospital information system 120 installed in the healthcare provider. Store in the database 116.
  • the accumulated data acquisition unit 119 may be activated directly by the user, or may be activated periodically at a time preset by the user (for example, every Saturday night). Further, the accumulated data acquisition unit 119 may be activated at a timing when data in the case database 121, the examination information database 122, and the prescription information database 123 are updated.
  • the control unit 101 includes, for example, a processor that executes a program stored in the memory 103, and controls each unit of the data server 100.
  • the memory 103 is a storage device such as a DRAM (Dynamic Random Access Memory), and stores data referred to by each unit of the data server 100 (for example, a program executed by the control unit 101).
  • DRAM Dynamic Random Access Memory
  • the program executed by the control unit 101 is read from a nonvolatile storage device (not shown) in the data server 100 such as an HDD (Hard Disk Drive), loaded into the memory 103, and stored in the control unit 101. Executed by the processor.
  • the program executed by the control unit 101 is provided to the data server 100 via a removable medium (CD-ROM, flash memory, etc.) or a network, and is stored in a nonvolatile storage device that is a non-temporary storage medium. For this reason, the data server 100 may have an interface for reading data from a removable medium.
  • the integrated database 116, the knowledge database 117, and the side effect analysis database 118 are stored in a nonvolatile storage device (not shown) in the data server 100, such as an HDD (Hard Disk Drive).
  • a nonvolatile storage device such as an HDD (Hard Disk Drive).
  • the databases 116, 117, and 118 may be stored in the memory 103.
  • Data stored in the databases 116, 117, and 118 is read from the non-volatile storage device and expanded in the memory 103 in a process described later. Note that when the databases 116, 117, and 118 are stored in the memory 103, the data stored in the databases 116, 117, and 118 may be acquired from the memory 103.
  • the output unit 102 is a device that outputs a result of processing by the data server 100, and may be a display device, for example.
  • the communication unit 104 is connected to the network 140 and communicates with the input / output terminal 130.
  • the selection unit 113, the statistical analysis unit 114, and the alignment control unit 115 are processing units that execute processing for realizing the functions of the data server 100, and each may be realized by dedicated hardware, or by software It may be realized.
  • each processing unit is realized by software, in the following description, the processing executed by each processing unit is actually executed by the control unit 101 in accordance with an instruction described in a program stored in the memory 103. Details of the processing executed by each processing unit described above will be described later.
  • the data server 100 is a computer system configured on a single physical computer or a plurality of logically or physically configured computers. It may operate on a thread, or may operate on a virtual computer constructed on a plurality of physical computer resources.
  • the case database 121, the examination information database 122, the prescription information database 123, and the medical practice information database 124 are stored in another storage device (not shown) in the hospital information system 120 such as an HDD (Hard Disk Drive). May be.
  • the case database 121 includes a case information table 200 (see FIG. 2).
  • the inspection information database 122 includes an inspection information table 400 (see FIG. 4).
  • the prescription information database 123 includes a prescription information table 300 (see FIG. 3).
  • the medical practice information database 124 is a table used in the third embodiment, and is unnecessary in the first and second embodiments.
  • the medical practice information database 124 includes a medical practice cost information table 3300 (see FIG. 28) and a treatment information table 3500 (see FIG. 29). The configuration of these tables will be described later with reference to each drawing.
  • the network 140 connects the data server 100, the hospital information system 120, and the input / output terminal 130.
  • the data server 100 communicates with the hospital information system 120 and the input / output terminal 130 via the network 140.
  • the network 140 connects devices using wired communication using a LAN (Local Area Network) cable or wireless communication using a wireless LAN.
  • the network 140 may use another wide area network such as the Internet, VPN, mobile phone communication network, and PHS communication network.
  • the integrated database 116 of the first embodiment includes a case information table 200 (FIG. 2) for managing basic information for each case, a prescription information table 300 (FIG. 3) for recording prescription information for each case, It comprises an inspection information table 400 (FIG. 4) for storing inspection information.
  • a case information table 200 for managing basic information for each case
  • a prescription information table 300 for recording prescription information for each case
  • It comprises an inspection information table 400 (FIG. 4) for storing inspection information.
  • FIG. 2 to FIG. 13 and FIG. 28 to FIG. 31 are examples of data necessary for realizing the side effect analysis support system of each embodiment of the present invention, and may be shown depending on the functions implemented in the system. May be included, or some of the illustrated fields may not be included.
  • FIG. 2 is a diagram illustrating a configuration example of the case information table 200.
  • the case information table 200 is a table for storing case information records acquired from the case database 121, and includes a patient ID 201, gender 202, hospitalization flag 203, diagnosis date 204, hospitalization date 205, discharge date 206, and disease name. 207 fields are included.
  • the patient ID 201 is identification information for uniquely identifying a patient.
  • the gender 202 is the gender of the patient, and may be represented by a flag of “0” or “1”.
  • the hospitalization flag 203 is a flag indicating whether it is hospital hospital data or outpatient data.
  • the diagnosis date 204 is an outpatient diagnosis date of a medical institution.
  • the hospitalization date 205 and the discharge date 206 are the hospitalization date and discharge date of the medical institution, respectively.
  • the disease name 207 is the name of the disease.
  • the hospitalization flag 203 is “1”, this indicates hospitalization data, and “0” is stored in the diagnosis date 204. On the other hand, if the hospitalization flag 203 is “0”, it indicates outpatient data, and “0” is stored in the hospitalization date 205 and the discharge date 206.
  • each case information record of the case information table 200 One hospitalization of one patient becomes one case.
  • the case information record 200A of the case information table 200 shown in FIG. 2 indicates that a male patient whose patient ID is “## 1” is used for the treatment of diabetes from April 1, 2014 to April 26, 2014. Shows that he was hospitalized until the day.
  • FIG. 3 is a diagram illustrating a configuration example of the prescription information table 300.
  • the prescription information table 300 is a table for storing prescription information records acquired from the prescription information database 123, and includes fields of a patient ID 301, a drug name 302, a prescription start date 303, and a prescription end date 304.
  • the patient ID 301 is identification information for uniquely identifying a patient.
  • the drug name 302 is information for identifying a drug prescribed to the patient.
  • the prescription start date 303 and the prescription end date 304 are the date when the prescription of the drug is started and the date when the prescription is completed.
  • the prescription information records 300A and 300B of the prescription information table 300 shown in FIG. 3 in the case where the patient ID is “## 1”, the drug A is prescribed in the period from April 1 to 14, 2014. It shows that.
  • the prescription information records 300C to 300D indicate that the medicine B was prescribed during the period from April 3rd to 20th, 2014.
  • the prescription information record 300E indicates that the medicine C was prescribed during the period from April 14th to 18th, 2014.
  • the prescription information table 300 may have a field for storing the prescription usage / dose of each drug (not shown).
  • FIG. 4 is a diagram illustrating a configuration example of the inspection information table 400.
  • the examination information table 400 is a table that stores examination information records acquired from the examination information database 122, and includes fields of a patient ID 401, an examination item 402, an examination date 403, and an examination value 404.
  • the patient ID 401 is identification information for uniquely identifying a patient.
  • the inspection item 402 is information for identifying the inspection item.
  • the inspection date 403 is the date when the inspection is performed.
  • the inspection value 404 is a result of the inspection.
  • the examination information table 400 may include information indicating examination results other than those described above, such as an image examination (for example, CT image examination), and information related to the patient's subjective symptoms such as “nausea” or “vomiting”. May be included.
  • an image examination for example, CT image examination
  • information related to the patient's subjective symptoms such as “nausea” or “vomiting”. May be included.
  • the knowledge database 117 includes a side effect knowledge information table 500 (FIG. 5) that manages test items for side effects and test value thresholds for determining side effects, and a period for extracting drugs that cause side effect details as analysis targets.
  • a setting information table 600 (FIG. 6) for managing the disease
  • a disease information table 700 (FIG. 7) for managing symptoms for the disease
  • a drug information table 800 (FIG. 8) for managing the effect of the drug and known side effects. Composed.
  • FIG. 5 is a diagram showing a configuration example of the side effect knowledge information table 500.
  • the side effect knowledge information table 500 includes fields of side effect 501, side effect details 502, test item 503, abnormal value (male) 504, and abnormal value (female) 505.
  • the side effect 501 is side effect information (for example, name).
  • the side effect details 502 are detailed information on the side effects.
  • the inspection item 503 is an inspection item for the side effect details.
  • the abnormal value (male) 504 is a threshold value for men for determining that a side effect with the detailed side effect has occurred.
  • the abnormal value (female) 505 is a threshold value for women for determining that a side effect with the detailed side effect has occurred.
  • the side effect “hepatic dysfunction” and the side effect detail “increase in ALT (GPT)” indicate the test item “ALT (GPT)”.
  • FIG. 6 is a diagram illustrating a configuration example of the setting information table 600.
  • the setting information table 600 includes fields for side effect details 601 and extraction target days 602.
  • the side effect details 601 are detailed information on side effects.
  • the extraction target days 602 is the number of days for extracting a drug that causes the side effect details as an analysis target.
  • the prescription period of the causative agent for analyzing the side effect detail “ALT (GPT) increase” is 30 days, and the side effect detail “ALT (GPT) increase”. It shows that the prescription period of the causative agent for analyzing is 30 days.
  • the setting information table 600 includes a drug name field, and the extraction target period may be set for the combination of the side effect details and the drug. Further, the information stored in the setting information table 600 may be settable by the user for an arbitrary period.
  • FIG. 7 is a diagram illustrating a configuration example of the disease information table 700.
  • the disease information table 700 includes fields for a disease name 701 and a symptom 702.
  • the disease name 701 is a name of a disease.
  • Symptom 702 is a symptom for the disease name.
  • the disease “chronic hepatitis C” indicates that ALT (GPT) increases and AST (GOT) increases, and the disease name “diabetes” Indicates that a symptom of increased HbA1c occurs.
  • FIG. 8 is a diagram showing a configuration example of the medicine information table 800.
  • Drug information table 800 includes fields of drug name 801, effect 802, known side effect 803, and corresponding disease 804.
  • the drug name 801 is information for identifying a drug.
  • the effect 802 is an effect of the drug.
  • Known side effects 803 are known side effects of the drug.
  • Corresponding disease 804 is a disease for which the drug is prescribed.
  • the example 800A of the drug information record in the drug information table 800 illustrated in FIG. 8 indicates that the drug name “drug A” has the effect “decrease Cre” and the side effect “increase ALT (GPT)”.
  • the medicine information table 800 may have a field for storing the incidence of known side effects of each medicine described in an attached document (not shown).
  • the relationship between the drug name and the corresponding disease may be created by a healthcare provider or created by an organization that analyzes and visualizes side effects.
  • the relationship between the drug name and the corresponding disease may be stored in the medical practice information database 124 instead of the knowledge database 117. Furthermore, what is stored in advance in the data server may be used.
  • the effects and side effects can be analyzed by narrowing down the drugs to be analyzed or the drugs having side effects based on the effects for each drug and the known side effect information, and the accuracy of analysis of the known effects and side effects can be improved.
  • the effects and side effects can be analyzed by excluding drugs with analysis target effects or side effects based on the effects for each drug and known side effect information, and unknown effects and side effects can be analyzed with high accuracy.
  • the side effect analysis database 118 of the first embodiment includes a side effect case table 900 (FIG. 9) for storing information on side effect cases, a side effect drug table 1000 (FIG. 10) for storing information on drugs that cause side effects, A medicinal effect information table 1100 (FIG. 11) for storing information, a side effect risk table 1200 (FIG. 12) for storing side effect risk information, and a medicinal effect evaluation table 1300 (FIG. 13) for storing medicinal effect information.
  • the side effect case table 900 for storing information on side effect cases
  • a side effect drug table 1000 for storing information on drugs that cause side effects
  • a medicinal effect information table 1100 (FIG. 11) for storing information
  • a side effect risk table 1200 (FIG. 12) for storing side effect risk information
  • a medicinal effect evaluation table 1300 (FIG. 13) for storing medicinal effect information.
  • FIG. 9 is a diagram showing a configuration example of the side effect case table 900.
  • the side effect case table 900 is a table for storing information on side effect cases extracted by the side effect case extraction unit 110.
  • the patient ID 901, the side effect details 902, the side effect count 903, the side effect start date 904, the side effect end date 905, and the improvement period start It includes fields for date 906 and improvement period end date 907.
  • the patient ID 901 is identification information for uniquely identifying a patient.
  • the side effect details 902 are detailed information on side effects.
  • the number of side effects 903 is a number for identifying a side effect that has occurred. Of the records of side effects with the same patient ID 901 and side effect details 902, the maximum value of the number of side effects 903 is the number of times the side effect has occurred in the patient.
  • the side effect start date 904 and the side effect end date 905 are the start date and the end date of the side effect, respectively, the first day when the test value is determined to be abnormal and the first date when the test value is determined to be normal. is there.
  • the improvement period start date 906 and the improvement period end date 907 are the start date and end date of the improvement period from the side effect.
  • the patient whose patient ID is “## 1” has experienced ALT (GPT) increase side effects twice, and the first side effect start date is 2014.
  • the side effect end date is April 25, 2014
  • the improvement period start date is April 15, 2014
  • the improvement period end date is April 25, 2014.
  • the second side effect start date is May 1, 2014
  • the side effect end date is May 10, 2014
  • the improvement period start date and improvement period end date is “0” (that is, there is no improvement period).
  • FIG. 10 is a diagram showing a configuration example of the side effect medicine table 1000.
  • the side effect drug table 1000 is a table for storing information on drugs that cause side effects calculated by the side effect cause information calculation unit 111.
  • the patient ID 1001 is identification information for uniquely identifying a patient.
  • the side effect 1002 is side effect information (for example, name).
  • the side effect detail 1003 is detailed information on the side effect.
  • the number of side effects 1004 is the number of times the side effects have occurred.
  • the drug name 1005 is the name of the drug to be subjected to side effect analysis.
  • the side effect cause drug information 1006 is a flag indicating whether the drug is a side effect cause drug candidate.
  • DDSA1007 is the difference in the number of days from the prescription start date to the side effect start date.
  • DDSE1008 is the difference in the number of days from the prescription end date to the side effect start
  • the side effect medicine table 1000 may record the total number of days prescribed between the prescription start date and the side effect start date, the total prescription dose, and the like (not shown).
  • the patient whose patient ID is “## 1” has the side effect of “liver dysfunction” and the side effect details of “ALT (GPT) increase”. Since the causal drug information 1006 is “1”, it indicates that drug A is a candidate drug for a side effect, DDSA is 7 days, and DDSE is 0 days. Further, since the drug B side-effect-causing drug information 1006 is “0”, DDSA is “NULL”, and DDSE is “NULL”, it indicates that drug B is not a candidate drug for causing a side effect.
  • FIG. 11 is a diagram showing a configuration example of the medicinal effect information table 1100.
  • the medicinal effect information table 1100 is a table for storing medicinal effect information calculated by the medicinal effect information calculating unit 112, and includes fields of a patient ID 1101, a drug name 1102, an effect 1103, a total prescription date 1104, and an improvement degree 1105.
  • the patient ID 1101 is identification information for uniquely identifying a patient.
  • the drug name 1102 is the name of the drug.
  • the effect 1103 is an effect of the drug.
  • the total number of prescription days 1104 is the number of days on which the drug is prescribed.
  • the degree of improvement 1105 is the amount of change in the test value due to the prescription of the drug, and stores the value calculated in the medicinal effect information calculation process (see FIG. 18).
  • the medicinal effect information table 1100 may record the total dose of the prescription (not shown).
  • FIG. 12 is a diagram showing a configuration example of the side effect risk table 1200.
  • the side effect risk table 1200 is a table for storing information on the side effect risk of the drug calculated by the statistical analysis unit 114.
  • the side effect detail 1201 is detailed information on the side effect.
  • the drug name 1202 is the name of the drug that causes the side effect.
  • the total number of uses 1203 is the number of cases using the drug.
  • the use number 1204 in the case of a side effect is the number of cases in which a side effect has occurred using the drug (the number of use in a side effect case).
  • the side effect risk 1205 is a side effect risk of the drug, and stores the value calculated by the side effect risk calculation process (see FIG. 19).
  • the risk prescription days 1206 are prescription days with a high possibility of occurrence of side effects, and the values calculated in the side effect risk calculation process (see FIG. 19) are stored.
  • the side effect risk record example 1200A of the side effect risk table 1200 shown in FIG. 12 the total use number of the drug A is 1200 cases, and the side effect detail “increased ALT (GPT)” is 120 cases. Therefore, the side effect risk of an increase in ALT (GPT) related to the drug name “A” is 3.2, indicating that the risk prescription day with a high possibility of causing the side effect of increasing ALT (GPT) is 10 days. .
  • FIG. 13 is a diagram showing a configuration example of the medicinal effect evaluation table 1300.
  • the medicinal effect evaluation table 1300 is a table for storing information on medicinal effect evaluation of the drug calculated by the statistical analysis unit 114, and includes fields of an effect 1301, a drug name 1302, a total use number 1303 and a medicinal effect evaluation 1304.
  • the effect 1301 is a drug effect.
  • the drug name 1302 is the name of the drug that generates the effect.
  • the total use number 1303 is the number of cases using the drug.
  • the medicinal effect evaluation 1304 is an evaluation of the medicinal effect of the drug, and includes an increase / decrease in a test value and a change in the size of a lesion in a unit period by prescribing the drug. In the medicinal effect evaluation 1304, the value calculated by the medicinal effect evaluation calculating process (see FIG. 20) is stored.
  • the total number of drugs A used is 1200 cases, and when drug A is used, Cre decreases by 0.21 / day of the effect. Indicates to do.
  • selection of a drug in consideration of the drug effect and the risk of side effect can be supported by the side effect risk of each drug, the risk prescription time, and the information of the drug effect evaluation. For example, select drugs with low side effect risk from drugs with the same effect, prescribe drugs with high drug efficacy evaluation and high risk of side effects within the period up to the dangerous prescription days, and then switch to drugs with low drug efficacy and side effect risk. be able to.
  • FIG. 14 is a flowchart of analysis processing executed by the data server 100 of the first embodiment.
  • the control unit 101 reads out case data from the integrated database 116 and stores it in the memory 103 (S1401).
  • the case data is data related to the case, and includes, for example, information for identifying the case, information on prescription drugs corresponding to the case, information on test results corresponding to the case, and the like.
  • the control unit 101 may read all data stored in the integrated database 116 and store it in the memory 103 in step S1401.
  • control unit 101 reads knowledge data from the knowledge database 117 and stores it in the memory 103 (S1402).
  • the control unit 101 may read all data stored in the knowledge database 117 and store it in the memory 103 in step S1402.
  • the control unit 101 activates the display screen generation unit 105.
  • the display screen generation unit 105 provides a screen for the user to select details of the side effects to be analyzed and visualized, effects, and analysis options, and receives input of these parameters (S1403).
  • the side effect details selected in step S1403 may be referred to as target side effect details, and the effect may be referred to as the target effect.
  • the analysis screen 2100 (FIG. 21) is displayed on the input / output terminal 130.
  • FIG. 21 is a diagram showing an example of an analysis screen 2100 displayed by the input / output terminal 130 when the user selects the side effect details and effects to be analyzed and visualized and the analysis option in the first embodiment.
  • the analysis screen 2100 includes an analysis target side effect selection field 2101, an analysis target effect selection field 2102, an analysis option selection field 2103, a “change setting” button 2104, an “analysis execution” button 2105, and an analysis result display item.
  • a selection field 2106, an analysis result list display area 2107, a “reference details” button 2108, a statistical analysis selection option field 2109, and a “statistical analysis” button 2110 are included.
  • the user operates the check box in the side effect selection field 2101 to select the side effect details to be analyzed and visualized from one or more side effect details displayed in the side effect selection field 2101.
  • the user operates the check box in the analysis target effect selection field 2102 to select an effect to be analyzed and visualized from one or more effects displayed in the analysis target effect selection field 2102.
  • the user selects a check box in the analysis option selection field 2103, and selects an analysis option to be used for analysis from one or more analysis options displayed in the analysis option selection field 2103.
  • FIG. 22 is a diagram illustrating an example of a setting screen 2300 displayed on the input / output terminal 130 when the user changes various settings in the first embodiment.
  • the setting screen 2300 includes a test value abnormality threshold change area 2301, an improvement period change area 2401, a post-side effect analysis setting change area 3001, a “save” button 2302, and a “cancel” button 2303.
  • the post-side effect analysis setting change area 3001 is a setting area used in the second embodiment, and is not necessary in the first embodiment. For this reason, in the side effect analysis support system that does not have the function of the second embodiment, the post-side effect analysis setting change area 3001 may not be displayed.
  • the user changes the threshold value by operating an up / down button 23011 for abnormal values (male) and an up / down button 23012 for abnormal values (female) in various inspection items in the inspection value abnormal threshold change area 2301.
  • the user can directly input the threshold value of the side effect details to be set directly in the test value abnormality threshold value change area 2301.
  • the user operates the drop button 24011 in the improvement period change area 2401 to change the calculation definition of the improvement period used for analysis and visualization from the calculation definitions of the plurality of improvement periods displayed in the drop-down list (not shown). select. Specifically, the period during which the test value monotonously decreases / increases is used as the improvement period, the test value is fitted with a linear model, and the period during which the fitted value decreases / increases is used as the improvement period. You can choose the right method.
  • the user operates the drop button 24012 in the improvement period change area 2401 to select a calculation target section for the improvement period used for analysis and visualization from a plurality of calculation target sections for the improvement period displayed in a drop-down list (not shown). To do. Specifically, the last monotonic decrease / increase period during the side effect period is set as the calculation target section of the improvement period, and all the monotonic decrease / increase periods during the side effect period are set as the calculation period of the improvement period. A method can be selected.
  • the user operates the “Save” button 2302 when saving the changed setting value, and operates the “Cancel” button 2303 when not saving. If any one of the buttons is operated, the setting screen 2300 is terminated and the analysis screen 2100 is returned to.
  • FIG. 15 is a flowchart of a process in which the side effect case extraction unit 110 extracts a case in which a side effect has occurred.
  • the side effect case extraction unit 110 extracts each examination item corresponding to each side effect detail selected in step S1403 from the data of the side effect knowledge information table 500 stored in the memory 103 (S1501).
  • step S1502 the side effect case extraction unit 110 acquires records of all patient IDs in the examination information table 400, and determines whether the processing is completed for all patient IDs (S1502). As a result, if the process has not been completed for some patient IDs, the processes of steps S1503 to S1507 are repeatedly executed for each patient ID.
  • step S1503 the side effect case extraction unit 110 determines whether “use disease information” 21031 is selected in the analysis option selection field 2103 in step S1403 (S1503). As a result, if “use disease information” 21031 is not selected, the process proceeds to step S1505. On the other hand, if “use disease information” 21031 is selected, the side effect case extraction unit 110 selects a patient to be analyzed based on the disease information (S1504). Specifically, a disease name in which each side effect detail selected in step S1403 is stored as a symptom is extracted, and a patient ID whose extracted disease name is stored in a disease name 207 in the case information table 200 is analyzed.
  • the disease name “chronic hepatitis C” is extracted as one of the extracted disease names, and “ The patient ID “## 2” in which “chronic hepatitis C” is stored is excluded from the analysis target.
  • the side effect case extraction unit 110 extracts an abnormal value corresponding to gender (S1505). For example, if “increased ALT (GPT)” is selected in the side effect details, since the patient whose patient ID is “## 1” is a male, an abnormal value (male) of “exceeding 42” is reported as side effect knowledge. Extracted from the information table 500.
  • the side effect case extraction unit 110 calculates the side effect period and the number of side effects (S1506). For example, when the side effect detail “increased ALT (GPT)” is selected and the patient ID is analyzed for “## 1”, the patient ID is “## 1” and the examination item is “ALT (GPT)”. Are acquired from the inspection information table 400.
  • the test date when the test value first corresponds to the abnormal value (male) “exceeding 42” is set as the side effect start date, and thereafter, the test date or the final test date that no longer corresponds to the abnormal value is calculated as the side effect end date. Also, the number of side effects occurring during this side effect period is calculated as the number of side effects.
  • the side effect case extraction unit 110 associates the patient ID, the side effect details, the number of side effects, the start date and the end date of the side effect period, and registers them in the fields 901 to 905 of the side effect case table 900 (S1507).
  • a side effect case can be automatically extracted by detecting a side effect based on a test value and a predefined abnormal value by the side effect case extraction process.
  • step S1502 If it is determined in step S1502 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1405.
  • step S1405 the control unit 101 activates the examination information interpretation unit 109, and extracts the improvement period corresponding to the detailed side effect response selected in step S1403 for the side effect case extracted in step S1404.
  • FIG. 16 is a flowchart of processing in which the examination information interpretation unit 109 calculates the improvement period.
  • the examination information interpretation unit 109 reads the side effect case table 900 created in step S1404 into the memory 103 (S1601).
  • the examination information interpretation unit 109 acquires records of all patient IDs in the side effect case table 900, and determines whether the processing is completed for all patient IDs (S1602). As a result, if the processing has not been completed for some patient IDs, the processing of steps S1603 to S1607 is repeatedly executed for each patient ID.
  • step S1603 the examination information interpretation unit 109 repeatedly executes the processes of steps S1604 to S1607 for each number of side effects of the patient ID to be calculated. If processing has been completed for all the side effects, the process returns to step S1602 to process the next patient ID.
  • step S1604 the test information interpretation unit 109 acquires from the test information table 400 all the test values from the start date to the end date of the side effect period of the patient ID, the side effect details, and the number of side effects as the target of the improvement period.
  • the inspection information interpretation unit 109 extracts the inspection value increase / decrease period (S1605).
  • the definition of the improvement period is set to be a period in which the inspection value is monotonously decreasing / increasing
  • the period in which the inspection value is monotonously decreasing or monotonously increasing is calculated.
  • the examination information interpretation unit 109 calculates an improvement period (S1606).
  • the calculation target section of the improvement period is set to be the last monotonic decrease / increase period during the side effect period
  • the start date of the last monotone decrease period or the monotone increase period during the side effect period extracted in step S1605 Is the improvement period start date and the end date is the improvement period end date.
  • test value indicates one peak
  • a monotonically increasing period 1 and a monotonically decreasing period 2 Is calculated.
  • the period 2 in which the test value monotonously decreases is the last period
  • the start date and the end date of the period 2 are the improvement period start date and the improvement period end date, respectively.
  • the period 11 is monotonically increasing
  • the period 12 is monotonically decreasing
  • the period 13 is monotonically increasing thereafter
  • the monotonic A decreasing period 14 a further monotonically increasing period 15, and a monotonically decreasing period 16 are calculated.
  • the period 16 in which the test value monotonously decreases is the last period
  • the start date and the end date of the period 16 are the improvement period start date and the improvement period end date, respectively.
  • the examination information interpretation unit 109 registers the improvement period start date and the improvement period end in the fields 906 and 907 of the side effect case table 900 (S1607).
  • the improvement period can be efficiently calculated by extracting the inspection value increase / decrease period using the inspection value and the definition of the improvement period set in advance.
  • step S1602 If it is determined in step S1602 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1406.
  • step S1406 the control unit 101 activates the side effect cause information calculation unit 111 and calculates side effect cause drug information 1006 of each prescription drug for the side effect case extracted in step S1404.
  • FIG. 17 is a flowchart of processing in which the side effect cause information calculation unit 111 calculates the cause of the side effect.
  • the side effect cause information calculation unit 111 reads the side effect case table 900 created in steps S1404 and S1405 to the memory 103 (S1701).
  • step S1702 the side effect cause information calculation unit 111 acquires records of all patient IDs in the side effect case table 900, and determines whether the processing is completed for all patient IDs (S1702). As a result, if the processing has not been completed for some patient IDs, the processing of steps S1703 to S1710 is repeatedly executed for each patient ID.
  • step S1703 the side effect cause information calculation unit 111 repeatedly executes the processing of steps S1704 to S1710 for each side effect count for all side effect details in the side effect case table 900. If processing has been completed for all side effects, the process returns to step S1702 to process the next patient ID.
  • the side effect cause information calculation unit 111 acquires the extraction target days indicating the target period for extracting the prescription drug for which the side effect cause drug information is calculated. Specifically, the number of extraction target days corresponding to the side effect details to be analyzed is acquired from the field 602 of the setting information table 600. For example, when calculating the side effect-causing drug information for the side effect details “increase in ALT (GPT)”, “30” is acquired as the extraction target days. That is, 30 days before the side effect start date are acquired as the drug extraction period.
  • the side effect cause information calculating unit 111 extracts all drugs prescribed during the extraction target days before the side effect start date, and registers them in the side effect drug table 1000 (S1705). Specifically, all the drugs prescribed for the number of extraction target days are extracted from the prescription information table 300 among the drugs prescribed for cases for which side effect cause drug information is calculated. For example, in the case where the patient ID is “## 1”, when the side effect details of the analysis target is “ALT (GPT) increase”, the number of extraction target days (before the side effect start date April 7, 2014) 30 days), the drugs A and B are extracted and registered in the side effect drug table 1000.
  • the side effect cause information calculation unit 111 excludes medicines that are continuously prescribed from the medicine extracted in step S1705 even after the start of the improvement period (S1706). For example, in the case where the patient ID is “## 1”, when the side effect detail of the analysis target is “increase in ALT (GPT)”, among the drugs (drug A, drug B) extracted in step S1705, Since drug B is continuously prescribed after the start of the improvement period, drug B is excluded.
  • step S1403 it is determined whether or not “use drug information (side effect)” 21032 is selected in the analysis option selection field 2103 (S1707). As a result, if “use drug information (side effect)” 21032 is not selected, the process advances to step S1709. On the other hand, if “use drug information (side effect)” 21032 is selected, the side effect cause information calculation unit 111 excludes the drug based on the side effect known information (S1708). Specifically, only the drugs whose details of side effects to be analyzed are stored in the field 803 of the drug information table 800 are set as the calculation targets of the side effect cause drug information, and the drugs not stored in the field 803 are excluded from the calculation targets.
  • the side effect cause information calculation unit 111 registers “1” in the side effect cause drug information 1006 of the side effect drug table 1000 for the drugs extracted in step S1705 and not excluded in step S1708 (S1709).
  • the side effect cause information calculation unit 111 uses Formula (1) to calculate the difference (DDSA) between the prescription start date and the side effect start date of the drug that was not excluded in Step S1708 among the drugs extracted in Step S1705.
  • the difference (DDSE) between the prescription end date and the side effect start date is calculated using Equation (2) (S1710).
  • the side effect cause information calculation unit 111 registers the DDSA and DDSE calculated in step S1710 in the fields 1007 and 1008 of the side effect drug table 1000, respectively (S1711).
  • step S1702 If it is determined in step S1702 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1407.
  • step S1407 the control unit 101 activates the medicinal effect information calculating unit 112 and calculates medicinal effect information of each prescription drug.
  • FIG. 18 is a flowchart of a process in which the medicinal effect information calculation unit 112 calculates medicinal effect information of each prescription drug.
  • the medicinal effect information calculation unit 112 determines whether or not the processing has been completed for all medicinal effects selected in the effect selection column 2102 to be analyzed in step S1403 (S1801). As a result, if the process has not been completed for some of the medicinal effects, the processes of steps S1802 to S1809 are repeatedly executed for each medicinal effect.
  • step S1802 the medicinal effect information calculation unit 112 acquires all the patient IDs in the case information table 200, and determines whether the processing is completed for all the patient IDs (S1802). As a result, if the process has not been completed for some patient IDs, the processes in steps S1803 to S1809 are repeatedly executed for each patient ID. If processing has been completed for all patient IDs, the process returns to step S1801 to process the next medicinal effect.
  • step S1803 the medicinal effect information calculation unit 112 extracts the increase / decrease period of the test value of the test item corresponding to the medicinal effect selected in step S1403 (S1803).
  • the definition of the improvement period is set as a period in which the inspection value monotonously decreases / increases
  • the period in which the inspection value monotonously decreases or monotonously increases is calculated.
  • a table in which the inspection items corresponding to the medicinal effect are stored in advance is used (not shown).
  • the correspondence between the side effect details 502 and the test item 503 in the side effect knowledge information table 500 may be used.
  • the medicinal effect information calculation unit 112 calculates an improvement period (S1804).
  • the start date and end date of the last monotonous decrease / increase period of the test value increase / decrease period extracted in step S1803 are set as the improvement period start date and the improvement period end date, respectively.
  • the medicinal effect information calculation unit 112 acquires information on the medicine prescribed before the end date of the improvement period from the prescription information table 300 (S1805).
  • the drug may be acquired according to a preset extraction definition, such as acquiring only the drug prescribed within 60 days before the end date of the improvement period. For example, in the case where the patient ID is “## 1”, when the medicinal effect improvement period end date to be analyzed is April 18, 2014, the medicine A, the medicine B, and the medicine C are acquired.
  • step S1403 it is determined whether or not “use drug information (effect)” 21033 is selected in the analysis option selection field 2103 (S1806). As a result, if “use drug information (effect)” 21033 is not selected, the process advances to step S1709. On the other hand, if “use drug information (side effects)” 21032 is selected, the drug effect information calculation unit 112 excludes drugs based on drug effect information (S1807). Specifically, only drugs whose medicinal effects to be analyzed are stored in the field 802 of the drug information table 800 are calculated, and drugs that are not stored in the field 802 are excluded.
  • the drugs acquired in step S1805 are drug A, drug B, and drug C, drug B is excluded, and drug A and drug C are included in the drug efficacy information. It is a calculation target.
  • the medicinal effect information calculation unit 112 calculates the total number of prescription days for the drugs not acquired in step S1807 among the drugs acquired in step S1805 (S1808). For example, in the case where the patient ID is “## 1”, if it is determined in step S1807 that the drug A and the drug C are the targets for calculating the drug efficacy information, the total prescription days for the drug A are 14 days, The prescription days are calculated to be 4 days.
  • the medicinal effect information calculation unit 112 calculates the improvement degree of the drug for which the total number of prescription days has been calculated in step S1808 (S1809). Specifically, after the prescription of the medicine whose total prescription days have been calculated in step S1808, the amount of change in the test value due to the prescription for the target medicinal effect is calculated as the improvement degree. For example, in the case where the patient ID is “## 1”, the drugs whose total prescription days are calculated in step S1808 are the drug A and the drug C, and after the drug A is prescribed, the test value corresponding to the target drug effect ( When Cre) is improved by 1.9, the improvement degree of drug A is 1.9. In addition, when the test value (Cre) corresponding to the target drug effect is improved by 0.4 after the prescription of the drug C, the improvement degree of the drug C is 0.4.
  • the medicinal effect information calculation unit 112 registers the calculated degree of improvement in the medicinal effect evaluation 1304 of the medicinal effect information table 1100 in association with the drug name (S1810).
  • the drug efficacy can be efficiently analyzed. Furthermore, by narrowing down the drugs whose drug efficacy is to be analyzed using the drug information, it is possible to exclude drugs that are not related to the drug efficacy, and the drug efficacy can be analyzed efficiently.
  • control unit 101 activates the display screen generation unit 105.
  • the display screen generation unit 105 analyzes the results of all the analysis processes based on the side effect case calculated in step S1404, the side effect cause drug information calculated in step S1406, and the effect information calculated in step S1407.
  • a screen to be output to the result list display area 2107 is generated (S1408), and the generated screen data is output (S1409).
  • FIG. 21 is an example of a screen that is displayed after the analysis result output to the analysis result list display area 2107 is narrowed down to information on side effects using the analysis result display item selection field 2106.
  • the analysis screen 2100 shown in FIG. In the analysis result list display area 2107, a patient ID of a side effect case and a drug with “1” registered in the side effect cause drug information in each case are displayed as a side effect cause drug candidate.
  • the display screen generation unit 105 displays a screen for displaying the case analysis screen 2500 on the input / output terminal 130. Output data.
  • FIG. 23 is a diagram illustrating an example of a case analysis screen 2500 displayed on the input / output terminal 130 when the user refers to a detailed analysis result for each case in the first embodiment.
  • the case analysis screen 2500 includes a side effect selection field 2501, a side effect detail selection field 2502, a patient ID selection field 2504, a “return to patient list” button 2505, a test result display area 2506, a prescription drug display area 2507, And a side effect cause drug candidate display area 2508.
  • the user operates a drop-down button in the side-effect selection column 2501 to select a side-effect to be analyzed and visualized from a plurality of side-effects displayed in a drop-down list (not shown).
  • the user may be able to directly input a side effect in the side effect selection field 2501.
  • the user operates the drop-down button in the side-effect detail selection column 2502, and selects side-effect details to be analyzed and visualized from a plurality of side-effect details displayed in a drop-down list (not shown).
  • the user may be able to directly input the side effect details in the side effect detail selection field 2502.
  • the user operates a drop-down button in the patient ID selection field 2504 to select a patient ID to be analyzed and visualized from a plurality of patient IDs displayed in a drop-down list (not shown).
  • the user may be able to directly enter the patient ID in the patient ID selection field 2504.
  • the user When ending the detailed analysis for each case, the user operates the “return to patient list” button 2505 to end the case analysis screen 2500 and display the analysis screen 2100.
  • a graph 25062 showing the transition of the test value of the test item corresponding to the side effect details selected in the side effect detail selection column 2502, and a line indicating a threshold value for determining whether the test value is abnormal 25063 and a graph 25061 showing the transition of the test value of the test item corresponding to the medicinal effect selected in step S1403 are displayed.
  • the prescription drug display area 2507 displays time series information of the prescription start date and prescription end date of the drug prescribed for the case selected in the patient ID selection field 2504.
  • the transition of the test value corresponding to the side effect details, the transition of the test value of the test item corresponding to the medicinal effect, the time series information of the prescription start date and prescription end date of the drug, and the side effect cause drug candidate are 1
  • the side effect cause drug candidate is 1
  • the user selects a statistical analysis option in the statistical analysis selection option field 2109 of the analysis screen 2100 when the user performs a statistical analysis of side effects and effects.
  • the statistical analysis selection option column 2109 includes a side effect analysis tabulation method selection column 21091, a side effect analysis statistical method selection column 21092, and a medicinal effect analysis statistical method selection column 21093.
  • the user operates the drop-down button in the side-effect analysis tabulation method selection field 21091 to select a tabulation method to be used for statistical analysis from a plurality of side-effect analysis tabulation methods displayed in a drop-down list (not shown).
  • the user may be able to directly input the side effect analysis tabulation method in the side effect analysis tabulation method selection field 21091.
  • the user operates a drop-down button in the side-effect analysis statistical method selection column 21092 to select a statistical method to be used for statistical analysis from a plurality of side-effect analysis statistical methods displayed in a drop-down list (not shown).
  • the user may be able to directly input the side effect analysis statistical method in the side effect analysis statistical method selection field 21092.
  • the user operates the drop-down button of the statistical analysis method selection column 21093 for drug efficacy analysis, and selects a statistical technique to be used for statistical analysis from a plurality of statistical techniques for drug efficacy analysis displayed in a drop-down list (not shown).
  • the user may be able to directly input the statistical method of the medicinal effect analysis into the statistical method selection column 21093 of the medicinal effect analysis.
  • control unit 101 activates the statistical analysis unit 114 and executes a side effect risk calculation process (see FIG. 19) and a medicinal effect evaluation calculation process (see FIG. 20).
  • FIG. 19 is a flowchart of processing in which the statistical analysis unit 114 calculates the side effect risk.
  • the statistical analysis unit 114 reads the side effect medicine table 1000 and the prescription information table 300 and stores them in the memory 103 (S1901).
  • the statistical analysis unit 114 counts the total number of cases used for each drug included in the read prescription information table 300 (S1902).
  • the statistical analysis unit 114 acquires all the side effect details in the side effect drug table 1000 and determines whether the processing is completed for all the side effect details (S1903). As a result, if processing for some of the side effect details has not been completed, the processing of steps S1904 to S1907 is executed for each side effect detail.
  • step S1904 the statistical analysis unit 114 counts the number of drugs used in the side effect case by the counting method selected in the statistical method selection column 21092 for side effect analysis. For example, when “only target drugs prescribed immediately before side effects” is selected in the side effect analysis tabulation method selection field 21091, in each case, the drug with side effect cause drug information “1” immediately before the side effect
  • the prescribed number of drugs that is, the drug with the smallest DDSE value
  • the number of drugs used in the case of side effects is counted.
  • a value obtained by dividing the number of use cases in each drug by the number of use cases of each drug may be calculated as the side effect rate.
  • the statistical analysis unit 114 calculates the risk prescription days (S1905).
  • the average value of DDSA of the drugs targeted for aggregation in each case is calculated as the risk prescription day.
  • a value representative of DDSA of the drug may be calculated by other methods such as using the median value of DDSA.
  • the statistical analysis unit 114 calculates the side effect risk by the statistical method selected in the statistical method selection column 21092 for side effect analysis (S1906). For example, when “logistic regression” is selected in the statistical method selection column 21092 for side effect analysis, the total number of use cases of each drug counted in step S1902, the number of use cases in the side effect cases counted in step S1904, and each case The odds ratio calculated by logistic regression using the side effect-causing drug information is used as the side effect risk.
  • the side effect risk may be calculated by other statistical methods.
  • the statistical analysis unit 114 registers the drug name, the total number of use cases, the use number 1204 in the side effect case, the side effect risk 1205, and the risk prescription day 1206 in the side effect risk table 1200 (S1907).
  • step S1903 If it is determined in step S1903 that the processing for all the side effect details has been completed, the control unit 101 activates the display screen generation unit 105.
  • the display screen generation unit 105 calculates the total number of use cases of each drug calculated in step S1902, the number of use cases in the side effect case calculated in step S1903, the risk prescription days calculated in step S1905, and the calculation in step S1906.
  • a statistical analysis result reference screen 2600 that displays all analysis processing results is generated using the side effect risk that has been generated (S1908), and the generated screen data is output (S1909).
  • FIG. 20 is a flowchart of a process in which the statistical analysis unit 114 calculates the efficacy evaluation.
  • the statistical analysis unit 114 reads the medicinal effect information table 1100 and the prescription information table 300 and stores them in the memory 103 (S2001).
  • the statistical analysis unit 114 counts the number of all use cases of each drug from the prescription information table 300 (S2002). Note that when the number of use cases of each drug has already been counted in step S1902, the total number of use cases of each drug calculated in step S1902 may be used.
  • the statistical analysis unit 114 acquires all the effects of the medicinal effect information table 1100, and determines whether the processing has been completed for all the effects (S2003). As a result, if the processing has not been completed for some effects, the processing from step S2004 to S2006 is executed for each effect.
  • the statistical analysis unit 114 totals the total prescription days and the improvement degree of the drug related to the effect (S2004).
  • the statistical analysis unit 114 calculates a medicinal effect evaluation by the statistical method selected in the statistical method selection column 21093 of the medicinal effect analysis (S2005). For example, when “Logistic regression” is selected in the statistical analysis method selection column 21093 for the medicinal effect analysis, the total number of use cases of each drug calculated in step S2002, the total prescription days and the improvement degree of each drug calculated in step S2004 The odds ratio calculated by logistic regression is used as the drug efficacy evaluation.
  • a value calculated by another method may be used, such as a value obtained by dividing the sum total of the degree of improvement totaled in step S2005 by the total prescription days totaled in step S2005.
  • the statistical analysis unit 114 registers the drug name, the number of all used cases, and the drug efficacy evaluation of each drug in the drug efficacy evaluation table 1300 (S2006).
  • step S2003 If it is determined in step S2003 that the processing has been completed for all the effects, the control unit 101 activates the display screen generation unit 105.
  • the display screen generation unit 105 uses the total number of use cases of each drug calculated in step S2002 and the medicinal effect evaluation calculated in step S2005 to display a statistical analysis result reference screen 2600 that displays all analysis processing results.
  • a screen to be output is generated (S2007), and the generated screen data is output (S2008).
  • the display screen generation unit 105 calculates the total number of use cases of each drug calculated in step S1902 and the calculation in step S1903. Statistical analysis of all analysis results using the number of side effect cases used, risk prescription days calculated in step S1905, side effect risk calculated in step S1906, and medicinal efficacy evaluation calculated in step S2005 A screen to be output to the result reference screen 2600 is generated.
  • FIG. 24 is a diagram showing an example of a statistical analysis result reference screen 2600 displayed by the input / output terminal 130 when the user refers to the statistical analysis result in the first embodiment.
  • Screen example 2600 includes a side effect selection field 2601, a side effect detail selection field 2602, a medicinal effect selection field 2603, an alignment criterion selection area 2604, and a statistical analysis result display area 2606.
  • the user operates the drop-down button in the side-effect selection field 2601 to select a side effect to be displayed in the statistical analysis result display area 2606 from a plurality of side effects displayed in a drop-down list (not shown).
  • the user may be able to directly input a side effect in the side effect selection field 2601.
  • the user operates the drop-down button in the side-effect detail selection field 2602 to select the side-effect details to be displayed in the statistical analysis result display area 2606 from a plurality of side-effect details displayed in a drop-down list (not shown).
  • the user may be able to directly input the side effect details in the side effect detail selection field 2602.
  • the user operates the drop-down button in the medicinal effect selection field 2603 to select the medicinal effect to be displayed in the statistical analysis result display area 2606 from the plural medicinal effects displayed in the drop-down list (not shown).
  • the user may be able to directly enter the medicinal effect in the medicinal effect selection field 2603.
  • the user operates the drop-down button in the item selection field 26042 in the alignment reference selection area 2604, and selects an alignment reference from a plurality of items displayed in a drop-down list (not shown).
  • the user may be able to directly input an item as an alignment reference in the item selection field 26042.
  • the user selects a radio button in the permutation setting area 26043 attached to the alignment reference selection area 2604, and selects whether the permutation of the alignment is ascending or descending.
  • the alignment control unit 115 When the user operates the “alignment setting” button 26041 in the alignment reference selection area 2604, the alignment control unit 115 is activated, and the item selected in the item selection field 26042 is statistically analyzed according to the permutation selected in the permutation setting area 26043. The statistical analysis results displayed in the result display area 2606 are rearranged.
  • the side effects selected in the side effect selection field 2601 the side effect details selected in the side effect detail selection field 2602, the total number of drugs used corresponding to the drug effect selected in the drug effect selection field 2603, The number of uses, the rate of side effects, the risk of side effects, the number of days for prescribing a risk, and the evaluation of drug efficacy are displayed.
  • the side effect risk, risk prescription days, and drug efficacy evaluation of drugs in various medications are visualized and displayed on a single screen.
  • the side effect analysis support system calculates the side effect improvement period from the fluctuation of the test value, and among the prescribed drugs, drugs other than those prescribed continuously after the start of the improvement period are related to the side effects.
  • Side effect cause drug information 1006 is calculated, and the data of the screen 2100 for displaying the drug information related to the side effect is output, so that the prescribed drug is separated before and after the start date of the side effect improvement period. The drug that caused the side effect can be estimated.
  • the side effect analysis support system calculates a period of improvement of side effects by excluding patients with diseases in which the same symptoms as the side effects to be analyzed occur, it is possible to appropriately select side effect cases.
  • the side effect analysis support system acquires the prescription start date of the drug from the prescription information table 300, calculates the side effect start date and the side effect end date from the variation of the test result recorded in the test information table 400, and starts the prescription of the drug
  • DDSA side effect start date
  • the side effect end date from the variation of the test result recorded in the test information table 400
  • the side effect analysis support system acquires the prescription start date of the drug from the prescription information table 300, calculates the side effect start date and the side effect end date from the variation of the test result recorded in the test information table 400, and starts the prescription of the drug
  • DDSA start date of side effect
  • the side effect analysis support system calculates side effect causal agent information and improvement period for each number of side effect periods when multiple side effect periods specified by the side effect start date and side effect end date are found. Even when it is repeated, it is possible to accurately estimate the drug that caused the side effect.
  • the side effect analysis support system extracts the period during which the test value monotonously decreases and the period during which the test value monotonously increases during the side effect period, and improves the last period in one side effect period among the extracted periods. Therefore, even when the test value repeatedly increases and decreases, the improvement period can be accurately calculated.
  • the side effect analysis support system excludes drugs that cause known side effects and selects drugs that are related to side effects, it is possible to exclude drugs that are not related to side effects from drug candidates that cause side effects.
  • the causative drug can be estimated accurately.
  • a drug related to a side effect can be selected for only a drug that causes a known side effect, and the drug that caused the side effect can be estimated from the known drug that causes the side effect.
  • the side effect analysis support system selects the drug related to the side effect from the drugs prescribed in the extraction target period before the start date of the side effect, it is possible to accurately estimate the drug that caused the side effect. it can.
  • the period from the prescription of a drug to the occurrence of a side effect varies depending on the side effect, it is possible to analyze by analyzing the side effect that occurs early from the prescription and the side effect that occurs late.
  • the side effect analysis support system calculates the side effect risk of the drug by statistically processing the side effect cause drug information and the number of drug use cases of multiple patients. Risk can be calculated and the risk of side effects can be estimated accurately.
  • the side effect analysis support system calculates the number of days (DDSE) from the end date of drug prescription to the start date of side effect, and calculates the side effect risk of the drug with the smallest DDSE. The appropriate drug can be selected accurately.
  • DDSE number of days
  • the side effect analysis support system sets DDSE to 0 when DDSE is negative, it is possible to extract all drugs related to side effects.
  • the side effect analysis support system outputs a case analysis screen 2500 for displaying the side effect improvement period and the drug prescription period in the same time series, so the drug prescription period, the change in the test value, and the side effect improvement period And can be visualized. That is, in the example of the analysis screen shown in FIG. 23, it can be determined that the drugs B and C are not related to side effects.
  • the side effect analysis support system calculates the improvement period of the test value from the fluctuation of the test value, and among the prescribed drugs, the drug prescribed before the improvement period is related to the improvement of the side effect. Since the medicinal effect information indicating the medicinal effect is calculated, it is possible to identify the drug that causes medicinal effect. Moreover, the improvement degree by a chemical
  • medical agent can be quantitatively evaluated by quantifying a medicinal effect. In addition, the risk of side effects and the evaluation of drug efficacy can be easily displayed on one screen.
  • the side effect analysis support system calculates the evaluation value indicating the degree of efficacy of the drug by statistically processing the drug efficacy information and the number of use cases of the drug.
  • the medicinal effect can be calculated and the medicinal effect can be accurately estimated.
  • the side effect analysis support system of the second embodiment is a computer system that analyzes the side effects and effects of each drug for each case, visualizes and outputs the analysis results, and includes a hospital information system 120 and The network 140 and the input / output terminal 130 are configured.
  • the same components and functions as those in the first embodiment will be denoted by the same reference numerals, and description thereof will be omitted.
  • the knowledge database 117 includes a post-side effect analysis target days setting table 2700.
  • FIG. 25 is a diagram showing a configuration example of the post-side effect analysis target days setting table 2700.
  • the post-side effect analysis target days setting table 2700 is a target side effect analysis for analyzing whether “1” is registered in the side-effect-causing drug information even during another side-effect period for a drug registered with “1” in the side-effect-causing drug information. It includes a field 2702 for details 2701 and the number of days 2702 for analysis after a side effect corresponding to the side effect detail. For example, the record 2700A indicates that the number of days after the side effect analysis of the side effect details of “increased ALT (GPT)” is 365 days.
  • FIG. 26 is a flowchart of processing in which the side effect cause information calculation unit 111 of the second embodiment updates side effect cause information.
  • the side effect cause information update process shown in FIG. 26 may be automatically executed after the side effect cause information calculation unit 111 calculates the cause of the side effect (FIG. 17), or the process is executed according to a user instruction. Also good.
  • the side effect cause information calculation unit 111 includes the prescription information table 300 stored in the integrated database 116, the side effect case table 900 created in steps 1404 and 1405, and the side effect drug table 1000 created in step 1406. Reading to the memory 103 (S2801).
  • the side effect cause information calculation unit 111 reads the post-side effect analysis target days setting table 2700 stored in the knowledge database 117 into the memory 103 (S2802).
  • the side effect cause information calculation unit 111 acquires records of all patient IDs in the side effect medicine table 1000, and determines whether the processing has been completed for all patient IDs (S2803). As a result, if the processing is not completed for some patient IDs, the processing from step S2804 to S2806 is repeatedly executed for each patient ID.
  • the side effect cause information calculation unit 111 acquires all side effect detail records in the side effect drug table 1000, and repeatedly executes the processing from step S2805 to S2806 for each side effect detail (S2804). If all the side effect details have been processed, the process returns to step S2803 to process the next patient ID.
  • step S2805 the side effect cause information calculation unit 111 acquires the number of days after analysis of side effects of the side effect details selected in step S2804 from the analysis target days setting table after post side effect 2700.
  • the side effect cause information calculation unit 111 repeatedly executes the processes of steps S2807 to S2809 in ascending order of the number of side effects for the side effect number corresponding to the patient ID selected in step S2803 and the side effect details selected in step S2804. (S2806).
  • step 2807 the side effect cause information calculation unit 111 acquires a side effect end date corresponding to the number of side effects selected in step S2806, and from the acquired side effect end date, the side effect analysis target days acquired in step 2805 is calculated.
  • the period until elapses is set as the analysis target section after the side effect. For example, when the side effect end date is April 25, 2014 and the side effect analysis target number is 365 days, 365 days from April 26, 2014 to April 25, 2015 are set as the post-side effect analysis target section. To do. Then, it is determined whether the side effect start date of the number of side effects other than that selected in step 2806 is included in the set analysis target section.
  • the process returns to step S2806.
  • the side effect cause information calculating unit 111 sets 1 in the side effect cause drug information by the side effect count in the post-side effect analysis target section. The number of registered times is counted for each medicine (S2808).
  • the side effect cause information calculation unit 111 updates the side effect cause drug information of each drug based on the counting result and the post-side effect exclusion condition set on the setting screen (FIG. 22) (step 2809).
  • the exclusion condition after side effect is “when there is a prescription at least once in the post-side effect analysis target section and the number of times 1 is registered in the side effect cause drug information in the side effect period in the post-side effect analysis target section is zero. ”Is set.
  • the side effect cause information calculation unit 111 has one or more prescriptions in the post-side effect analysis target section among the drugs registered with “1” in the side effect cause drug information in the number of side effects selected in step 2806, and The side effect cause drug information is updated from “1” to “0” for the drug for which “1” is not registered in the side effect cause drug information in the side effect period in the post-side effect analysis target section.
  • the post-adverse reaction exclusion condition is “the number of prescriptions that have been prescribed once or more in the post-side-effect analysis target section and 1 is registered in the side-effect cause drug information in the side-effect period in the post-side-effect analysis target section. It may be “when more than half of the number of side effects in the target section”.
  • the post-side effect analysis setting change area 3001 of the setting screen includes a side effect detail selection column 30011, a post-side effect exclusion condition selection column 30012, and a post-side effect analysis days definition column 30013.
  • the user operates the drop button 30011 in the post-side effect analysis setting change area 3001 to select side effect details to be analyzed after side effects from a plurality of side effect details displayed in a drop-down list (not shown). Further, the user operates the drop button 30012 to select a post-side effect exclusion condition to be excluded from the analysis from a plurality of exclusion conditions displayed in a drop-down list (not shown). In addition, the user operates the up / down button 30013 to change the number of days to be analyzed after a side effect. Alternatively, the user can directly input the number of days after the side effect analysis target of the side effect details to be set directly into the post-side effect analysis setting change area 3001.
  • the user operates the “Save” button 2302 when saving the changed setting value, and operates the “Cancel” button 2303 when not saving. If any one of the buttons is operated, the setting screen 2300 is terminated and the analysis screen 2100 is returned to.
  • FIG. 27 is a diagram illustrating an example of a case analysis screen 3100 displayed by the input / output terminal 130 when the user refers to a detailed analysis result for each case in the second embodiment.
  • the case analysis screen 3100 includes a side effect selection field 3101, a side effect detail selection field 3102, a patient ID selection field 3104, a “return to patient list” button 3105, a test result display area 3106, a prescription drug display area 3107, A side effect causal drug candidate display area 3108 and a post side effect analysis target section selection field 3109 for selecting the number of side effects in the post side effect analysis section are included.
  • the user operates a drop-down button in the side-effect selection column 3101 to select a side-effect to be analyzed and visualized from a plurality of side-effects displayed in a drop-down list (not shown).
  • the user may be able to directly input a side effect in the side effect selection field 3101.
  • the user operates the drop-down button in the side-effect detail selection column 3102 to select side-effect details to be analyzed and visualized from a plurality of side-effect details displayed in a drop-down list (not shown).
  • the user may be able to directly input the side effect details in the side effect detail selection field 3102.
  • the user operates a drop-down button in the patient ID selection field 3104 to select a patient ID to be analyzed and visualized from a plurality of patient IDs displayed in a drop-down list (not shown).
  • the user may be able to input the patient ID directly in the patient ID selection field 3104.
  • the user When ending the detailed analysis for each case, the user operates the “return to patient list” button 3105 to end the case analysis screen 3100 and display the analysis screen 2100.
  • the test result display area 3106 includes a graph 31062 indicating the transition of the test value of the test item corresponding to the side effect details selected in the side effect detail selection field 3102 and a line indicating a threshold value for determining whether the test value is abnormal.
  • 31063 a graph example 31061 indicating the transition of the test value of the test item corresponding to the medicinal effect selected in step S1403, and a line indicating the analysis interval after the side effect of the number of side effects selected in the post-side effect analysis target interval selection field 3109 31064 is displayed.
  • the prescription drug display area 3107 time series information of the prescription start date and prescription end date of the drug prescribed for the case selected in the patient ID selection field 3104 is displayed.
  • the side effect cause drug candidate display area 3108 among the drugs displayed in the prescription drug display area 3107, the drug in which “1” is stored in the side effect cause drug information corresponding to the side effect details in the side effect detail selection field 3102 Is displayed.
  • the patient whose patient ID is “## 4” has the side effect “liver dysfunction” and the side effect detail “ALT (GPT) rise” twice, and the duration of the first side effect “Side effect period 1”, the second side effect period is displayed as “side effect period 2”, and the improvement period of each side effect is shown.
  • medicines A, B, C, D, and E are respectively prescribed to a patient whose patient ID is “## 4”.
  • the post-side effect exclusion condition is that “there is one or more prescriptions in the post-side effect analysis target section, Consider a case where “when the number of times 1 is registered is 0” is set.
  • the side effect period 2 is included in the post-side effect analysis target section of the side effect period 1, and the drug D is prescribed only before the start date of the side effect in the side effect period 2, so the side effect cause of the drug D in the side effect period 2 “1” is recorded in the medicine information. Therefore, since the drug D does not meet the post-side effect exclusion condition, “1” is recorded in the side effect cause drug information of the drug D in the side effect period 1.
  • Drug E is prescribed before the side effect start date of side effect period 1 and after the end date of improvement period of side effect period 1.
  • the post-adverse effect exclusion condition is “there is one or more prescriptions in the post-side effect analysis target section,
  • the side effect period 2 is included in the post-side effect analysis target section of the side effect period 1, but since the drug E is prescribed after the start date of the side effect improvement period of the side effect period 2, the drug list in the side effect period 2 Drug E is not included in the inside. Therefore, the drug E corresponds to the post-side effect exclusion condition, and “0” is recorded in the side effect cause drug information of the drug E in the side effect period 1.
  • the side effect analysis support system of the second embodiment uses the side effect period of the second side effect that occurred after the end of the first side effect, and the prescription period of the drug prescribed after the end of the first side effect, Among the prescribed drugs, drugs other than those prescribed continuously after the second side effect improvement period are selected as drugs related to the second side effect, so the reproducibility of the side effects can be analyzed, Side-effect-causing drug information can be calculated with high accuracy.
  • the side effect analysis support system of the third embodiment is a computer system that analyzes side effects, effects, medical costs for each drug and side effects, predicted medical costs for each drug, and visualizes and outputs the results.
  • the hospital information system 120, the network 140, and the input / output terminal 130 are included.
  • the third embodiment only the parts different from the first or second embodiment described above will be described, and the same configurations and functions as those in the first or second embodiment will be denoted by the same reference numerals, and the description thereof will be omitted. To do.
  • the data server 100 of the third embodiment has a medical cost calculation unit 151 in addition to the configuration of the first embodiment.
  • the medical cost calculation unit 151 is a processing unit that executes processing for realizing the function of the data server 100, and may be realized by dedicated hardware or software. When each processing unit is realized by software, in the following description, the processing executed by the medical cost calculation unit 151 is actually executed by the control unit 101 in accordance with an instruction described in a program stored in the memory 103. Details of processing executed by the medical cost calculation unit 151 will be described later.
  • the hospital information system 120 of the third embodiment includes a case database 121, an examination information database 122, a prescription information database 123, and a medical practice information database 124. These databases may be stored in another storage device (not shown) in the hospital information system 120 such as an HDD (Hard Disk Drive).
  • the medical practice information database 124 includes a medical practice cost information table 3300 (see FIG. 28) and a treatment information table 3500 (see FIG. 29). The configuration of these tables will be described later with reference to each drawing. Further, as described above, the medical practice information database 124 may include a table that records the relationship between drug names and corresponding diseases.
  • the integrated database 116 of the third embodiment includes a case information table 200 (FIG. 2) for managing basic information for each case, a prescription information table 300 (FIG. 3) for recording prescription information for each case,
  • the examination information table 400 (FIG. 4) for storing examination information, a medical practice cost information table 3300 (FIG. 28), and a treatment information table 3500 (FIG. 29).
  • the integrated database 116 may also include a drug-compatible disease information table that records the relationship between drug names and corresponding diseases.
  • FIG. 28 is a diagram showing a configuration example of the medical practice cost information table 3300.
  • the medical practice cost information table 3300 is a table for storing a cost record of a medical practice acquired from the medical practice information database 124, and includes fields of a medical practice 3301, a type 3302, and a cost 3303.
  • the medical practice 3301 is a name of the medical practice, and a record of the medical practice cost information table 3300 is recorded for each medical practice.
  • the type 3302 is a type of the medical practice.
  • the cost 3303 is the medical practice cost.
  • the record 3300A of the medical practice cost information table 3300 shown in FIG. 28 indicates that the medicine A is prescribed as the medical practice and the cost of the medicine A is 1000.
  • FIG. 29 is a diagram showing a configuration example of the treatment information table 3500.
  • the treatment information table 3500 is a table for storing treatment records acquired from the medical practice information database 124, and includes fields for a patient ID 3501, a treatment name 3502, and an implementation date 3503.
  • the patient ID 3501 is identification information for uniquely identifying a patient.
  • the treatment name 3502 is the name of the treatment performed on the patient.
  • An implementation date 3503 is a date when treatment is performed on the patient.
  • the record 3500A of the treatment information table 3500 illustrated in FIG. 29 indicates that the treatment of the operation Z was performed on April 3, 2014 for the patient whose patient ID is “## 1”.
  • the side effect analysis database 118 of the third embodiment includes a side effect case table 900 (FIG. 9), a side effect drug table 1000 (FIG. 10), a medicinal effect information table 1100 (FIG. 11), and a side effect risk table 1200 (FIG. 12).
  • FIG. 30 is a diagram showing a configuration example of the side effect medical cost table 3700.
  • the side effect medical cost table 3700 includes fields of disease name 3701, side effect details 3702, side effect presence / absence 3703, number of patients 3704, and average medical cost (side effect) 3705.
  • the disease name 3701 is the name of the disease.
  • the side effect details 3702 are detailed information on side effects.
  • the side effect presence / absence 3703 is information indicating the presence or absence of a side effect.
  • the number of patients is the number of patients corresponding to the set of disease name 3701, side effect details 3702, and side effect presence / absence 3703.
  • the average medical cost (side effect) 3705 is an average value for each side effect of the medical cost.
  • the record 3700A of the side-by-side treatment cost table 3700 shown in FIG. 30 shows that among the patients diagnosed with diabetes, there are 120 people who have no side effect of increasing ALT (GPT). This shows that the average medical cost is 14380.
  • FIG. 31 is a diagram showing a configuration example of the medical treatment cost table 3800 for each medicine.
  • the medical treatment cost table 3800 by drug includes fields of disease name 3801, main drug name 3802, side effect details 3803, presence / absence of side effects 3804, number of patients 3805, prediction / average flag 3806, and medical cost (drug) 3807.
  • the disease name 3801 is the name of the disease.
  • the main drug name is a name of a drug mainly prescribed for the disease.
  • the side effect details 3803 is detailed information on side effects.
  • Side effect presence / absence 3804 is information indicating the presence or absence of a side effect.
  • the number of patients 3805 is the number of patients corresponding to the set of disease name 3801, main drug name 3802, side effect details 3803, and side effect presence / absence 3804.
  • the prediction / average flag 3806 is information indicating whether the medical cost is an average value or a prediction value.
  • the medical cost (medicine) 3807 is an average value or a predicted value for each medical cost.
  • the record 3800A of the medical treatment cost table 3800 shown in FIG. 31 shows that among the patients diagnosed with diabetes, the number of patients who are prescribed mainly for the drug A and have no side effect of increasing ALT (GPT). It shows that the average value of the medical costs of 40 people is 12,500. Further, the record 3900A indicates that, among patients diagnosed with diabetes, the predicted value of the medical cost when the medicine A is prescribed is 13841.
  • FIG. 32 is a flowchart of processing in which the control unit 101 according to the third embodiment calculates medical costs.
  • the control unit 101 reads out case data from the integrated database 116 and stores it in the memory 103 (S4001).
  • case data data relating to cases (for example, information for identifying cases, information on prescription drugs corresponding to cases, information on test results corresponding to cases, etc.) will be collectively referred to as case data.
  • the control unit 101 may read all data included in the integrated database 116 and store it in the memory 103.
  • control unit 101 reads side effect data from the side effect analysis database 118 and stores it in the memory 103 (S4002).
  • the control unit 101 may read all data included in the side effect analysis database 118 and store it in the memory 103.
  • control unit 101 reads knowledge data from the knowledge database 117 and stores it in the memory 103 (S4003).
  • the control unit 101 may read all the data included in the knowledge database 117 and store it in the memory 103.
  • control unit 101 activates the medical cost calculation unit 151 (S4004).
  • FIG. 33 is a flowchart of processing in which the medical cost calculation unit 151 calculates an average medical cost.
  • the medical cost calculation unit 151 reads the data of the case information table 200 from the memory 103 (S4101).
  • the medical cost calculation unit 151 acquires records of all patient IDs in the case information table 200, and determines whether processing has been completed for all patient IDs (S4102). As a result, if the processing has not been completed for some patient IDs, the processing from step S4103 to S4108 is repeatedly executed for each patient ID.
  • the medical cost calculation unit 151 acquires all the hospitalization date 205 and the discharge date 206 of the patient ID selected in Step S4102, and both the hospitalization date 205 and the discharge date 206 are “ Select the record that is not recorded as “0” (that is, the hospitalization date, discharge date, and disease name of a patient who has been hospitalized and has been discharged), and the hospitalization period from the hospitalization date to the discharge date Is stored in the memory 103 (S4103). For patients who have been discharged and discharged multiple times, multiple hospital stays are selected.
  • the medical cost calculation unit 151 stores all information on the prescription, examination, and treatment performed in each hospitalization period selected in step S4103, the prescription information table 300, the examination information table 400, and the treatment information table. It is acquired from 3500 (S4104).
  • the medical cost calculation unit 151 acquires all records of the medical service cost information table 3300 stored in the memory 103, and information on prescriptions, examinations, and treatments performed during the hospitalization period acquired in step S4104.
  • the total medical cost during each hospitalization period is calculated using the cost corresponding to the medical practice recorded in the medical practice cost information table 3300, and the patient ID, hospitalization date, discharge date, and disease name are calculated.
  • the calculated total medical cost are stored in a total medical cost table (not shown) provided in the memory 103 (S4105).
  • the total medical cost may be the insurance score used when requesting medical fees in Japan, or may be a cost determined by a healthcare provider.
  • the medical cost calculation unit 151 acquires all records of the side effect case table 900 stored in the memory 103, and details of side effects during the hospitalization period from the hospitalization date and discharge date selected in step S4103. Is determined for each side effect detail (S4106).
  • the medical cost calculation unit 151 acquires all the records of the drug-corresponding disease information table stored in the memory 103, and obtains the disease name of the hospitalization period extracted in step S4103 and the prescription information during the hospitalization period. Used to determine the main drug prescribed mainly during each hospital stay (S4107). If multiple drugs corresponding to the disease name were prescribed during the hospitalization period, the drug whose start date during hospitalization is close to the date of hospitalization may be determined as the main drug, and the number of prescriptions during the hospitalization period The drug with the largest number may be determined as the main drug.
  • the medical cost calculation unit 151 calculates the total medical cost during the hospitalization period calculated in step S4105, the patient ID, the hospitalization date, the main drug name, the side effect details, the presence or absence of side effects, and the side effect details. Are stored in the memory 103 (S4108). Then, the process returns to step S4102, and the next patient ID is processed.
  • the medical cost calculation unit 151 determines whether the total medical costs for each hospitalization period of each patient ID stored in the memory 103 and the presence or absence of side effects. And the side effect details, the total number of patients corresponding to the combination of disease name, presence / absence of side effect and side effect details is calculated, and the average medical treatment cost (side effect) of the set of disease name, side effect presence / absence and side effect details is calculated.
  • the disease name, the presence / absence of side effects, the side effect details, and the average medical cost (side effects) of the number of patients are associated and stored in the side-by-side clinical cost table 3700 (S4109).
  • the average medical treatment cost is the sum of the total medical treatment costs of patients who fall under the combination of disease name, presence / absence of side effects and side effect details, and the number of patients who fall under the combination of disease name, presence / absence of side effects and side effect details. It can be calculated by dividing.
  • the medical cost calculation unit 151 uses the total medical cost of the set of the patient ID and hospitalization period stored in the memory 103, the presence / absence of side effects, and the main drug, and the disease name, main drug, and side effect details, Calculate the average medical cost (drug) by counting the number of patients that fall under the group of the presence or absence of side effects, and correlate the disease name, main drug, side effect details, the presence or absence of side effects, the number of patients, and the average medical cost (drug) And stored in the medical treatment cost table 3800 for each medicine (S4110).
  • the average medical cost is the sum of the total medical costs of patients who fall under the combination of disease name, presence / absence of side effect, detailed side effect and main drug, and falls under the combination of disease name, presence / absence of side effect and disease name It can be calculated by dividing by the number of patients.
  • the medical cost calculation unit 151 uses the disease name, main drug, side effect details, presence / absence of side effects, number of corresponding patients, and average medical cost (drug) to determine the disease name and main drug.
  • a corresponding predicted medical cost is calculated, and the disease name, main drug, and predicted medical cost are associated with each other and stored in the drug-specific medical cost table 3800 (S4111).
  • a predicted medical cost corresponding to a combination of a disease name and a main drug can be calculated by Equation (3).
  • control unit 101 activates the display screen generation unit 105.
  • the display screen generation unit 105 generates a screen for outputting the analysis result based on the medical cost calculated in step S4004 (S4005).
  • FIG. 34 is a diagram showing an example of a statistical analysis result reference screen 4200 displayed by the input / output terminal 130 when the user refers to the statistical analysis result.
  • the statistical analysis result reference screen 4200 includes a disease selection column 4201, an analysis result list display area 4202, a display area 4203 of the analysis result graph 1, an X-axis selection column 4204 of the analysis result graph 1, and a Y-axis of the analysis result graph 1.
  • a selection field 4205, an analysis result graph 2 display area 4206, an analysis result graph 2 X-axis selection field 4207, and an analysis result graph 2 Y-axis selection field 4208 are included.
  • the user operates a drop-down box in the disease selection field 4201 to select a disease whose analysis result is displayed in the analysis result list display area 4202 from a plurality of disease names displayed in the drop-down box (not shown).
  • the analysis result list display area 4202 displays the data of the disease selected in the disease selection column 4201 acquired from the medical treatment cost table 3800 for each drug.
  • the user changes the graph displayed in the display area 4203 of the analysis result graph 1 by operating the X axis selection column 4204 and the Y axis selection column 4205 of the analysis result graph 1.
  • the analysis result graph 1 display area 4203 displays the data of the disease selected in the disease selection column 4201 acquired from the side-by-side treatment cost table 3700 and the medicine-by-medical treatment cost table 3800, and the X-axis selection in the analysis result graph 1 Displayed on the graph of the axis selected in the column 4204 and the Y-axis selection column 4205.
  • the user changes the graph displayed in the display area 4206 of the analysis result graph 2 by operating the X axis selection column 4207 and the Y axis selection column 4208 of the analysis result graph 2.
  • the analysis result graph 2 display area 4206 displays the data of the disease selected in the disease selection column 4201 acquired from the side-by-side medical treatment cost table 3700 and the medicine-by-medical medical cost table 3800, and the X-axis selection of the analysis result graph 2 Displayed on the graph of the axis selected in the column 4207 and the Y-axis selection column 4208.
  • the average medical cost and the predicted medical cost of the disease “diabetes” are displayed in the analysis result list display area 4202, and the predicted medical cost of the drug A is the drug S. It is displayed that it is smaller than the predicted medical cost.
  • the side effect analysis support system of the third example calculates the total medical cost by totaling the cost of the medical treatment of the patient during the hospitalization period, sums the total medical cost for each side effect, and calculates the total value. Is divided by the number of patients for each side effect, and the total cost of treatment is calculated for each side effect and drug group, and the total value is divided by the number of patients for each side effect and drug.
  • the medical cost drug
  • the predicted medical cost, drug efficacy, and side effects due to the prescription of the drug can be displayed in an integrated manner. For this reason, it becomes possible to select the medicine to be used while comprehensively considering these pieces of information.
  • the side effect analysis support system of each embodiment of the present invention information that supports the appropriate medication treatment by simultaneously visualizing the side effect risk of various drugs, the risk prescription days, the drug efficacy evaluation, and the predicted medical cost.
  • a system can be provided.
  • the present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the scope of the appended claims.
  • the above-described embodiments have been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to those having all the configurations described.
  • a part of the configuration of one embodiment may be replaced with the configuration of another embodiment.
  • another configuration may be added, deleted, or replaced.
  • each of the above-described configurations, functions, processing units, processing means, etc. may be realized in hardware by designing a part or all of them, for example, with an integrated circuit, and the processor realizes each function. It may be realized by software by interpreting and executing the program to be executed.
  • Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, and an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, and a DVD.
  • a storage device such as a memory, a hard disk, and an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, and a DVD.
  • control lines and information lines indicate what is considered necessary for the explanation, and do not necessarily indicate all control lines and information lines necessary for mounting. In practice, it can be considered that almost all the components are connected to each other.

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Abstract

This analysis system calculates, from variations in test results, a period of improvement in relation to a side effect, then calculates side effect-causing medication information indicating a connection between prescribed medication, excluding medication that was continuously prescribed after the beginning of the period of improvement, and the side effect, and outputs information about the medication connected with the side effect.

Description

分析システムAnalysis system
　本発明は、薬剤の副作用の分析を支援する分析システムに関する。 The present invention relates to an analysis system that supports analysis of drug side effects.
　近年、医療の高度化及び高齢化社会の進展に伴い、医療費の増加が社会問題化している。このため、政府機関や病院では、医療機関に蓄積された診療データを活用し、医療の質の向上やコストの最適化を支援するヘルスケアデータ分析が求められている。このうち、院内の副作用の発生状況や薬剤の治療効果を分析して可視化する投薬治療プロセス分析技術が検討されている。 In recent years, with the advancement of medical care and the aging society, an increase in medical expenses has become a social problem. For this reason, government agencies and hospitals are demanding healthcare data analysis that uses medical data accumulated in medical institutions to support improvements in medical quality and cost optimization. Among these, a medication treatment process analysis technique for analyzing and visualizing the occurrence of side effects in the hospital and the therapeutic effect of drugs has been studied.
　本技術の背景技術として、特開２００２－２４５１７２号公報（特許文献１）がある。特許文献１には、「副作用と、その原因となり得る薬剤名と、その薬剤による副作用の「発現頻度」、「臨床的検査法」、「臨床的観察法」、「防止法」、「対処法」、「高齢者、妊・産・授乳婦、新生児、低出生体重児、幼・小児等での注意事項」の副作用関連情報とが対応して記憶された情報記憶手段を備える。ユーザから指定を受けた副作用と薬剤名とに基づいて情報記憶手段を検索して、その副作用を起こす原因薬剤名を特定してユーザ側に出力すると共に、原因薬剤に対する副作用関連情報を情報記憶手段から取得してユーザ側に出力可能な手段も備える。」と記載されている（要約参照）。 There is JP 2002-245172 (Patent Document 1) as background art of this technology. Patent Document 1 includes “a side effect, a name of a drug that can cause the side effect, a“ frequency of occurrence ”of a side effect caused by the drug, a“ clinical test method ”, a“ clinical observation method ”, a“ prevention method ”, and a“ coping method ”. ", Information storage means for storing side-effect related information such as" Precautions for elderly people, pregnant / child / nursing women, newborns, low birth weight infants, infants / children, etc. ". The information storage means is searched based on the side effect specified by the user and the drug name, the causative drug name causing the side effect is specified and output to the user side, and the side effect related information for the causative drug is also stored in the information storage means And a means capable of obtaining and outputting to the user side. (See summary).
特開２００２－２４５１７２号公報JP 2002-245172 A
　日々の診療では、治験のような特定の薬剤のみが処方される環境ではなく、多種多様な複数の薬剤が併用されるため、前述した従来の技術では、複数の併用される薬剤のうち、どの薬剤が副作用の原因か、どの薬剤が検査値を改善させたか等が判らないという課題がある。 In daily practice, it is not an environment where only specific drugs are prescribed as in clinical trials, but a wide variety of drugs are used in combination. Therefore, in the conventional technology described above, which of the drugs used in combination is There is a problem that it is not known whether a drug causes a side effect or which drug improves a test value.
　本発明の目的は、多種多様な薬剤が処方される投薬治療において、症例毎の副作用の原因薬剤や治療効果を示した薬剤等を可視化し、適切な投薬治療を支援する情報システムを提供することである。 It is an object of the present invention to provide an information system for visualizing a drug causing a side effect for each case, a drug showing a therapeutic effect, etc. in a medication treatment in which a wide variety of medications are prescribed, and supporting an appropriate medication treatment. It is.
　本願において開示される発明の代表的な一例を示せば以下の通りである。すなわち、プロセッサと、前記プロセッサに接続される記憶装置とを備える分析システムであって、前記記憶装置は、患者の疾患及び入院期間を含む症例情報と、前記患者への薬剤の処方期間を含む処方情報と、前記患者の検査結果を含む検査情報と、前記検査結果を副作用であるかを判定するための判定条件を含む知識情報とを保持し、前記プロセッサは、前記検査情報に記録された検査結果の変動から副作用の改善期間を算出し、前記処方期間を、前記処方情報から取得し、前記処方されている薬剤のうち、前記改善期間の開始後に継続して処方されている薬剤以外の薬剤が前記副作用と関係することを示す副作用原因薬剤情報を算出し、前記副作用と関係する薬剤の情報を出力することを特徴とする分析システム。 A typical example of the invention disclosed in the present application is as follows. That is, an analysis system including a processor and a storage device connected to the processor, wherein the storage device includes case information including a patient's disease and hospitalization period, and a prescription period including a prescription period of the drug for the patient. Information, test information including the patient test result, and knowledge information including determination conditions for determining whether the test result is a side effect, the processor stores the test recorded in the test information A side effect improvement period is calculated from the change in results, the prescription period is acquired from the prescription information, and among the prescription drugs, a drug other than a prescription medicine continuously prescribed after the start of the improvement period An analysis system characterized in that side effect-causing drug information indicating that the drug is related to the side effect is calculated, and information on the drug related to the side effect is output.
　本発明の一形態によれば、副作用の原因となった薬剤を推定することができる。前述した以外の課題、構成及び効果は、以下の実施例の説明により明らかにされる。 According to one aspect of the present invention, it is possible to estimate a drug that causes a side effect. Problems, configurations, and effects other than those described above will become apparent from the description of the following embodiments.
本発明の第１実施例の副作用分析支援システムの構成を示すブロック図である。It is a block diagram which shows the structure of the side effect analysis assistance system of 1st Example of this invention. 本発明の第１実施例の症例情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the case information table of 1st Example of this invention. 本発明の第１実施例の処方情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the prescription information table of 1st Example of this invention. 本発明の第１実施例の検査情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the test | inspection information table of 1st Example of this invention. 本発明の第１実施例の副作用知識情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the side effect knowledge information table of 1st Example of this invention. 本発明の第１実施例の設定情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the setting information table of 1st Example of this invention. 本発明の第１実施例の疾患情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the disease information table of 1st Example of this invention. 本発明の第１実施例の薬剤情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the chemical | medical agent information table of 1st Example of this invention. 本発明の第１実施例の副作用症例テーブルの構成例を示す図である。It is a figure which shows the structural example of the side effect case table of 1st Example of this invention. 本発明の第１実施例の副作用薬剤テーブルの構成例を示す図である。It is a figure which shows the structural example of the side effect medicine table of 1st Example of this invention. 本発明の第１実施例の薬効情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the medicinal effect information table of 1st Example of this invention. 本発明の第１実施例の副作用リスクテーブルの構成例を示す図である。It is a figure which shows the structural example of the side effect risk table of 1st Example of this invention. 本発明の第１実施例の薬効評価テーブルの構成例を示す図である。It is a figure which shows the structural example of the medicinal effect evaluation table of 1st Example of this invention. 本発明の第１実施例の分析処理のフローチャートである。It is a flowchart of the analysis process of 1st Example of this invention. 本発明の第１実施例の副作用発生症例抽出処理のフローチャートである。It is a flowchart of the side effect occurrence case extraction process of 1st Example of this invention. 本発明の第１実施例の改善期間算出処理のフローチャートである。It is a flowchart of the improvement period calculation process of 1st Example of this invention. 本発明の第１実施例の副作用原因算出処理のフローチャートである。It is a flowchart of the side effect cause calculation process of 1st Example of this invention. 本発明の第１実施例の薬効情報算出処理のフローチャートである。It is a flowchart of the medicinal effect information calculation process of 1st Example of this invention. 本発明の第１実施例の副作用リスク算出処理のフローチャートである。It is a flowchart of the side effect risk calculation process of 1st Example of this invention. 本発明の第１実施例の薬効評価算出処理のフローチャートである。It is a flowchart of the medicinal effect evaluation calculation process of 1st Example of this invention. 本発明の第１実施例の分析画面の例を示す図である。It is a figure which shows the example of the analysis screen of 1st Example of this invention. 本発明の第１実施例の設定画面の例を示す図である。It is a figure which shows the example of the setting screen of 1st Example of this invention. 本発明の第１実施例の症例分析画面の例を示す図である。It is a figure which shows the example of the case analysis screen of 1st Example of this invention. 本発明の第１実施例の統計解析結果参照画面の例を示す図である。It is a figure which shows the example of the statistical-analysis result reference screen of 1st Example of this invention. 本発明の第２実施例の副作用後分析対象日数設定テーブルの構成例を示す図である。It is a figure which shows the structural example of the analysis target days setting table after a side effect of 2nd Example of this invention. 本発明の第２実施例の副作用原因情報更新処理のフローチャートである。It is a flowchart of the side effect cause information update process of 2nd Example of this invention. 本発明の第２実施例の症例分析画面の例を示す図である。It is a figure which shows the example of the case analysis screen of 2nd Example of this invention. 本発明の第３実施例の診療行為コスト情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the medical practice cost information table of 3rd Example of this invention. 本発明の第３実施例の処置情報テーブルの構成例を示す図である。It is a figure which shows the structural example of the treatment information table of 3rd Example of this invention. 本発明の第３実施例の副作用別診療コストテーブルの構成例を示す図である。It is a figure which shows the structural example of the medical treatment cost table classified by side effect of 3rd Example of this invention. 本発明の第３実施例の薬剤別診療コストテーブルの構成例を示す図である。It is a figure which shows the structural example of the medical treatment cost table classified by medicine of 3rd Example of this invention. 本発明の第３実施例の診療コスト算出処理のフローチャートである。It is a flowchart of the medical treatment cost calculation process of 3rd Example of this invention. 本発明の第３実施例の平均診療コスト算出処理のフローチャートである。It is a flowchart of the average medical treatment cost calculation process of 3rd Example of this invention. 本発明の第３実施例の統計解析結果参照画面の例を示す図である。It is a figure which shows the example of the statistical analysis result reference screen of 3rd Example of this invention.
　以下、図面に基づいて、本発明の実施の形態を説明する。なお、本発明の実施形態は、後述する形態例に限定されるものではなく、その技術思想の範囲において、種々の変形が可能である。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments of the present invention are not limited to the embodiments described later, and various modifications are possible within the scope of the technical idea.
　＜実施例１＞
　図１は、本発明の第１実施例の副作用分析支援システムの構成を示すブロック図である。 <Example 1>
FIG. 1 is a block diagram showing the configuration of a side effect analysis support system according to the first embodiment of the present invention.
　本実施例の副作用分析支援システムは、症例毎に各薬剤の副作用及び効果を分析し、分析結果を可視化して出力する計算機システムであり、病院情報システム１２０と、ネットワーク１４０と、入出力端末１３０とで構成される。 The side effect analysis support system of this embodiment is a computer system that analyzes side effects and effects of each drug for each case, visualizes and outputs the analysis results, and includes a hospital information system 120, a network 140, and an input / output terminal 130. It consists of.
　本実施例では、入出力端末１３０は、キーボード、マウス、又はタッチパネルなどの入力部（図示省略）と、ディスプレイなどの出力部（図示省略）と、データサーバ１００などと通信する通信部（図示省略）と、を有する一つ又は複数のパーソナルコンピュータである。また、ボタン又はタッチパネルなどの入力部とディスプレイなどの出力部と、データサーバ１００などと通信する通信部とを有するＰＤＡ、ＰＨＳ、携帯電話、スマートフォン、タブレット端末などの可搬型端末を入出力端末１３０として利用してもよい。 In this embodiment, the input / output terminal 130 includes an input unit (not shown) such as a keyboard, a mouse, or a touch panel, an output unit (not shown) such as a display, and a communication unit (not shown) that communicates with the data server 100 and the like. And one or more personal computers. In addition, a portable terminal such as a PDA, PHS, mobile phone, smartphone, or tablet terminal having an input unit such as a button or a touch panel, an output unit such as a display, and a communication unit that communicates with the data server 100 or the like is input / output terminal 130. It may be used as
　第１実施例のシステムでは、入出力端末１３０が病院、診療所などの医療機関（ヘルスケアプロバイダ）、厚労省などの国の機関などに設置されて、ユーザに利用される。一方、データサーバ１００はデータセンタに設置されてもよいし、各機関に設置されてもよい。データサーバ１００をデータセンタに設置することによって、ユーザの個人情報及びユーザから収集されるデータなどのプライバシー情報を一元的に管理できるので、情報漏洩防止等のセキュリティを確実に管理することができる。運用の形態によっては、データサーバ１００をヘルスケアプロバイダに設置してもよい。 In the system of the first embodiment, the input / output terminal 130 is installed in a medical institution (health care provider) such as a hospital or clinic, a national institution such as the Ministry of Health, Labor and Welfare, and used by the user. On the other hand, the data server 100 may be installed in a data center or in each organization. By installing the data server 100 in the data center, privacy information such as personal information of the user and data collected from the user can be managed centrally, so that security such as information leakage prevention can be reliably managed. Depending on the mode of operation, the data server 100 may be installed in a healthcare provider.
　入出力端末１３０のユーザは、医師、各機関の管理者及び各機関の経営責任者である。ユーザは、入出力端末１３０を操作し、本実施例で示される情報システムを用いて、薬剤の副作用、効果を分析し、及び可視化する。 The user of the input / output terminal 130 is a doctor, an administrator of each organization, and a manager of each organization. The user operates the input / output terminal 130 to analyze and visualize the side effects and effects of the drug using the information system shown in the present embodiment.
　データサーバ１００は、相互に接続された蓄積データ取得部１１９、制御部１０１、出力部１０２、メモリ１０３、通信部１０４、表示画面生成部１０５、症例データ抽出部１０６、処方データ抽出部１０７、検査データ抽出部１０８、検査情報解釈部１０９、副作用症例抽出部１１０、副作用原因情報算出部１１１、薬効情報算出部１１２、疾患選択部１１３、統計解析部１１４、整列制御部１１５、統合データベース１１６、知識データベース１１７、副作用分析データベース１１８及び診療コスト算出部１５１で構成される。なお、診療コスト算出部１５１は、第３実施例で使用される処理部であり、第１及び第２実施例では不要である。 The data server 100 includes an accumulated data acquisition unit 119, a control unit 101, an output unit 102, a memory 103, a communication unit 104, a display screen generation unit 105, a case data extraction unit 106, a prescription data extraction unit 107, and an examination that are connected to each other. Data extraction unit 108, examination information interpretation unit 109, side effect case extraction unit 110, side effect cause information calculation unit 111, medicinal effect information calculation unit 112, disease selection unit 113, statistical analysis unit 114, alignment control unit 115, integrated database 116, knowledge A database 117, a side effect analysis database 118, and a medical cost calculation unit 151 are configured. The medical cost calculation unit 151 is a processing unit used in the third embodiment, and is not necessary in the first and second embodiments.
　統合データベース１１６は、症例データベース１２１、検査情報データベース１２２及び処方情報データベース１２３に蓄積されたデータを格納する。知識データベース１１７は、副作用知識情報テーブル５００（図５参照）と、設定情報テーブル６００（図６参照）と、疾患情報テーブル７００（図７参照）と、薬剤情報テーブル８００（図８参照）とを含む。これらのテーブルの構成は、各図を用いて後述する。副作用分析データベース１１８は、副作用症例テーブル９００（図９参照）と、副作用薬剤テーブル１０００（図１０参照）と、薬効情報テーブル１１００（図１１参照）と、副作用リスクテーブル１２００（図１２参照）と、薬効評価テーブル１３００（図１３参照）とを含む。これらのテーブルの構成は、各図を用いて後述する。 The integrated database 116 stores data accumulated in the case database 121, the examination information database 122, and the prescription information database 123. The knowledge database 117 includes a side effect knowledge information table 500 (see FIG. 5), a setting information table 600 (see FIG. 6), a disease information table 700 (see FIG. 7), and a drug information table 800 (see FIG. 8). Including. The configuration of these tables will be described later with reference to each drawing. The side effect analysis database 118 includes a side effect case table 900 (see FIG. 9), a side effect drug table 1000 (see FIG. 10), a medicinal effect information table 1100 (see FIG. 11), a side effect risk table 1200 (see FIG. 12), And a medicinal effect evaluation table 1300 (see FIG. 13). The configuration of these tables will be described later with reference to each drawing.
　蓄積データ取得部１１９は、ヘルスケアプロバイダに設置してある病院情報システム１２０が格納している症例データベース１２１と、検査情報データベース１２２と、処方情報データベース１２３とに蓄積されたデータを取得し、統合データベース１１６に格納する。蓄積データ取得部１１９は、ユーザにより直接起動される、又は、ユーザが予め設定した時間（例えば、毎週土曜の夜など）に定期的に起動されてもよい。また、症例データベース１２１と、検査情報データベース１２２と、処方情報データベース１２３とのデータが更新されたタイミングで蓄積データ取得部１１９を起動してもよい。 The accumulated data acquisition unit 119 acquires and integrates data accumulated in the case database 121, the examination information database 122, and the prescription information database 123 stored in the hospital information system 120 installed in the healthcare provider. Store in the database 116. The accumulated data acquisition unit 119 may be activated directly by the user, or may be activated periodically at a time preset by the user (for example, every Saturday night). Further, the accumulated data acquisition unit 119 may be activated at a timing when data in the case database 121, the examination information database 122, and the prescription information database 123 are updated.
　制御部１０１は、例えば、メモリ１０３に格納されたプログラムを実行するプロセッサによって構成されており、データサーバ１００の各部を制御する。メモリ１０３は、例えばＤＲＡＭ（Ｄｙｎａｍｉｃ　Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）のような記憶装置であり、データサーバ１００の各部によって参照されるデータ（例えば、制御部１０１によって実行されるプログラム等）を格納する。 The control unit 101 includes, for example, a processor that executes a program stored in the memory 103, and controls each unit of the data server 100. The memory 103 is a storage device such as a DRAM (Dynamic Random Access Memory), and stores data referred to by each unit of the data server 100 (for example, a program executed by the control unit 101).
　制御部１０１が実行するプログラムは、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）のような、データサーバ１００内の不揮発性記憶装置（図示省略）から読み出されて、メモリ１０３にロードされて、制御部１０１内のプロセッサによって実行される。また、制御部１０１が実行するプログラムは、リムーバブルメディア（ＣＤ－ＲＯＭ、フラッシュメモリなど）又はネットワークを介してデータサーバ１００に提供され、非一時的記憶媒体である不揮発性記憶装置に格納される。このため、データサーバ１００は、リムーバブルメディアからデータを読み込むインターフェースを有するとよい。 The program executed by the control unit 101 is read from a nonvolatile storage device (not shown) in the data server 100 such as an HDD (Hard Disk Drive), loaded into the memory 103, and stored in the control unit 101. Executed by the processor. The program executed by the control unit 101 is provided to the data server 100 via a removable medium (CD-ROM, flash memory, etc.) or a network, and is stored in a nonvolatile storage device that is a non-temporary storage medium. For this reason, the data server 100 may have an interface for reading data from a removable medium.
　統合データベース１１６、知識データベース１１７及び副作用分析データベース１１８は、例えば、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）のような、データサーバ１００内の不揮発性記憶装置（図示省略）に格納される。なお、データベース１１６、１１７、１１８は、メモリ１０３に格納されてもよい。データベース１１６、１１７、１１８に格納されたデータは、後述する処理において、不揮発性記憶装置から読み出され、メモリ１０３に展開される。なお、データベース１１６、１１７、１１８がメモリ１０３に格納されている場合、データベース１１６、１１７、１１８に格納されたデータは、メモリ１０３から取得すればよい。 The integrated database 116, the knowledge database 117, and the side effect analysis database 118 are stored in a nonvolatile storage device (not shown) in the data server 100, such as an HDD (Hard Disk Drive). Note that the databases 116, 117, and 118 may be stored in the memory 103. Data stored in the databases 116, 117, and 118 is read from the non-volatile storage device and expanded in the memory 103 in a process described later. Note that when the databases 116, 117, and 118 are stored in the memory 103, the data stored in the databases 116, 117, and 118 may be acquired from the memory 103.
　出力部１０２は、データサーバ１００による処理の結果を出力する装置であり、例えばディスプレイ装置でもよい。 The output unit 102 is a device that outputs a result of processing by the data server 100, and may be a display device, for example.
　通信部１０４は、ネットワーク１４０に接続され、入出力端末１３０と通信する。 The communication unit 104 is connected to the network 140 and communicates with the input / output terminal 130.
　表示画面生成部１０５、症例データ抽出部１０６、処方データ抽出部１０７、検査データ抽出部１０８、検査情報解釈部１０９、副作用症例抽出部１１０、副作用原因情報算出部１１１、薬効情報算出部１１２、疾患選択部１１３、統計解析部１１４及び整列制御部１１５は、データサーバ１００の機能を実現するための処理を実行する処理部であり、それぞれが専用のハードウェアによって実現されてもよいし、ソフトウェアによって実現されてもよい。ソフトウェアによって各処理部を実現する場合、以下の説明において、各処理部が実行する処理は、実際には、制御部１０１がメモリ１０３に格納されたプログラムに記述された命令に従って実行する。前述した各処理部によって実行される処理の詳細については後述する。 Display screen generation unit 105, case data extraction unit 106, prescription data extraction unit 107, test data extraction unit 108, test information interpretation unit 109, side effect case extraction unit 110, side effect cause information calculation unit 111, medicinal effect information calculation unit 112, disease The selection unit 113, the statistical analysis unit 114, and the alignment control unit 115 are processing units that execute processing for realizing the functions of the data server 100, and each may be realized by dedicated hardware, or by software It may be realized. When each processing unit is realized by software, in the following description, the processing executed by each processing unit is actually executed by the control unit 101 in accordance with an instruction described in a program stored in the memory 103. Details of the processing executed by each processing unit described above will be described later.
　第１実施例のデータサーバ１００は、物理的に一つの計算機上で、又は、論理的又は物理的に構成された複数の計算機上で構成される計算機システムであり、同一の計算機上で別個のスレッドで動作してもよく、複数の物理的計算機資源上に構築された仮想計算機上で動作してもよい。 The data server 100 according to the first embodiment is a computer system configured on a single physical computer or a plurality of logically or physically configured computers. It may operate on a thread, or may operate on a virtual computer constructed on a plurality of physical computer resources.
　症例データベース１２１、検査情報データベース１２２、処方情報データベース１２３及び診療行為情報データベース１２４とは、例えば、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）のような、病院情報システム１２０内の別の記憶装置（図示省略）に格納されてもよい。症例データベース１２１は、症例情報テーブル２００（図２参照）を含む。検査情報データベース１２２は、検査情報テーブル４００（図４参照）を含む。処方情報データベース１２３は、処方情報テーブル３００（図３参照）を含む。診療行為情報データベース１２４は、第３実施例で使用されるテーブルであり、第１及び第２実施例では不要である。診療行為情報データベース１２４は、診療行為コスト情報テーブル３３００（図２８参照）と、処置情報テーブル３５００（図２９参照）とを含む。これらのテーブルの構成は、各図を用いて後述する。 The case database 121, the examination information database 122, the prescription information database 123, and the medical practice information database 124 are stored in another storage device (not shown) in the hospital information system 120 such as an HDD (Hard Disk Drive). May be. The case database 121 includes a case information table 200 (see FIG. 2). The inspection information database 122 includes an inspection information table 400 (see FIG. 4). The prescription information database 123 includes a prescription information table 300 (see FIG. 3). The medical practice information database 124 is a table used in the third embodiment, and is unnecessary in the first and second embodiments. The medical practice information database 124 includes a medical practice cost information table 3300 (see FIG. 28) and a treatment information table 3500 (see FIG. 29). The configuration of these tables will be described later with reference to each drawing.
　ネットワーク１４０は、データサーバ１００、病院情報システム１２０及び入出力端末１３０を接続する。データサーバ１００は、ネットワーク１４０を介して病院情報システム１２０及び入出力端末１３０と通信する。ネットワーク１４０は、ＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）ケーブルによる有線通信又は無線ＬＡＮによる無線通信を利用して、各装置間を接続する。また、ネットワーク１４０は、インターネット、ＶＰＮ、携帯電話通信網、ＰＨＳ通信網など、他の広域ネットワークを利用してもよい。 The network 140 connects the data server 100, the hospital information system 120, and the input / output terminal 130. The data server 100 communicates with the hospital information system 120 and the input / output terminal 130 via the network 140. The network 140 connects devices using wired communication using a LAN (Local Area Network) cable or wireless communication using a wireless LAN. The network 140 may use another wide area network such as the Internet, VPN, mobile phone communication network, and PHS communication network.
　以下では、統合データベース１１６を構成するテーブルの構造を説明する。第１実施例の統合データベース１１６は、症例毎の基本情報等を管理する症例情報テーブル２００（図２）と、症例毎の処方情報を記録する処方情報テーブル３００（図３）と、症例毎の検査情報を格納する検査情報テーブル４００（図４）とで構成される。 Hereinafter, the structure of the table constituting the integrated database 116 will be described. The integrated database 116 of the first embodiment includes a case information table 200 (FIG. 2) for managing basic information for each case, a prescription information table 300 (FIG. 3) for recording prescription information for each case, It comprises an inspection information table 400 (FIG. 4) for storing inspection information.
　図２から図１３、図２８から図３１に示すテーブルは、本発明の各実施例の副作用分析支援システムを実現するために必要なデータ一例であり、システムに実装される機能によっては、図示されていないフィールドを含んでもよいし、図示されているフィールドの一部を含まなくてもよい。 The tables shown in FIG. 2 to FIG. 13 and FIG. 28 to FIG. 31 are examples of data necessary for realizing the side effect analysis support system of each embodiment of the present invention, and may be shown depending on the functions implemented in the system. May be included, or some of the illustrated fields may not be included.
　図２は、症例情報テーブル２００の構成例を示す図である。 FIG. 2 is a diagram illustrating a configuration example of the case information table 200.
　症例情報テーブル２００は、症例データベース１２１から取得した症例情報レコードを格納するテーブルであり、患者ＩＤ２０１、性別２０２、入院フラグ２０３、診断日２０４、入院年月日２０５、退院年月日２０６及び疾患名２０７のフィールドを含む。患者ＩＤ２０１は、患者を一意に識別するための識別情報である。性別２０２は、患者の性別であり、「０」又は「１」のフラグで表してもよい。入院フラグ２０３は、医療機関の入院のデータであるか、外来のデータであるかを示すフラグである。診断日２０４は、医療機関の外来の診断日である。入院年月日２０５及び退院年月日２０６は、それぞれ、医療機関の入院年月日及び退院年月日である。疾患名２０７は、疾患の名称である。 The case information table 200 is a table for storing case information records acquired from the case database 121, and includes a patient ID 201, gender 202, hospitalization flag 203, diagnosis date 204, hospitalization date 205, discharge date 206, and disease name. 207 fields are included. The patient ID 201 is identification information for uniquely identifying a patient. The gender 202 is the gender of the patient, and may be represented by a flag of “0” or “1”. The hospitalization flag 203 is a flag indicating whether it is hospital hospital data or outpatient data. The diagnosis date 204 is an outpatient diagnosis date of a medical institution. The hospitalization date 205 and the discharge date 206 are the hospitalization date and discharge date of the medical institution, respectively. The disease name 207 is the name of the disease.
　なお、入院フラグ２０３が「１」であれば入院のデータであることを示し、診断日２０４には「０」が格納される。一方、入院フラグ２０３が「０」であれば外来のデータを示し、入院年月日２０５及び退院年月日２０６には「０」が格納される。 Note that if the hospitalization flag 203 is “1”, this indicates hospitalization data, and “0” is stored in the diagnosis date 204. On the other hand, if the hospitalization flag 203 is “0”, it indicates outpatient data, and “0” is stored in the hospitalization date 205 and the discharge date 206.
　症例情報テーブル２００の各症例情報レコードに、一つの症例に関する情報が格納される。一人の患者の１回の入院が一つの症例となる。例えば、図２に示す症例情報テーブル２００の症例情報レコード２００Ａは、患者ＩＤが「＃＃＃１」の男性の患者が、糖尿病の治療のために２０１４年４月１日から２０１４年４月２６日まで入院したことを示す。 Information on one case is stored in each case information record of the case information table 200. One hospitalization of one patient becomes one case. For example, the case information record 200A of the case information table 200 shown in FIG. 2 indicates that a male patient whose patient ID is “## 1” is used for the treatment of diabetes from April 1, 2014 to April 26, 2014. Shows that he was hospitalized until the day.
　図３は、処方情報テーブル３００の構成例を示す図である。 FIG. 3 is a diagram illustrating a configuration example of the prescription information table 300.
　処方情報テーブル３００は、処方情報データベース１２３から取得した処方情報レコードを格納するテーブルであり、患者ＩＤ３０１、薬剤名３０２、処方開始日３０３及び処方終了日３０４のフィールドを含む。患者ＩＤ３０１は、患者を一意に識別するための識別情報である。薬剤名３０２は、当該患者に処方された薬剤を識別するための情報である。処方開始日３０３及び処方終了日３０４は、当該薬剤の処方を開始した日及び処方を終了した日である。 The prescription information table 300 is a table for storing prescription information records acquired from the prescription information database 123, and includes fields of a patient ID 301, a drug name 302, a prescription start date 303, and a prescription end date 304. The patient ID 301 is identification information for uniquely identifying a patient. The drug name 302 is information for identifying a drug prescribed to the patient. The prescription start date 303 and the prescription end date 304 are the date when the prescription of the drug is started and the date when the prescription is completed.
　例えば、図３に示す処方情報テーブル３００の処方情報レコード３００Ａ及び３００Ｂは、患者ＩＤが「＃＃＃１」の症例において、薬剤Ａが２０１４年４月１日から１４日までの期間に処方されたことを示す。また、処方情報レコード３００Ｃ～３００Ｄは、薬剤Ｂが２０１４年４月３日から２０日までの期間に処方されたことを示す。また、処方情報レコード３００Ｅは、薬剤Ｃが２０１４年４月１４日から１８日までの期間に処方されたことを示す。また、処方情報テーブル３００は、各薬剤の処方の用法・用量を格納するフィールドを有してもよい（図示省略）。 For example, in the prescription information records 300A and 300B of the prescription information table 300 shown in FIG. 3, in the case where the patient ID is “## 1”, the drug A is prescribed in the period from April 1 to 14, 2014. It shows that. The prescription information records 300C to 300D indicate that the medicine B was prescribed during the period from April 3rd to 20th, 2014. The prescription information record 300E indicates that the medicine C was prescribed during the period from April 14th to 18th, 2014. The prescription information table 300 may have a field for storing the prescription usage / dose of each drug (not shown).
　図４は、検査情報テーブル４００の構成例を示す図である。 FIG. 4 is a diagram illustrating a configuration example of the inspection information table 400.
　検査情報テーブル４００は、検査情報データベース１２２から取得した検査情報レコードを格納するテーブルであり、患者ＩＤ４０１、検査項目４０２、検査日４０３及び検査値４０４のフィールドを含む。患者ＩＤ４０１は、患者を一意に識別するための識別情報である。検査項目４０２は、検査項目を識別する情報である。検査日４０３は、当該検査を行った日である。検査値４０４は、当該検査の結果である。 The examination information table 400 is a table that stores examination information records acquired from the examination information database 122, and includes fields of a patient ID 401, an examination item 402, an examination date 403, and an examination value 404. The patient ID 401 is identification information for uniquely identifying a patient. The inspection item 402 is information for identifying the inspection item. The inspection date 403 is the date when the inspection is performed. The inspection value 404 is a result of the inspection.
　例えば、図４に示す検査情報テーブル４００の例では、患者ＩＤが「＃＃＃１」の症例において、肝機能の状態を示す検査項目であるＡＬＴ（ＧＰＴ）の値が、２０１４年４月１日３４、２０１４年４月７日に６２、２０１４年４月１３日に９０、２０１４年４月１５日に１２５、２０１４年４月１８日に７８、２０１４年４月２５日に４１であったことを示し、ＡＳＴ（ＧＯＴ）の値が、２０１４年４月１日に２０であったことを示す。 For example, in the example of the examination information table 400 shown in FIG. 4, in the case of the patient ID “## 1”, the value of ALT (GPT) that is an examination item indicating the state of liver function is April 1, 2014. Date 34, April 7, 2014 62, April 13, 2014 90, April 15, 2014 125, April 18, 2014 78, April 25, 2014 41 It is shown that the value of AST (GOT) was 20 on April 1, 2014.
　また、検査情報テーブル４００は、画像検査（例えば、ＣＴ画像検査）などの前述以外の種類の検査結果を示す情報を含んでもよく、さらに、「吐き気」又は「嘔吐」といった患者の自覚症状に関する情報を含んでもよい。 In addition, the examination information table 400 may include information indicating examination results other than those described above, such as an image examination (for example, CT image examination), and information related to the patient's subjective symptoms such as “nausea” or “vomiting”. May be included.
　以下では、知識データベース１１７を構成するテーブルの構造を説明する。知識データベース１１７は、副作用に対する検査項目と副作用を判定するための検査値の閾値とを管理する副作用知識情報テーブル５００（図５）と、副作用詳細の原因となる薬剤を分析の対象として抽出する期間を管理する設定情報テーブル６００（図６）と、疾患に対する症状を管理する疾患情報テーブル７００（図７）と、薬剤の効果と既知の副作用とを管理する薬剤情報テーブル８００（図８）とで構成される。 Hereinafter, the structure of the tables constituting the knowledge database 117 will be described. The knowledge database 117 includes a side effect knowledge information table 500 (FIG. 5) that manages test items for side effects and test value thresholds for determining side effects, and a period for extracting drugs that cause side effect details as analysis targets. A setting information table 600 (FIG. 6) for managing the disease, a disease information table 700 (FIG. 7) for managing symptoms for the disease, and a drug information table 800 (FIG. 8) for managing the effect of the drug and known side effects. Composed.
　図５は、副作用知識情報テーブル５００の構成例を示す図である。 FIG. 5 is a diagram showing a configuration example of the side effect knowledge information table 500.
　副作用知識情報テーブル５００は、副作用５０１、副作用詳細５０２、検査項目５０３、異常値（男性）５０４及び異常値（女性）５０５のフィールドを含む。副作用５０１は、副作用の情報（例えば、名称）である。副作用詳細５０２は、当該副作用の詳細な情報である。検査項目５０３は、当該副作用詳細に対する検査項目である。異常値（男性）５０４は、当該副作用詳細の副作用が発生していると判定するための男性用の閾値である。異常値（女性）５０５は、当該副作用詳細の副作用が発生していると判定するための女性用の閾値である。 The side effect knowledge information table 500 includes fields of side effect 501, side effect details 502, test item 503, abnormal value (male) 504, and abnormal value (female) 505. The side effect 501 is side effect information (for example, name). The side effect details 502 are detailed information on the side effects. The inspection item 503 is an inspection item for the side effect details. The abnormal value (male) 504 is a threshold value for men for determining that a side effect with the detailed side effect has occurred. The abnormal value (female) 505 is a threshold value for women for determining that a side effect with the detailed side effect has occurred.
　例えば、図５に示す副作用知識情報テーブル５００の副作用知識情報レコードの例５００Ａは、副作用「肝機能障害」、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」の副作用は、検査項目「ＡＬＴ（ＧＰＴ）」の値が、男性であれば４２を超過、女性であれば２７を超過したときに副作用と判断されることを示す。 For example, in the example 500A of the side effect knowledge information record of the side effect knowledge information table 500 shown in FIG. 5, the side effect “hepatic dysfunction” and the side effect detail “increase in ALT (GPT)” indicate the test item “ALT (GPT)”. When the value exceeds 42 for males and 27 for females, it indicates that a side effect is determined.
　図６は、設定情報テーブル６００の構成例を示す図である。 FIG. 6 is a diagram illustrating a configuration example of the setting information table 600.
　設定情報テーブル６００は、副作用詳細６０１及び抽出対象日数６０２のフィールドを含む。副作用詳細６０１は、副作用の詳細な情報である。抽出対象日数６０２は、当該副作用詳細の原因となる薬剤を分析の対象として抽出する日数である。 The setting information table 600 includes fields for side effect details 601 and extraction target days 602. The side effect details 601 are detailed information on side effects. The extraction target days 602 is the number of days for extracting a drug that causes the side effect details as an analysis target.
　例えば、図６に示す設定情報テーブル６００の例では、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」を分析するための原因薬剤の処方期間が３０日であり、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」を分析するための原因薬剤の処方期間が３０日であることを示す。 For example, in the example of the setting information table 600 illustrated in FIG. 6, the prescription period of the causative agent for analyzing the side effect detail “ALT (GPT) increase” is 30 days, and the side effect detail “ALT (GPT) increase”. It shows that the prescription period of the causative agent for analyzing is 30 days.
　なお、設定情報テーブル６００は、薬剤名のフィールドを含み、副作用詳細と薬剤との組み合わせに対して抽出対象期間を設定してもよい。また、設定情報テーブル６００に格納される情報は、ユーザが任意の期間を設定可能でもよい。 The setting information table 600 includes a drug name field, and the extraction target period may be set for the combination of the side effect details and the drug. Further, the information stored in the setting information table 600 may be settable by the user for an arbitrary period.
　図７は、疾患情報テーブル７００の構成例を示す図である。 FIG. 7 is a diagram illustrating a configuration example of the disease information table 700.
　疾患情報テーブル７００は、疾患名７０１及び症状７０２のフィールドを含む。疾患名７０１は、疾患の名称である。症状７０２は、当該疾患名に対する症状である。 The disease information table 700 includes fields for a disease name 701 and a symptom 702. The disease name 701 is a name of a disease. Symptom 702 is a symptom for the disease name.
　例えば、図７に示す疾患情報テーブル７００の例では、疾患「Ｃ型慢性肝炎」は、ＡＬＴ（ＧＰＴ）の増加及びＡＳＴ（ＧＯＴ）の増加の症状が発生することを示し、疾患名「糖尿病」は、ＨｂＡ１ｃが増加する症状が発生することを示す。 For example, in the example of the disease information table 700 illustrated in FIG. 7, the disease “chronic hepatitis C” indicates that ALT (GPT) increases and AST (GOT) increases, and the disease name “diabetes” Indicates that a symptom of increased HbA1c occurs.
　このように、疾患と症状とを対応付けることによって、検査値が異常を示した症例のうち、検査値が異常になる症状を有する疾患の症例を除外して、薬剤により検査値が異常になった症例を副作用の症例として抽出することができる。 In this way, by associating the disease with the symptom, out of the cases where the test value was abnormal, the case of the disease having the symptom where the test value was abnormal was excluded, and the test value became abnormal due to the drug Cases can be extracted as cases of side effects.
　図８は、薬剤情報テーブル８００の構成例を示す図である。 FIG. 8 is a diagram showing a configuration example of the medicine information table 800.
　薬剤情報テーブル８００は、薬剤名８０１、効果８０２、既知の副作用８０３及び対応疾患８０４のフィールドを含む。薬剤名８０１は、薬剤を識別するための情報である。効果８０２は、当該薬剤の効果である。既知の副作用８０３は、当該薬剤の既知の副作用である。対応疾患８０４は、当該薬剤が処方される疾患である。 Drug information table 800 includes fields of drug name 801, effect 802, known side effect 803, and corresponding disease 804. The drug name 801 is information for identifying a drug. The effect 802 is an effect of the drug. Known side effects 803 are known side effects of the drug. Corresponding disease 804 is a disease for which the drug is prescribed.
　例えば、図８に示す薬剤情報テーブル８００の薬剤情報レコードの例８００Ａは、薬剤名「薬剤Ａ」は、効果「Ｃｒｅの減少」、副作用「ＡＬＴ（ＧＰＴ）の増加」であることを示している。また、薬剤情報テーブル８００は、添付文書などに記載されている各薬剤の既知の副作用の発生率を格納するフィールドを有してもよい（図省略）。 For example, the example 800A of the drug information record in the drug information table 800 illustrated in FIG. 8 indicates that the drug name “drug A” has the effect “decrease Cre” and the side effect “increase ALT (GPT)”. . Further, the medicine information table 800 may have a field for storing the incidence of known side effects of each medicine described in an attached document (not shown).
　なお、薬剤名と対応疾患との関係は、ヘルスケアプロバイダが作成したものでも、副作用を分析及び可視する機関で作成されたものでもよい。また、薬剤名と対応疾患との関係は、知識データベース１１７でなく、診療行為情報データベース１２４に格納してもよい。さらに、データサーバ内にあらかじめ格納されたものを用いてもよい。 The relationship between the drug name and the corresponding disease may be created by a healthcare provider or created by an organization that analyzes and visualizes side effects. The relationship between the drug name and the corresponding disease may be stored in the medical practice information database 124 instead of the knowledge database 117. Furthermore, what is stored in advance in the data server may be used.
　このように、薬剤毎の効果及び既知の副作用情報によって、分析対象の効果又は副作用がある薬剤を絞り込んで効果及び副作用を分析することができ、既知の効果及び副作用の分析の精度を向上できる。また、薬剤毎の効果及び既知の副作用情報によって、分析対象の効果又は副作用がある薬剤を除外して効果及び副作用を分析することができ、未知の効果及び副作用を精度よく分析することができる。 As described above, the effects and side effects can be analyzed by narrowing down the drugs to be analyzed or the drugs having side effects based on the effects for each drug and the known side effect information, and the accuracy of analysis of the known effects and side effects can be improved. In addition, the effects and side effects can be analyzed by excluding drugs with analysis target effects or side effects based on the effects for each drug and known side effect information, and unknown effects and side effects can be analyzed with high accuracy.
　以下では、副作用分析データベース１１８を構成するテーブルの構造を説明する。第１実施例の副作用分析データベース１１８は、副作用症例の情報を格納する副作用症例テーブル９００（図９）と、副作用を発生する薬剤の情報を格納する副作用薬剤テーブル１０００（図１０）と、薬効の情報を格納する薬効情報テーブル１１００（図１１）と、副作用リスクの情報を格納する副作用リスクテーブル１２００（図１２）と、薬効評価の情報を格納する薬効評価テーブル１３００（図１３）とで構成される。 Hereinafter, the structure of the table constituting the side effect analysis database 118 will be described. The side effect analysis database 118 of the first embodiment includes a side effect case table 900 (FIG. 9) for storing information on side effect cases, a side effect drug table 1000 (FIG. 10) for storing information on drugs that cause side effects, A medicinal effect information table 1100 (FIG. 11) for storing information, a side effect risk table 1200 (FIG. 12) for storing side effect risk information, and a medicinal effect evaluation table 1300 (FIG. 13) for storing medicinal effect information. The
　図９は、副作用症例テーブル９００の構成例を示す図である。 FIG. 9 is a diagram showing a configuration example of the side effect case table 900.
　副作用症例テーブル９００は、副作用症例抽出部１１０によって抽出された副作用症例の情報を格納するテーブルであり、患者ＩＤ９０１、副作用詳細９０２、副作用回数９０３、副作用開始日９０４、副作用終了日９０５、改善期間開始日９０６及び改善期間終了日９０７のフィールドを含む。 The side effect case table 900 is a table for storing information on side effect cases extracted by the side effect case extraction unit 110. The patient ID 901, the side effect details 902, the side effect count 903, the side effect start date 904, the side effect end date 905, and the improvement period start It includes fields for date 906 and improvement period end date 907.
　患者ＩＤ９０１は、患者を一意に識別するための識別情報である。副作用詳細９０２は、副作用の詳細な情報である。副作用回数９０３は、発生した副作用を識別するための番号である。なお、患者ＩＤ９０１及び副作用詳細９０２が同じ副作用のレコードのうち、副作用回数９０３の最大値が当該患者における当該副作用が発生した回数となる。副作用開始日９０４及び副作用終了日９０５は、それぞれ、当該副作用が開始した日及び終了した日であり、検査値が異常と判定された最初の日及び検査値が正常と判定された最初の日である。改善期間開始日９０６及び改善期間終了日９０７は、当該副作用からの改善期間の開始日及び終了日である。 The patient ID 901 is identification information for uniquely identifying a patient. The side effect details 902 are detailed information on side effects. The number of side effects 903 is a number for identifying a side effect that has occurred. Of the records of side effects with the same patient ID 901 and side effect details 902, the maximum value of the number of side effects 903 is the number of times the side effect has occurred in the patient. The side effect start date 904 and the side effect end date 905 are the start date and the end date of the side effect, respectively, the first day when the test value is determined to be abnormal and the first date when the test value is determined to be normal. is there. The improvement period start date 906 and the improvement period end date 907 are the start date and end date of the improvement period from the side effect.
　例えば、図９に示す副作用症例テーブル９００では、患者ＩＤが「＃＃＃１」の患者は、ＡＬＴ（ＧＰＴ）の増加の副作用を２回発生しており、１回目の副作用開始日は２０１４年４月７日、副作用終了日は２０１４年４月２５日、改善期間開始日は２０１４年４月１５日、改善期間終了日は２０１４年４月２５日である。また、２回目の副作用開始日は２０１４年５月１日、副作用終了日は２０１４年５月１０日、改善期間開始日及び改善期間終了日は「０」（つまり、改善期間はなし）であることを示す。 For example, in the side effect case table 900 shown in FIG. 9, the patient whose patient ID is “## 1” has experienced ALT (GPT) increase side effects twice, and the first side effect start date is 2014. On April 7, the side effect end date is April 25, 2014, the improvement period start date is April 15, 2014, and the improvement period end date is April 25, 2014. The second side effect start date is May 1, 2014, the side effect end date is May 10, 2014, and the improvement period start date and improvement period end date is “0” (that is, there is no improvement period). Indicates.
　図１０は、副作用薬剤テーブル１０００の構成例を示す図である。 FIG. 10 is a diagram showing a configuration example of the side effect medicine table 1000.
　副作用薬剤テーブル１０００は、副作用原因情報算出部１１１によって算出された副作用を発生する薬剤の情報を格納するテーブルであり、患者ＩＤ１００１、副作用１００２、副作用詳細１００３、副作用回数１００４、薬剤名１００５、副作用原因薬剤情報１００６、ＤＤＳＡ１００７及びＤＤＳＥ１００８のフィールドを含む。患者ＩＤ１００１は、患者を一意に識別するための識別情報である。副作用１００２は、副作用の情報（例えば、名称）である。副作用詳細１００３は、当該副作用の詳細な情報である。副作用回数１００４は、当該副作用が発生した回数である。薬剤名１００５は、副作用分析の対象となる薬剤の名称である。副作用原因薬剤情報１００６は、副作用原因薬剤候補であるかを表すフラグである。ＤＤＳＡ１００７は、処方開始日から副作用開始日までの日数の差である。ＤＤＳＥ１００８は、処方終了日から副作用開始日までの日数の差である。 The side effect drug table 1000 is a table for storing information on drugs that cause side effects calculated by the side effect cause information calculation unit 111. The patient ID 1001, side effects 1002, side effect details 1003, the number of side effects 1004, drug names 1005, side effect causes It includes fields for drug information 1006, DDSA 1007 and DDSE 1008. The patient ID 1001 is identification information for uniquely identifying a patient. The side effect 1002 is side effect information (for example, name). The side effect detail 1003 is detailed information on the side effect. The number of side effects 1004 is the number of times the side effects have occurred. The drug name 1005 is the name of the drug to be subjected to side effect analysis. The side effect cause drug information 1006 is a flag indicating whether the drug is a side effect cause drug candidate. DDSA1007 is the difference in the number of days from the prescription start date to the side effect start date. DDSE1008 is the difference in the number of days from the prescription end date to the side effect start date.
　また、副作用薬剤テーブル１０００は、処方開始日から副作用開始日までの間に処方された合計日数や処方の合計用量などを記録してもよい（図示省略）。 Further, the side effect medicine table 1000 may record the total number of days prescribed between the prescription start date and the side effect start date, the total prescription dose, and the like (not shown).
　例えば、図１０に示す副作用薬剤テーブル１０００では、患者ＩＤが「＃＃＃１」の患者は、副作用が「肝機能障害」、副作用詳細が「ＡＬＴ（ＧＰＴ）の増加」について、薬剤Ｂの副作用原因薬剤情報１００６が「１」であることから、薬剤Ａは副作用の原因薬剤候補であり、ＤＤＳＡが７日、ＤＤＳＥが０日であることを示す。また、薬剤Ｂの副作用原因薬剤情報１００６が「０」であり、ＤＤＳＡが「ＮＵＬＬ」、ＤＤＳＥが「ＮＵＬＬ」であることから、薬剤Ｂは副作用の原因薬剤候補ではないことを示す。 For example, in the side effect drug table 1000 shown in FIG. 10, the patient whose patient ID is “## 1” has the side effect of “liver dysfunction” and the side effect details of “ALT (GPT) increase”. Since the causal drug information 1006 is “1”, it indicates that drug A is a candidate drug for a side effect, DDSA is 7 days, and DDSE is 0 days. Further, since the drug B side-effect-causing drug information 1006 is “0”, DDSA is “NULL”, and DDSE is “NULL”, it indicates that drug B is not a candidate drug for causing a side effect.
　図１１は、薬効情報テーブル１１００の構成例を示す図である。 FIG. 11 is a diagram showing a configuration example of the medicinal effect information table 1100.
　薬効情報テーブル１１００は、薬効情報算出部１１２によって算出された薬効の情報を格納するテーブルであり、患者ＩＤ１１０１、薬剤名１１０２、効果１１０３、総処方日数１１０４及び改善度１１０５のフィールドを含む。患者ＩＤ１１０１は、患者を一意に識別するための識別情報である。薬剤名１１０２は、薬剤の名称である。効果１１０３は、当該薬剤の効果である。総処方日数１１０４は、当該薬剤が処方された日数である。改善度１１０５は、当該薬剤の処方による検査値の変化量であり、薬効情報算出処理（図１８参照）で算出された値が格納される。薬効情報テーブル１１００は、処方の合計用量などを記録してもよい（図示省略）。 The medicinal effect information table 1100 is a table for storing medicinal effect information calculated by the medicinal effect information calculating unit 112, and includes fields of a patient ID 1101, a drug name 1102, an effect 1103, a total prescription date 1104, and an improvement degree 1105. The patient ID 1101 is identification information for uniquely identifying a patient. The drug name 1102 is the name of the drug. The effect 1103 is an effect of the drug. The total number of prescription days 1104 is the number of days on which the drug is prescribed. The degree of improvement 1105 is the amount of change in the test value due to the prescription of the drug, and stores the value calculated in the medicinal effect information calculation process (see FIG. 18). The medicinal effect information table 1100 may record the total dose of the prescription (not shown).
　例えば、図１１に示す薬効情報テーブル１１００では、患者ＩＤが「＃＃＃１」の患者には、薬剤Ａが合計１４日処方され、Ｃｒｅが１．９改善し、薬剤Ｃが合計４日処方され、Ｃｒｅが０．４改善することを示す。 For example, in the medicinal effect information table 1100 shown in FIG. 11, for the patient whose patient ID is “## 1”, drug A is prescribed for a total of 14 days, Cre is improved by 1.9, and drug C is prescribed for a total of 4 days. And show that Cre improves by 0.4.
　図１２は、副作用リスクテーブル１２００の構成例を示す図である。 FIG. 12 is a diagram showing a configuration example of the side effect risk table 1200.
　副作用リスクテーブル１２００は、統計解析部１１４によって算出された薬剤の副作用リスクの情報を格納するテーブルであり、副作用詳細１２０１、薬剤名１２０２、全使用数１２０３、副作用症例における使用数１２０４、副作用リスク１２０５及び危険処方日数１２０６のフィールドを含む。副作用詳細１２０１は、当該副作用の詳細な情報である。薬剤名１２０２は、当該副作用を発生させる薬剤の名称である。全使用数１２０３は、当該薬剤を使用した症例数である。副作用症例における使用数１２０４は、当該薬剤を使用して副作用が発生した症例数（副作用症例における使用数）である。副作用リスク１２０５は、当該薬剤の副作用リスクであり、副作用リスク算出処理（図１９参照）で算出された値が格納される。危険処方日数１２０６は、副作用が発生する可能性が高い処方日数であり、副作用リスク算出処理（図１９参照）で算出された値が格納される。 The side effect risk table 1200 is a table for storing information on the side effect risk of the drug calculated by the statistical analysis unit 114. The side effect details 1201, the drug name 1202, the total use number 1203, the use number 1204 in the side effect case, the side effect risk 1205 And a field of 1206 risk prescription days. The side effect detail 1201 is detailed information on the side effect. The drug name 1202 is the name of the drug that causes the side effect. The total number of uses 1203 is the number of cases using the drug. The use number 1204 in the case of a side effect is the number of cases in which a side effect has occurred using the drug (the number of use in a side effect case). The side effect risk 1205 is a side effect risk of the drug, and stores the value calculated by the side effect risk calculation process (see FIG. 19). The risk prescription days 1206 are prescription days with a high possibility of occurrence of side effects, and the values calculated in the side effect risk calculation process (see FIG. 19) are stored.
　例えば、図１２に示す副作用リスクテーブル１２００の副作用リスクレコードの例１２００Ａは、薬剤Ａの全使用数は１２００症例であり、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」が起きた症例数は１２０であるので、薬剤名「Ａ」に関するＡＬＴ（ＧＰＴ）の増加の副作用リスクは３．２であり、ＡＬＴ（ＧＰＴ）が増加する副作用が発生する可能性が高い危険処方日数は１０日であることを示す。 For example, in the side effect risk record example 1200A of the side effect risk table 1200 shown in FIG. 12, the total use number of the drug A is 1200 cases, and the side effect detail “increased ALT (GPT)” is 120 cases. Therefore, the side effect risk of an increase in ALT (GPT) related to the drug name “A” is 3.2, indicating that the risk prescription day with a high possibility of causing the side effect of increasing ALT (GPT) is 10 days. .
　図１３は、薬効評価テーブル１３００の構成例を示す図である。 FIG. 13 is a diagram showing a configuration example of the medicinal effect evaluation table 1300.
　薬効評価テーブル１３００は、統計解析部１１４によって算出された薬剤の薬効評価の情報を格納するテーブルであり、効果１３０１、薬剤名１３０２、全使用数１３０３及び薬効評価１３０４のフィールドを含む。効果１３０１は、薬剤の効果である。薬剤名１３０２は、当該効果を発生させる薬剤の名称である。全使用数１３０３は、当該薬剤を使用した症例数である。薬効評価１３０４は、当該薬剤の薬効評価であり、当該薬剤を処方による単位期間における検査値の増減や、病巣の大きさの変化などである。薬効評価１３０４には、薬効評価算出処理（図２０参照）で算出された値が格納される。 The medicinal effect evaluation table 1300 is a table for storing information on medicinal effect evaluation of the drug calculated by the statistical analysis unit 114, and includes fields of an effect 1301, a drug name 1302, a total use number 1303 and a medicinal effect evaluation 1304. The effect 1301 is a drug effect. The drug name 1302 is the name of the drug that generates the effect. The total use number 1303 is the number of cases using the drug. The medicinal effect evaluation 1304 is an evaluation of the medicinal effect of the drug, and includes an increase / decrease in a test value and a change in the size of a lesion in a unit period by prescribing the drug. In the medicinal effect evaluation 1304, the value calculated by the medicinal effect evaluation calculating process (see FIG. 20) is stored.
　例えば、図１３に示す薬効評価テーブル１３００の薬効評価レコードの例１３００Ａは、薬剤Ａの全使用数は１２００症例であり、薬剤Ａを使用した場合に、０．２１／日の効果でＣｒｅが減少することを示す。 For example, in the example 1300A of the medicinal evaluation record of the medicinal effect evaluation table 1300 shown in FIG. 13, the total number of drugs A used is 1200 cases, and when drug A is used, Cre decreases by 0.21 / day of the effect. Indicates to do.
　このように各薬剤の副作用リスク、危険処方日数及び薬効評価の情報によって、薬効及び副作用リスクを考慮した薬剤の選択を支援することができる。例えば、効果が同一の薬剤から副作用リスクが低い薬剤を選択し、薬効評価と副作用リスクが高い薬剤は危険処方日数までの期間において処方し、その後、薬効と副作用リスクが低い薬剤に切り替えて処方することができる。 Thus, selection of a drug in consideration of the drug effect and the risk of side effect can be supported by the side effect risk of each drug, the risk prescription time, and the information of the drug effect evaluation. For example, select drugs with low side effect risk from drugs with the same effect, prescribe drugs with high drug efficacy evaluation and high risk of side effects within the period up to the dangerous prescription days, and then switch to drugs with low drug efficacy and side effect risk. be able to.
　次に、第１実施例の副作用分析支援システムの動作を、フローチャートを用いて説明する。 Next, the operation of the side effect analysis support system of the first embodiment will be described using a flowchart.
　図１４は、第１実施例のデータサーバ１００が実行する分析処理のフローチャートである。 FIG. 14 is a flowchart of analysis processing executed by the data server 100 of the first embodiment.
　まず、制御部１０１は、統合データベース１１６から症例データを読み出し、メモリ１０３に記憶する（Ｓ１４０１）。症例データは、症例に関するデータであり、例えば、症例を識別する情報、症例に対応する処方薬の情報、症例に対応する検査結果の情報等を含む。制御部１０１は、ステップＳ１４０１において、統合データベース１１６に格納される全データを読み出し、メモリ１０３に記憶してもよい。 First, the control unit 101 reads out case data from the integrated database 116 and stores it in the memory 103 (S1401). The case data is data related to the case, and includes, for example, information for identifying the case, information on prescription drugs corresponding to the case, information on test results corresponding to the case, and the like. The control unit 101 may read all data stored in the integrated database 116 and store it in the memory 103 in step S1401.
　次に、制御部１０１は、知識データベース１１７から知識データを読み出し、メモリ１０３に記憶する（Ｓ１４０２）。制御部１０１は、ステップＳ１４０２において、知識データベース１１７に格納される全データを読み出し、メモリ１０３に記憶してもよい。 Next, the control unit 101 reads knowledge data from the knowledge database 117 and stores it in the memory 103 (S1402). The control unit 101 may read all data stored in the knowledge database 117 and store it in the memory 103 in step S1402.
　次に、制御部１０１は、表示画面生成部１０５を起動する。表示画面生成部１０５は、ユーザが分析及び可視化の対象とする副作用の詳細と効果と分析オプションとを選択する画面を提供し、これらのパラメータの入力を受け付ける（Ｓ１４０３）。以下、ステップＳ１４０３で選択された副作用詳細を対象副作用詳細と記載し、効果を対象効果と記載する場合がある。表示画面生成部１０５がステップＳ１４０３を実行すると、分析画面２１００（図２１）が入出力端末１３０に表示される。 Next, the control unit 101 activates the display screen generation unit 105. The display screen generation unit 105 provides a screen for the user to select details of the side effects to be analyzed and visualized, effects, and analysis options, and receives input of these parameters (S1403). Hereinafter, the side effect details selected in step S1403 may be referred to as target side effect details, and the effect may be referred to as the target effect. When the display screen generation unit 105 executes step S1403, the analysis screen 2100 (FIG. 21) is displayed on the input / output terminal 130.
　図２１は、第１実施例において、ユーザが分析及び可視化の対象とする副作用詳細と効果と分析オプションを選択するときに入出力端末１３０が表示する分析画面２１００の例を示す図である。 FIG. 21 is a diagram showing an example of an analysis screen 2100 displayed by the input / output terminal 130 when the user selects the side effect details and effects to be analyzed and visualized and the analysis option in the first embodiment.
　分析画面２１００は分析対象の副作用選択欄２１０１と、分析対象の効果選択欄２１０２と、分析オプション選択欄２１０３と、「設定の変更」ボタン２１０４と、「分析実行」ボタン２１０５と、分析結果表示項目選択欄２１０６と、分析結果一覧表示エリア２１０７と、「詳細の参照」ボタン２１０８と、統計解析選択オプション欄２１０９と、「統計解析」ボタン２１１０とを含む。 The analysis screen 2100 includes an analysis target side effect selection field 2101, an analysis target effect selection field 2102, an analysis option selection field 2103, a “change setting” button 2104, an “analysis execution” button 2105, and an analysis result display item. A selection field 2106, an analysis result list display area 2107, a “reference details” button 2108, a statistical analysis selection option field 2109, and a “statistical analysis” button 2110 are included.
　ユーザは、副作用選択欄２１０１内のチェックボックスを操作し、副作用選択欄２１０１に表示される一つ以上の副作用詳細から分析及び可視化をする副作用詳細を選択する。 The user operates the check box in the side effect selection field 2101 to select the side effect details to be analyzed and visualized from one or more side effect details displayed in the side effect selection field 2101.
　そして、ユーザは、分析対象の効果選択欄２１０２内のチェックボックスを操作し、分析対象の効果選択欄２１０２に表示される一つ以上の効果から分析及び可視化する効果を選択する。 Then, the user operates the check box in the analysis target effect selection field 2102 to select an effect to be analyzed and visualized from one or more effects displayed in the analysis target effect selection field 2102.
　さらに、ユーザは、分析オプション選択欄２１０３内のチェックボックスを選択し、分析オプション選択欄２１０３に表示される一つ以上の分析オプションから分析に使用する分析オプションを選択する。 Further, the user selects a check box in the analysis option selection field 2103, and selects an analysis option to be used for analysis from one or more analysis options displayed in the analysis option selection field 2103.
　ここで、ユーザが「設定の変更」ボタン２１０４を操作すると、表示画面生成部１０５は各種設定画面を入出力端末１３０に表示するための画面データを出力する。図２２は、第１実施例において、ユーザが各種設定を変更するときに入出力端末１３０が表示する設定画面２３００の例を示す図である。 Here, when the user operates the “change setting” button 2104, the display screen generation unit 105 outputs screen data for displaying various setting screens on the input / output terminal 130. FIG. 22 is a diagram illustrating an example of a setting screen 2300 displayed on the input / output terminal 130 when the user changes various settings in the first embodiment.
　設定画面２３００は、検査値異常閾値変更エリア２３０１と、改善期間変更エリア２４０１と、副作用後分析設定変更エリア３００１と、「保存」ボタン２３０２と、「キャンセル」ボタン２３０３とを含む。なお、副作用後分析設定変更エリア３００１は、第２実施例で使用される設定エリアであり、第１実施例では不要である。このため、第２実施例の機能を有さない副作用分析支援システムでは、副作用後分析設定変更エリア３００１は表示されないとよい。 The setting screen 2300 includes a test value abnormality threshold change area 2301, an improvement period change area 2401, a post-side effect analysis setting change area 3001, a “save” button 2302, and a “cancel” button 2303. The post-side effect analysis setting change area 3001 is a setting area used in the second embodiment, and is not necessary in the first embodiment. For this reason, in the side effect analysis support system that does not have the function of the second embodiment, the post-side effect analysis setting change area 3001 may not be displayed.
　ユーザは、検査値異常閾値変更エリア２３０１内の各種検査項目の異常値（男性）のアップダウンボタン２３０１１、及び、異常値（女性）のアップダウンボタン２３０１２を操作することで、閾値を変更する。又は、ユーザは、検査値異常閾値変更エリア２３０１に直接、設定したい副作用詳細の閾値を入力することもできる。 The user changes the threshold value by operating an up / down button 23011 for abnormal values (male) and an up / down button 23012 for abnormal values (female) in various inspection items in the inspection value abnormal threshold change area 2301. Alternatively, the user can directly input the threshold value of the side effect details to be set directly in the test value abnormality threshold value change area 2301.
　また、ユーザは、改善期間変更エリア２４０１内のドロップボタン２４０１１を操作し、ドロップダウンリスト（図示省略）に表示される複数の改善期間の算出定義から分析及び可視化に使用する改善期間の算出定義を選択する。具体的には、検査値の単調減少／増加している期間を改善期間としたり、検査値を線形モデルでフィッティングし、フィッティングした値が減少／増加している期間を改善期間とする等の様々な方法を選択することができる。 In addition, the user operates the drop button 24011 in the improvement period change area 2401 to change the calculation definition of the improvement period used for analysis and visualization from the calculation definitions of the plurality of improvement periods displayed in the drop-down list (not shown). select. Specifically, the period during which the test value monotonously decreases / increases is used as the improvement period, the test value is fitted with a linear model, and the period during which the fitted value decreases / increases is used as the improvement period. You can choose the right method.
　ユーザは、改善期間変更エリア２４０１のドロップボタン２４０１２を操作し、ドロップダウンリスト（図示省略）に表示される複数の改善期間の算出対象区間から分析及び可視化に使用する改善期間の算出対象区間を選択する。具体的には、副作用期間中の最後の単調減少／増加期間を改善期間の算出対象区間としたり、副作用期間中の全ての単調減少／増加期間を改善期間の算出対象区間とする等の様々な方式を選択することができる。 The user operates the drop button 24012 in the improvement period change area 2401 to select a calculation target section for the improvement period used for analysis and visualization from a plurality of calculation target sections for the improvement period displayed in a drop-down list (not shown). To do. Specifically, the last monotonic decrease / increase period during the side effect period is set as the calculation target section of the improvement period, and all the monotonic decrease / increase periods during the side effect period are set as the calculation period of the improvement period. A method can be selected.
　このように、複数の改善期間の定義によって、副作用期間中に検査値異常のピークが複数あるような複雑な変動を示す副作用でも、各薬剤の副作用原因薬剤情報を適切に判断することができる。 As described above, by defining a plurality of improvement periods, it is possible to appropriately determine the side effect cause drug information of each drug even for a side effect showing a complicated variation such that there are a plurality of abnormalities in the test value during the side effect period.
　ユーザは、変更した設定値を保存する場合は「保存」ボタン２３０２を操作し、保存しない場合は「キャンセル」ボタン２３０３を操作する。いずれか一方のボタンが操作された場合、設定画面２３００は終了し、分析画面２１００に戻る。 The user operates the “Save” button 2302 when saving the changed setting value, and operates the “Cancel” button 2303 when not saving. If any one of the buttons is operated, the setting screen 2300 is terminated and the analysis screen 2100 is returned to.
　分析画面２１００（図２１）において、ユーザが、「分析実行」ボタン２１０５を操作すると、制御部１０１は、副作用症例抽出部１１０を起動する。副作用症例抽出部１１０は、メモリ１０３に記憶された統合データベース１１６のデータから、ステップＳ１４０３で選択された副作用詳細の副作用を示す症例を抽出する（Ｓ１４０４）。図１５は、副作用症例抽出部１１０が副作用が発生した症例を抽出する処理のフローチャートである。 In the analysis screen 2100 (FIG. 21), when the user operates an “execute analysis” button 2105, the control unit 101 activates the side effect case extraction unit 110. The side effect case extraction unit 110 extracts a case indicating the side effect of the side effect details selected in step S1403 from the data of the integrated database 116 stored in the memory 103 (S1404). FIG. 15 is a flowchart of a process in which the side effect case extraction unit 110 extracts a case in which a side effect has occurred.
　まず、副作用症例抽出部１１０は、ステップＳ１４０３で選択された各副作用詳細に対応する各検査項目をメモリ１０３に記憶された副作用知識情報テーブル５００のデータから抽出する（Ｓ１５０１）。 First, the side effect case extraction unit 110 extracts each examination item corresponding to each side effect detail selected in step S1403 from the data of the side effect knowledge information table 500 stored in the memory 103 (S1501).
　次に、ステップＳ１５０２において、副作用症例抽出部１１０は、検査情報テーブル４００の全ての患者ＩＤのレコードを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ１５０２）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ１５０３からＳ１５０７の処理を繰り返し実行する。 Next, in step S1502, the side effect case extraction unit 110 acquires records of all patient IDs in the examination information table 400, and determines whether the processing is completed for all patient IDs (S1502). As a result, if the process has not been completed for some patient IDs, the processes of steps S1503 to S1507 are repeatedly executed for each patient ID.
　ステップＳ１５０３において、副作用症例抽出部１１０は、ステップＳ１４０３で分析オプション選択欄２１０３において「疾患情報を使用」２１０３１が選択されているかを判定する（Ｓ１５０３）。その結果、「疾患情報を使用」２１０３１が選択されていなければ、ステップＳ１５０５に進む。一方、「疾患情報を使用」２１０３１が選択されていれば、副作用症例抽出部１１０は、疾患情報に基づき分析対象患者を選定する（Ｓ１５０４）。具体的には、ステップＳ１４０３で選択された各副作用詳細が症状として格納されている疾患名を抽出し、抽出した疾患名が症例情報テーブル２００の疾患名２０７に格納されている患者ＩＤを分析対象から除外する。例えば、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」が選択されている場合、抽出される疾患名の一つとして疾患名「Ｃ型慢性肝炎」を抽出し、症例情報テーブル２００において、疾患名に「Ｃ型慢性肝炎」が格納されている患者ＩＤ「＃＃＃２」を分析対象から除外する。 In step S1503, the side effect case extraction unit 110 determines whether “use disease information” 21031 is selected in the analysis option selection field 2103 in step S1403 (S1503). As a result, if “use disease information” 21031 is not selected, the process proceeds to step S1505. On the other hand, if “use disease information” 21031 is selected, the side effect case extraction unit 110 selects a patient to be analyzed based on the disease information (S1504). Specifically, a disease name in which each side effect detail selected in step S1403 is stored as a symptom is extracted, and a patient ID whose extracted disease name is stored in a disease name 207 in the case information table 200 is analyzed. Exclude from For example, when the side effect detail “increased ALT (GPT)” is selected, the disease name “chronic hepatitis C” is extracted as one of the extracted disease names, and “ The patient ID “## 2” in which “chronic hepatitis C” is stored is excluded from the analysis target.
　次に、副作用症例抽出部１１０は、性別に対応する異常値を抽出する（Ｓ１５０５）。例えば、副作用詳細に「ＡＬＴ（ＧＰＴ）の増加」が選択されている場合、患者ＩＤが「＃＃＃１」の患者は男性であるため、異常値（男性）の「４２超過」を副作用知識情報テーブル５００から抽出する。 Next, the side effect case extraction unit 110 extracts an abnormal value corresponding to gender (S1505). For example, if “increased ALT (GPT)” is selected in the side effect details, since the patient whose patient ID is “## 1” is a male, an abnormal value (male) of “exceeding 42” is reported as side effect knowledge. Extracted from the information table 500.
　次に、副作用症例抽出部１１０は、副作用期間及び副作用回数を算出する（Ｓ１５０６）。例えば、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」が選択され、患者ＩＤが「＃＃＃１」について分析する場合、患者ＩＤが「＃＃＃１」であり、検査項目が「ＡＬＴ（ＧＰＴ）」である全ての検査値を検査情報テーブル４００から取得する。検査値が異常値（男性）「４２超過」に初めて該当した検査日を副作用開始日とし、その後、異常値に該当しなくなった検査日又は最終検査日を副作用終了日として算出する。また、この副作用期間が何回目に発生した副作用であるかを副作用回数として算出する。 Next, the side effect case extraction unit 110 calculates the side effect period and the number of side effects (S1506). For example, when the side effect detail “increased ALT (GPT)” is selected and the patient ID is analyzed for “## 1”, the patient ID is “## 1” and the examination item is “ALT (GPT)”. Are acquired from the inspection information table 400. The test date when the test value first corresponds to the abnormal value (male) “exceeding 42” is set as the side effect start date, and thereafter, the test date or the final test date that no longer corresponds to the abnormal value is calculated as the side effect end date. Also, the number of side effects occurring during this side effect period is calculated as the number of side effects.
　次に、副作用症例抽出部１１０は、患者ＩＤ、副作用詳細、副作用回数、副作用期間の開始日及び終了日を関連付けて副作用症例テーブル９００のフィールド９０１～９０５に登録する（Ｓ１５０７）。 Next, the side effect case extraction unit 110 associates the patient ID, the side effect details, the number of side effects, the start date and the end date of the side effect period, and registers them in the fields 901 to 905 of the side effect case table 900 (S1507).
　このように、副作用症例抽出処理によって、検査値と予め定義してある異常値に基づいて副作用を検知することによって、副作用症例を自動的に抽出することができる。 As described above, a side effect case can be automatically extracted by detecting a side effect based on a test value and a predefined abnormal value by the side effect case extraction process.
　ステップＳ１５０２において、全ての患者ＩＤについて処理が完了していると判定されれば、呼出元の処理に戻り、ステップＳ１４０５に進む。ステップＳ１４０５では、制御部１０１は、検査情報解釈部１０９を起動し、ステップＳ１４０４で抽出された副作用症例について、ステップＳ１４０３で選択された副作用詳細対応の改善期間を抽出する。 If it is determined in step S1502 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1405. In step S1405, the control unit 101 activates the examination information interpretation unit 109, and extracts the improvement period corresponding to the detailed side effect response selected in step S1403 for the side effect case extracted in step S1404.
　図１６は、検査情報解釈部１０９が改善期間を算出する処理のフローチャートである。 FIG. 16 is a flowchart of processing in which the examination information interpretation unit 109 calculates the improvement period.
　まず、検査情報解釈部１０９は、ステップＳ１４０４で作成された副作用症例テーブル９００をメモリ１０３に読み出す（Ｓ１６０１）。 First, the examination information interpretation unit 109 reads the side effect case table 900 created in step S1404 into the memory 103 (S1601).
　次に、検査情報解釈部１０９は、副作用症例テーブル９００の全ての患者ＩＤのレコードを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ１６０２）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ１６０３からＳ１６０７の処理を繰り返し実行する。 Next, the examination information interpretation unit 109 acquires records of all patient IDs in the side effect case table 900, and determines whether the processing is completed for all patient IDs (S1602). As a result, if the processing has not been completed for some patient IDs, the processing of steps S1603 to S1607 is repeatedly executed for each patient ID.
　ステップＳ１６０３において、検査情報解釈部１０９は、算出処理の対象の患者ＩＤの副作用回数毎にステップＳ１６０４からＳ１６０７の処理を繰り返し実行する。なお、全ての副作用回数について処理が完了していれば、ステップＳ１６０２に戻り、次の患者ＩＤについて処理をする。 In step S1603, the examination information interpretation unit 109 repeatedly executes the processes of steps S1604 to S1607 for each number of side effects of the patient ID to be calculated. If processing has been completed for all the side effects, the process returns to step S1602 to process the next patient ID.
　ステップＳ１６０４において、検査情報解釈部１０９は、改善期間の対象とする患者ＩＤと副作用詳細と副作用回数の副作用期間の開始日から終了日までの全ての検査値を検査情報テーブル４００から取得する。 In step S1604, the test information interpretation unit 109 acquires from the test information table 400 all the test values from the start date to the end date of the side effect period of the patient ID, the side effect details, and the number of side effects as the target of the improvement period.
　次に、検査情報解釈部１０９は、検査値増減期間を抽出する（Ｓ１６０５）。例えば、改善期間の定義が検査値の単調減少／増加している期間であると設定されている場合、検査値が単調減少又は単調増加している期間を算出する。 Next, the inspection information interpretation unit 109 extracts the inspection value increase / decrease period (S1605). For example, when the definition of the improvement period is set to be a period in which the inspection value is monotonously decreasing / increasing, the period in which the inspection value is monotonously decreasing or monotonously increasing is calculated.
　次に、検査情報解釈部１０９は、改善期間を算出する（Ｓ１６０６）。例えば、改善期間の算出対象区間が副作用期間中の最後の単調減少／増加期間であると設定されている場合、ステップＳ１６０５で抽出した副作用期間中の最後の単調減少期間又は単調増加期間の開始日を改善期間開始日、終了日を改善期間終了日とする。 Next, the examination information interpretation unit 109 calculates an improvement period (S1606). For example, when the calculation target section of the improvement period is set to be the last monotonic decrease / increase period during the side effect period, the start date of the last monotone decrease period or the monotone increase period during the side effect period extracted in step S1605 Is the improvement period start date and the end date is the improvement period end date.
　例えば、図２３に示すように、副作用を示す検査値が単調増加した後に単調減少する（検査値が一つのピークを示す）場合、単調増加している期間１と単調減少している期間２とを算出する。そして、検査値が単調減少している期間２が最後の期間となり、当該期間２の開始日及び終了日が、それぞれ改善期間開始日及び改善期間終了日となる。 For example, as shown in FIG. 23, when a test value indicating a side effect monotonically increases and then monotonously decreases (test value indicates one peak), a monotonically increasing period 1 and a monotonically decreasing period 2 Is calculated. Then, the period 2 in which the test value monotonously decreases is the last period, and the start date and the end date of the period 2 are the improvement period start date and the improvement period end date, respectively.
　また、副作用を示す検査値が複数の（例えば、三つの）ピークを示す場合、単調増加している期間１１と、単調減少している期間１２と、その後単調増加している期間１３と、単調減少している期間１４と、さらに単調増加している期間１５と、単調減少している期間１６とを算出する。そして、検査値が単調減少している期間１６が最後の期間となり、当該期間１６の開始日及び終了日が、それぞれ改善期間開始日及び改善期間終了日となる。 Further, when the test value indicating the side effect shows a plurality of (for example, three) peaks, the period 11 is monotonically increasing, the period 12 is monotonically decreasing, the period 13 is monotonically increasing thereafter, and the monotonic A decreasing period 14, a further monotonically increasing period 15, and a monotonically decreasing period 16 are calculated. Then, the period 16 in which the test value monotonously decreases is the last period, and the start date and the end date of the period 16 are the improvement period start date and the improvement period end date, respectively.
　次に、検査情報解釈部１０９は、改善期間開始日及び改善期間終了を副作用症例テーブル９００のフィールド９０６及び９０７に登録する（Ｓ１６０７）。 Next, the examination information interpretation unit 109 registers the improvement period start date and the improvement period end in the fields 906 and 907 of the side effect case table 900 (S1607).
　このように、検査値と予め設定した改善期間の定義を用いて検査値増減期間を抽出することによって、改善期間を効率的に算出することができる。 Thus, the improvement period can be efficiently calculated by extracting the inspection value increase / decrease period using the inspection value and the definition of the improvement period set in advance.
　ステップＳ１６０２において、全ての患者ＩＤについて処理が完了していると判定されれば、呼出元の処理に戻り、ステップＳ１４０６に進む。ステップＳ１４０６では、制御部１０１は、副作用原因情報算出部１１１を起動し、ステップＳ１４０４で抽出された副作用症例について、各処方薬の副作用原因薬剤情報１００６を算出する。 If it is determined in step S1602 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1406. In step S1406, the control unit 101 activates the side effect cause information calculation unit 111 and calculates side effect cause drug information 1006 of each prescription drug for the side effect case extracted in step S1404.
　図１７に、副作用原因情報算出部１１１が副作用の原因を算出する処理のフローチャートである。 FIG. 17 is a flowchart of processing in which the side effect cause information calculation unit 111 calculates the cause of the side effect.
　まず、副作用原因情報算出部１１１は、ステップＳ１４０４とステップＳ１４０５で作成された副作用症例テーブル９００をメモリ１０３に読み出す（Ｓ１７０１）。 First, the side effect cause information calculation unit 111 reads the side effect case table 900 created in steps S1404 and S1405 to the memory 103 (S1701).
　次に、ステップＳ１７０２において、副作用原因情報算出部１１１は、副作用症例テーブル９００の全ての患者ＩＤのレコードを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ１７０２）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ１７０３からＳ１７１０の処理を繰り返し実行する。 Next, in step S1702, the side effect cause information calculation unit 111 acquires records of all patient IDs in the side effect case table 900, and determines whether the processing is completed for all patient IDs (S1702). As a result, if the processing has not been completed for some patient IDs, the processing of steps S1703 to S1710 is repeatedly executed for each patient ID.
　ステップＳ１７０３において、副作用原因情報算出部１１１は、副作用症例テーブル９００の全ての副作用詳細について、副作用回数毎にステップＳ１７０４からＳ１７１０の処理を繰り返し実行する。なお、全ての副作用回数について処理が完了していれば、ステップＳ１７０２に戻り、次の患者ＩＤについて処理をする。 In step S1703, the side effect cause information calculation unit 111 repeatedly executes the processing of steps S1704 to S1710 for each side effect count for all side effect details in the side effect case table 900. If processing has been completed for all side effects, the process returns to step S1702 to process the next patient ID.
　ステップＳ１７０４において、副作用原因情報算出部１１１は、副作用原因薬剤情報を算出する対象の処方薬を抽出する対象の期間を示す抽出対象日数を取得する。具体的には、分析の対象とする副作用詳細に対応する抽出対象日数を設定情報テーブル６００のフィールド６０２から取得する。例えば、副作用詳細「ＡＬＴ（ＧＰＴ）の増加」について副作用原因薬剤情報を算出する場合、抽出対象日数として「３０」を取得する。すなわち、副作用開始日以前の３０日間を薬剤の抽出期間として取得する。 In step S1704, the side effect cause information calculation unit 111 acquires the extraction target days indicating the target period for extracting the prescription drug for which the side effect cause drug information is calculated. Specifically, the number of extraction target days corresponding to the side effect details to be analyzed is acquired from the field 602 of the setting information table 600. For example, when calculating the side effect-causing drug information for the side effect details “increase in ALT (GPT)”, “30” is acquired as the extraction target days. That is, 30 days before the side effect start date are acquired as the drug extraction period.
　次に、副作用原因情報算出部１１１は、副作用開始日以前の抽出対象日数の間に処方された全ての薬剤を抽出し、副作用薬剤テーブル１０００に登録する（Ｓ１７０５）。具体的には、副作用原因薬剤情報を算出する症例に処方された薬剤のうち、抽出対象日数に処方された全ての薬剤を処方情報テーブル３００から抽出する。例えば、患者ＩＤが「＃＃＃１」の症例において、分析対象の副作用詳細が「ＡＬＴ（ＧＰＴ）の増加」である場合、抽出対象日数（副作用開始日である２０１４年４月７日以前の３０日間）に処方された薬剤であるため、薬剤Ａ及び薬剤Ｂが抽出され、副作用薬剤テーブル１０００に登録される。 Next, the side effect cause information calculating unit 111 extracts all drugs prescribed during the extraction target days before the side effect start date, and registers them in the side effect drug table 1000 (S1705). Specifically, all the drugs prescribed for the number of extraction target days are extracted from the prescription information table 300 among the drugs prescribed for cases for which side effect cause drug information is calculated. For example, in the case where the patient ID is “## 1”, when the side effect details of the analysis target is “ALT (GPT) increase”, the number of extraction target days (before the side effect start date April 7, 2014) 30 days), the drugs A and B are extracted and registered in the side effect drug table 1000.
　次に、副作用原因情報算出部１１１は、ステップＳ１７０５で抽出した薬剤のうち、改善期間の開始後も継続して処方された薬剤を除外する（Ｓ１７０６）。例えば、患者ＩＤが「＃＃＃１」の症例において、分析対象の副作用詳細が「ＡＬＴ（ＧＰＴ）の増加」である場合、ステップＳ１７０５で抽出された薬剤（薬剤Ａ、薬剤Ｂ）のうち、薬剤Ｂは改善期間開始後も継続して処方されているため、薬剤Ｂが除外される。 Next, the side effect cause information calculation unit 111 excludes medicines that are continuously prescribed from the medicine extracted in step S1705 even after the start of the improvement period (S1706). For example, in the case where the patient ID is “## 1”, when the side effect detail of the analysis target is “increase in ALT (GPT)”, among the drugs (drug A, drug B) extracted in step S1705, Since drug B is continuously prescribed after the start of the improvement period, drug B is excluded.
　なお、改善期間の前後の両方において処方されているが、断続して処方されている薬剤は、継続して処方されているものとして取り扱うとよい。 In addition, although it is prescribed both before and after the improvement period, it is better to treat the medicine prescribed intermittently as being continuously prescribed.
　次に、ステップＳ１４０３で分析オプション選択欄２１０３において「薬剤情報を使用（副作用）」２１０３２が選択されているかを判定する（Ｓ１７０７）。その結果、「薬剤情報を使用（副作用）」２１０３２が選択されていなければ、ステップＳ１７０９に進む。一方、「薬剤情報を使用（副作用）」２１０３２が選択されていれば、副作用原因情報算出部１１１は、副作用既知情報に基づいて薬剤を除外する（Ｓ１７０８）。具体的には、分析対象の副作用詳細が薬剤情報テーブル８００のフィールド８０３に格納されている薬剤のみを副作用原因薬剤情報の算出対象とし、フィールド８０３に格納されていない薬剤を算出対象から除外する。 Next, in step S1403, it is determined whether or not “use drug information (side effect)” 21032 is selected in the analysis option selection field 2103 (S1707). As a result, if “use drug information (side effect)” 21032 is not selected, the process advances to step S1709. On the other hand, if “use drug information (side effect)” 21032 is selected, the side effect cause information calculation unit 111 excludes the drug based on the side effect known information (S1708). Specifically, only the drugs whose details of side effects to be analyzed are stored in the field 803 of the drug information table 800 are set as the calculation targets of the side effect cause drug information, and the drugs not stored in the field 803 are excluded from the calculation targets.
　次に、副作用原因情報算出部１１１は、ステップＳ１７０５で抽出した薬剤のうち、ステップＳ１７０８で除外されなかった薬剤について副作用薬剤テーブル１０００の副作用原因薬剤情報１００６に「１」を登録する（Ｓ１７０９）。 Next, the side effect cause information calculation unit 111 registers “1” in the side effect cause drug information 1006 of the side effect drug table 1000 for the drugs extracted in step S1705 and not excluded in step S1708 (S1709).
　次に、副作用原因情報算出部１１１は、ステップＳ１７０５で抽出した薬剤のうち、ステップＳ１７０８で除外されなかった薬剤の処方開始日と副作用開始日との差（ＤＤＳＡ）を数式（１）を用いて算出し、処方終了日と副作用開始日との差（ＤＤＳＥ）を数式（２）を用いて算出する（Ｓ１７１０）。 Next, the side effect cause information calculation unit 111 uses Formula (1) to calculate the difference (DDSA) between the prescription start date and the side effect start date of the drug that was not excluded in Step S1708 among the drugs extracted in Step S1705. The difference (DDSE) between the prescription end date and the side effect start date is calculated using Equation (2) (S1710).
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
　次に、副作用原因情報算出部１１１は、ステップＳ１７１０で算出したＤＤＳＡ及びＤＤＳＥを副作用薬剤テーブル１０００のフィールド１００７及び１００８に、それぞれ登録する（Ｓ１７１１）。 Next, the side effect cause information calculation unit 111 registers the DDSA and DDSE calculated in step S1710 in the fields 1007 and 1008 of the side effect drug table 1000, respectively (S1711).
　このように、改善期間前後の処方の継続性を用いて副作用原因薬剤情報を算出することによって、副作用と関係ない薬剤を副作用原因薬剤候補から除外することが可能となり、効率的に副作用を分析することができる。 In this way, by calculating side effect-causing drug information using the continuity of prescriptions before and after the improvement period, it is possible to exclude drugs that are not related to side effects from drug candidates that cause side effects, and efficiently analyze side effects be able to.
　ステップＳ１７０２において、全ての患者ＩＤについて処理が完了していると判定されれば、呼出元の処理に戻り、ステップＳ１４０７に進む。ステップＳ１４０７では、制御部１０１は、薬効情報算出部１１２を起動し、各処方薬の薬効情報を算出する。 If it is determined in step S1702 that the process has been completed for all patient IDs, the process returns to the caller process and proceeds to step S1407. In step S1407, the control unit 101 activates the medicinal effect information calculating unit 112 and calculates medicinal effect information of each prescription drug.
　図１８は、薬効情報算出部１１２が各処方薬の薬効情報を算出する処理のフローチャートである。 FIG. 18 is a flowchart of a process in which the medicinal effect information calculation unit 112 calculates medicinal effect information of each prescription drug.
　まず、薬効情報算出部１１２は、ステップＳ１４０３で分析対象の効果選択欄２１０２で選択された全ての薬効について処理が完了しているかを判定する（Ｓ１８０１）。その結果、一部の薬効について処理が完了していなければ、薬効毎にステップＳ１８０２からＳ１８０９の処理を繰り返し実行する。 First, the medicinal effect information calculation unit 112 determines whether or not the processing has been completed for all medicinal effects selected in the effect selection column 2102 to be analyzed in step S1403 (S1801). As a result, if the process has not been completed for some of the medicinal effects, the processes of steps S1802 to S1809 are repeatedly executed for each medicinal effect.
　ステップＳ１８０２において、薬効情報算出部１１２は、症例情報テーブル２００の全ての患者ＩＤを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ１８０２）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ１８０３からＳ１８０９の処理を繰り返し実行する。なお、全ての患者ＩＤについて処理が完了していれば、ステップＳ１８０１に戻り、次の薬効について処理をする。 In step S1802, the medicinal effect information calculation unit 112 acquires all the patient IDs in the case information table 200, and determines whether the processing is completed for all the patient IDs (S1802). As a result, if the process has not been completed for some patient IDs, the processes in steps S1803 to S1809 are repeatedly executed for each patient ID. If processing has been completed for all patient IDs, the process returns to step S1801 to process the next medicinal effect.
　ステップＳ１８０３において、薬効情報算出部１１２は、ステップＳ１４０３で選択された薬効に対応する検査項目の検査値の増減期間を抽出する（Ｓ１８０３）。例えば、改善期間の定義が検査値の単調減少／増加している期間と設定されている場合、検査値が単調減少又は単調増加している期間を算出する。薬効に対応する検査項目を選択する際は、薬効と対応する検査項目を予め格納したテーブルを使用する（図示省略）。又は、副作用知識情報テーブル５００の副作用詳細５０２と検査項目５０３との対応関係を使用してもよい。 In step S1803, the medicinal effect information calculation unit 112 extracts the increase / decrease period of the test value of the test item corresponding to the medicinal effect selected in step S1403 (S1803). For example, when the definition of the improvement period is set as a period in which the inspection value monotonously decreases / increases, the period in which the inspection value monotonously decreases or monotonously increases is calculated. When selecting an inspection item corresponding to the medicinal effect, a table in which the inspection items corresponding to the medicinal effect are stored in advance is used (not shown). Alternatively, the correspondence between the side effect details 502 and the test item 503 in the side effect knowledge information table 500 may be used.
　次に、薬効情報算出部１１２は、改善期間を算出する（Ｓ１８０４）。例えば、ステップＳ１８０３で抽出した検査値増減期間の最後の単調減少／増加期間の開始日及び終了日を、それぞれ、改善期間開始日及び改善期間終了日とする。 Next, the medicinal effect information calculation unit 112 calculates an improvement period (S1804). For example, the start date and end date of the last monotonous decrease / increase period of the test value increase / decrease period extracted in step S1803 are set as the improvement period start date and the improvement period end date, respectively.
　次に、薬効情報算出部１１２は、改善期間の終了日より前に処方された薬剤の情報を処方情報テーブル３００から取得する（Ｓ１８０５）。このとき、改善期間の終了日前６０日以内に処方された薬剤のみを取得するなど、予め設定した抽出定義に従って薬剤を取得してもよい。例えば、患者ＩＤが「＃＃＃１」の症例において、分析対象の薬効の改善期間終了日が２０１４年４月１８日である場合、薬剤Ａと薬剤Ｂと薬剤Ｃが取得される。 Next, the medicinal effect information calculation unit 112 acquires information on the medicine prescribed before the end date of the improvement period from the prescription information table 300 (S1805). At this time, the drug may be acquired according to a preset extraction definition, such as acquiring only the drug prescribed within 60 days before the end date of the improvement period. For example, in the case where the patient ID is “## 1”, when the medicinal effect improvement period end date to be analyzed is April 18, 2014, the medicine A, the medicine B, and the medicine C are acquired.
　次に、ステップＳ１４０３で分析オプション選択欄２１０３において「薬剤情報を使用（効果）」２１０３３が選択されているかを判定する（Ｓ１８０６）。その結果、「薬剤情報を使用（効果）」２１０３３が選択されていなければ、ステップＳ１７０９に進む。一方、「薬剤情報を使用（副作用）」２１０３２が選択されていれば、薬効情報算出部１１２は、薬効情報に基づく薬剤を除外する（Ｓ１８０７）。具体的には、分析対象の薬効が薬剤情報テーブル８００のフィールド８０２に格納されている薬剤のみを薬効情報の算出対象とし、フィールド８０２に格納されていない薬剤を除外する。例えば、患者ＩＤが「＃＃＃１」の症例において、ステップＳ１８０５において取得された薬剤は薬剤Ａと薬剤Ｂと薬剤Ｃである場合、薬剤Ｂが除外され、薬剤Ａと薬剤Ｃが薬効情報の算出対象となる。 Next, in step S1403, it is determined whether or not “use drug information (effect)” 21033 is selected in the analysis option selection field 2103 (S1806). As a result, if “use drug information (effect)” 21033 is not selected, the process advances to step S1709. On the other hand, if “use drug information (side effects)” 21032 is selected, the drug effect information calculation unit 112 excludes drugs based on drug effect information (S1807). Specifically, only drugs whose medicinal effects to be analyzed are stored in the field 802 of the drug information table 800 are calculated, and drugs that are not stored in the field 802 are excluded. For example, in the case where the patient ID is “## 1”, if the drugs acquired in step S1805 are drug A, drug B, and drug C, drug B is excluded, and drug A and drug C are included in the drug efficacy information. It is a calculation target.
　次に、薬効情報算出部１１２は、ステップＳ１８０５で取得した薬剤のうち、ステップＳ１８０７で除外されなかった薬剤について、総処方日数を算出する（Ｓ１８０８）。例えば、患者ＩＤが「＃＃＃１」の症例において、ステップＳ１８０７において薬剤Ａと薬剤Ｃが薬効情報の算出対象であるとされた場合、薬剤Ａの総処方日数は１４日、薬剤Ｂの総処方日数は４日であると算出される。 Next, the medicinal effect information calculation unit 112 calculates the total number of prescription days for the drugs not acquired in step S1807 among the drugs acquired in step S1805 (S1808). For example, in the case where the patient ID is “## 1”, if it is determined in step S1807 that the drug A and the drug C are the targets for calculating the drug efficacy information, the total prescription days for the drug A are 14 days, The prescription days are calculated to be 4 days.
　次に、薬効情報算出部１１２は、ステップＳ１８０８で総処方日数を算出した薬剤の改善度を算出する（Ｓ１８０９）。具体的には、ステップＳ１８０８で総処方日数を算出した薬剤の処方後に、対象とする薬効の処方による検査値の変化量を改善度として算出する。例えば、患者ＩＤが「＃＃＃１」の症例において、ステップＳ１８０８において総処方日数を算出した薬剤が薬剤Ａ及び薬剤Ｃであり、薬剤Ａの処方後に、対象とする薬効に対応する検査値（Ｃｒｅ）が１．９改善した場合、薬剤Ａの改善度は１．９となる。また、薬剤Ｃの処方後に、対象とする薬効に対応する検査値（Ｃｒｅ）が０．４改善した場合、薬剤Ｃの改善度は０．４となる。 Next, the medicinal effect information calculation unit 112 calculates the improvement degree of the drug for which the total number of prescription days has been calculated in step S1808 (S1809). Specifically, after the prescription of the medicine whose total prescription days have been calculated in step S1808, the amount of change in the test value due to the prescription for the target medicinal effect is calculated as the improvement degree. For example, in the case where the patient ID is “## 1”, the drugs whose total prescription days are calculated in step S1808 are the drug A and the drug C, and after the drug A is prescribed, the test value corresponding to the target drug effect ( When Cre) is improved by 1.9, the improvement degree of drug A is 1.9. In addition, when the test value (Cre) corresponding to the target drug effect is improved by 0.4 after the prescription of the drug C, the improvement degree of the drug C is 0.4.
　次に、薬効情報算出部１１２は、算出された改善度を、薬剤名と関連付けて、薬効情報テーブル１１００の薬効評価１３０４に登録する（Ｓ１８１０）。 Next, the medicinal effect information calculation unit 112 registers the calculated degree of improvement in the medicinal effect evaluation 1304 of the medicinal effect information table 1100 in association with the drug name (S1810).
　このように、検査値増減期間から改善期間を算出し、改善期間を用いて薬効情報を算出する薬剤を抽出することによって、薬効を効率的に分析できる。さらに、薬剤情報を用いて、薬効を分析する薬剤を絞り込むことによって、薬効と関係のない薬剤を除外することが可能となり、薬効を効率的に分析できる。 Thus, by calculating the improvement period from the test value increase / decrease period and extracting the drug for calculating the drug effect information using the improvement period, the drug efficacy can be efficiently analyzed. Furthermore, by narrowing down the drugs whose drug efficacy is to be analyzed using the drug information, it is possible to exclude drugs that are not related to the drug efficacy, and the drug efficacy can be analyzed efficiently.
　次に、制御部１０１は、表示画面生成部１０５を起動する。表示画面生成部１０５は、ステップＳ１４０４で算出された副作用症例と、ステップＳ１４０６で算出された副作用原因薬剤情報と、ステップＳ１４０７で算出された効果情報とに基づいて、全ての分析処理の結果を分析結果一覧表示エリア２１０７に出力する画面を生成し（Ｓ１４０８）、生成した画面データを出力する（Ｓ１４０９）。 Next, the control unit 101 activates the display screen generation unit 105. The display screen generation unit 105 analyzes the results of all the analysis processes based on the side effect case calculated in step S1404, the side effect cause drug information calculated in step S1406, and the effect information calculated in step S1407. A screen to be output to the result list display area 2107 is generated (S1408), and the generated screen data is output (S1409).
　図２１に示す分析画面２１００は、図１４に示す分析処理の結果を表示する際にも使用される。 21 is also used when displaying the result of the analysis process shown in FIG.
　図２１に示す分析画面２１００は、分析結果表示項目選択欄２１０６を用いて、分析結果一覧表示エリア２１０７に出力する分析結果を副作用に関する情報に絞り込んだ後に表示される画面例である。分析結果一覧表示エリア２１０７には、副作用症例の患者ＩＤと、各症例における副作用原因薬剤情報に「１」が登録された薬剤が副作用原因薬剤候補として表示される。 21 is an example of a screen that is displayed after the analysis result output to the analysis result list display area 2107 is narrowed down to information on side effects using the analysis result display item selection field 2106. The analysis screen 2100 shown in FIG. In the analysis result list display area 2107, a patient ID of a side effect case and a drug with “1” registered in the side effect cause drug information in each case are displayed as a side effect cause drug candidate.
　そして、ユーザが、副作用症例のうち詳細に分析したい症例を選択し、「詳細の参照」ボタン２１０８を操作すると、表示画面生成部１０５は症例分析画面２５００を入出力端末１３０に表示するための画面データを出力する。 When the user selects a case to be analyzed in detail from the side effect cases and operates the “reference details” button 2108, the display screen generation unit 105 displays a screen for displaying the case analysis screen 2500 on the input / output terminal 130. Output data.
　図２３は、第１実施例において、ユーザが症例毎の詳細な分析結果の参照するときに入出力端末１３０が表示する症例分析画面２５００の例を示す図である。 FIG. 23 is a diagram illustrating an example of a case analysis screen 2500 displayed on the input / output terminal 130 when the user refers to a detailed analysis result for each case in the first embodiment.
　症例分析画面２５００は、副作用選択欄２５０１と、副作用詳細選択欄２５０２と、患者ＩＤ選択欄２５０４と、「患者一覧に戻る」ボタン２５０５と、検査結果表示エリア２５０６と、処方薬剤表示エリア２５０７と、副作用原因薬剤候補表示エリア２５０８とを含む。 The case analysis screen 2500 includes a side effect selection field 2501, a side effect detail selection field 2502, a patient ID selection field 2504, a “return to patient list” button 2505, a test result display area 2506, a prescription drug display area 2507, And a side effect cause drug candidate display area 2508.
　ユーザは、副作用選択欄２５０１のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用から分析及び可視化をする副作用を選択する。ユーザは、副作用選択欄２５０１に、副作用を直接入力することができてもよい。 The user operates a drop-down button in the side-effect selection column 2501 to select a side-effect to be analyzed and visualized from a plurality of side-effects displayed in a drop-down list (not shown). The user may be able to directly input a side effect in the side effect selection field 2501.
　ユーザは、副作用詳細選択欄２５０２のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用詳細から分析及び可視化をする副作用詳細を選択する。ユーザは、副作用詳細選択欄２５０２に、副作用詳細を直接入力することができてもよい。 The user operates the drop-down button in the side-effect detail selection column 2502, and selects side-effect details to be analyzed and visualized from a plurality of side-effect details displayed in a drop-down list (not shown). The user may be able to directly input the side effect details in the side effect detail selection field 2502.
　ユーザは、患者ＩＤ選択欄２５０４のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の患者ＩＤから分析及び可視化をする患者ＩＤを選択する。ユーザは、患者ＩＤ選択欄２５０４に、患者ＩＤを直接入力することができてもよい。 The user operates a drop-down button in the patient ID selection field 2504 to select a patient ID to be analyzed and visualized from a plurality of patient IDs displayed in a drop-down list (not shown). The user may be able to directly enter the patient ID in the patient ID selection field 2504.
　ユーザは、症例毎の詳細な分析を終了する場合、「患者一覧に戻る」ボタン２５０５を操作することによって、症例分析画面２５００を終了し、分析画面２１００を表示する。 When ending the detailed analysis for each case, the user operates the “return to patient list” button 2505 to end the case analysis screen 2500 and display the analysis screen 2100.
　検査結果表示エリア２５０６には、副作用詳細選択欄２５０２で選択された副作用詳細に対応する検査項目の検査値の推移を示すグラフ２５０６２と、当該検査値が異常かを判定するための閾値を示す線２５０６３と、ステップＳ１４０３で選択された薬効に対応する検査項目の検査値の推移を示すグラフ２５０６１とが表示される。また、処方薬剤表示エリア２５０７には、患者ＩＤ選択欄２５０４で選択された症例に処方された薬剤の処方開始日と処方終了日の時系列情報が表示される。また、副作用原因薬剤候補表示エリア２５０８には、処方薬剤表示エリア２５０７に表示される薬剤のうち、副作用詳細選択欄２５０２で副作用詳細に対応する副作用原因薬剤情報に「１」が格納されている薬剤が表示される。 In the test result display area 2506, a graph 25062 showing the transition of the test value of the test item corresponding to the side effect details selected in the side effect detail selection column 2502, and a line indicating a threshold value for determining whether the test value is abnormal 25063 and a graph 25061 showing the transition of the test value of the test item corresponding to the medicinal effect selected in step S1403 are displayed. The prescription drug display area 2507 displays time series information of the prescription start date and prescription end date of the drug prescribed for the case selected in the patient ID selection field 2504. In the side effect cause drug candidate display area 2508, among the drugs displayed in the prescription drug display area 2507, the drug for which “1” is stored in the side effect cause drug information corresponding to the side effect details in the side effect detail selection column 2502 is stored. Is displayed.
　このように、副作用詳細に対応する検査値の推移と、薬効に対応する検査項目の検査値の推移と、薬剤の処方開始日と処方終了日の時系列情報と、副作用原因薬剤候補とを１画面で表示することによって、副作用原因薬剤情報が正しく算出されているかの判断を支援できる。 Thus, the transition of the test value corresponding to the side effect details, the transition of the test value of the test item corresponding to the medicinal effect, the time series information of the prescription start date and prescription end date of the drug, and the side effect cause drug candidate are 1 By displaying on the screen, it is possible to assist in determining whether the side effect-causing drug information is correctly calculated.
　ユーザは、ユーザが副作用及び効果の統計解析を行うとき、分析画面２１００の統計解析選択オプション欄２１０９で統計解析オプションを選択する。統計解析選択オプション欄２１０９は、副作用分析の集計方法選択欄２１０９１と、副作用分析の統計手法選択欄２１０９２と、薬効分析の統計手法選択欄２１０９３とを含む。 The user selects a statistical analysis option in the statistical analysis selection option field 2109 of the analysis screen 2100 when the user performs a statistical analysis of side effects and effects. The statistical analysis selection option column 2109 includes a side effect analysis tabulation method selection column 21091, a side effect analysis statistical method selection column 21092, and a medicinal effect analysis statistical method selection column 21093.
　ユーザは、副作用分析の集計方法選択欄２１０９１のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用分析の集計方法から統計解析に使用する集計方法を選択する。ユーザは、副作用分析の集計方法選択欄２１０９１に、副作用分析の集計方法を直接入力することができてもよい。 The user operates the drop-down button in the side-effect analysis tabulation method selection field 21091 to select a tabulation method to be used for statistical analysis from a plurality of side-effect analysis tabulation methods displayed in a drop-down list (not shown). The user may be able to directly input the side effect analysis tabulation method in the side effect analysis tabulation method selection field 21091.
　ユーザは、副作用分析の統計手法選択欄２１０９２のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用分析の統計手法から統計解析に使用する統計手法を選択する。ユーザは、副作用分析の統計手法選択欄２１０９２に、副作用分析の統計手法を直接入力することができてもよい。 The user operates a drop-down button in the side-effect analysis statistical method selection column 21092 to select a statistical method to be used for statistical analysis from a plurality of side-effect analysis statistical methods displayed in a drop-down list (not shown). The user may be able to directly input the side effect analysis statistical method in the side effect analysis statistical method selection field 21092.
　ユーザは、薬効分析の統計手法選択欄２１０９３のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の薬効分析の統計手法から統計解析に使用する統計手法を選択する。ユーザは、薬効分析の統計手法選択欄２１０９３に、薬効分析の統計手法を直接入力することができてもよい。 The user operates the drop-down button of the statistical analysis method selection column 21093 for drug efficacy analysis, and selects a statistical technique to be used for statistical analysis from a plurality of statistical techniques for drug efficacy analysis displayed in a drop-down list (not shown). The user may be able to directly input the statistical method of the medicinal effect analysis into the statistical method selection column 21093 of the medicinal effect analysis.
　ユーザが、「統計解析」ボタン２１１０を操作すると、制御部１０１は、統計解析部１１４を起動し、副作用リスク算出処理（図１９参照）及び薬効評価算出処理（図２０参照）を実行する。 When the user operates the “statistical analysis” button 2110, the control unit 101 activates the statistical analysis unit 114 and executes a side effect risk calculation process (see FIG. 19) and a medicinal effect evaluation calculation process (see FIG. 20).
　図１９は、統計解析部１１４が副作用リスクを算出する処理のフローチャートである。 FIG. 19 is a flowchart of processing in which the statistical analysis unit 114 calculates the side effect risk.
　まず、統計解析部１１４は、副作用薬剤テーブル１０００及び処方情報テーブル３００を読み出し、メモリ１０３に記憶する（Ｓ１９０１）。 First, the statistical analysis unit 114 reads the side effect medicine table 1000 and the prescription information table 300 and stores them in the memory 103 (S1901).
　次に、統計解析部１１４は、読み出した処方情報テーブル３００に含まれる各薬剤の全使用症例数を集計する（Ｓ１９０２）。 Next, the statistical analysis unit 114 counts the total number of cases used for each drug included in the read prescription information table 300 (S1902).
　次に、統計解析部１１４は、副作用薬剤テーブル１０００の全ての副作用詳細を取得し、全ての副作用詳細について処理が完了しているかを判定する（Ｓ１９０３）。その結果、一部の副作用詳細について処理が完了していなければ、副作用詳細毎にステップＳ１９０４からＳ１９０７の処理を実行する。 Next, the statistical analysis unit 114 acquires all the side effect details in the side effect drug table 1000 and determines whether the processing is completed for all the side effect details (S1903). As a result, if processing for some of the side effect details has not been completed, the processing of steps S1904 to S1907 is executed for each side effect detail.
　ステップＳ１９０４では、統計解析部１１４は、副作用分析の統計手法選択欄２１０９２で選択された集計方法によって、副作用症例における薬剤の使用数を集計する。例えば、副作用分析の集計方法選択欄２１０９１で「副作用直前に処方された薬剤のみを対象」が選択されている場合、各症例において、副作用原因薬剤情報が「１」の薬剤のうち副作用の直前に処方された薬剤（すなわち、ＤＤＳＥの値が最小の薬剤）を集計の対象とし、副作用症例における薬剤の使用数を集計する。各薬剤の副作用症例における使用数を各薬剤の全使用症例数で除した値を副作用率として算出してもよい。 In step S1904, the statistical analysis unit 114 counts the number of drugs used in the side effect case by the counting method selected in the statistical method selection column 21092 for side effect analysis. For example, when “only target drugs prescribed immediately before side effects” is selected in the side effect analysis tabulation method selection field 21091, in each case, the drug with side effect cause drug information “1” immediately before the side effect The prescribed number of drugs (that is, the drug with the smallest DDSE value) is counted, and the number of drugs used in the case of side effects is counted. A value obtained by dividing the number of use cases in each drug by the number of use cases of each drug may be calculated as the side effect rate.
　次に、統計解析部１１４は、危険処方日数を算出する（Ｓ１９０５）。例えば、ステップＳ１９０４において各症例において集計対象とした薬剤のＤＤＳＡの平均値を危険処方日数として算出する。危険処方日数は、ＤＤＳＡの中央値を用いるなどの、他の方法によって当該薬剤のＤＤＳＡを代表する値を算出してもよい。 Next, the statistical analysis unit 114 calculates the risk prescription days (S1905). For example, in step S1904, the average value of DDSA of the drugs targeted for aggregation in each case is calculated as the risk prescription day. For the risk prescription days, a value representative of DDSA of the drug may be calculated by other methods such as using the median value of DDSA.
　次に、統計解析部１１４は、副作用分析の統計手法選択欄２１０９２で選択された統計手法によって副作用リスクを算出する（Ｓ１９０６）。例えば、副作用分析の統計手法選択欄２１０９２で「ロジスティック回帰」が選択されている場合、ステップＳ１９０２で集計した各薬剤の全使用症例数と、ステップＳ１９０４で集計した副作用症例における使用数と、各症例の副作用原因薬剤情報とを用いて、ロジスティック回帰によって算出したオッズ比を、副作用リスクとする。副作用リスクは他の統計手法によって算出してもよい。 Next, the statistical analysis unit 114 calculates the side effect risk by the statistical method selected in the statistical method selection column 21092 for side effect analysis (S1906). For example, when “logistic regression” is selected in the statistical method selection column 21092 for side effect analysis, the total number of use cases of each drug counted in step S1902, the number of use cases in the side effect cases counted in step S1904, and each case The odds ratio calculated by logistic regression using the side effect-causing drug information is used as the side effect risk. The side effect risk may be calculated by other statistical methods.
　次に、統計解析部１１４は、各薬剤の薬剤名と全使用症例数と副作用症例における使用数１２０４と副作用リスク１２０５と危険処方日数１２０６とを副作用リスクテーブル１２００に登録する（Ｓ１９０７）。 Next, the statistical analysis unit 114 registers the drug name, the total number of use cases, the use number 1204 in the side effect case, the side effect risk 1205, and the risk prescription day 1206 in the side effect risk table 1200 (S1907).
　ステップＳ１９０３において、全ての副作用詳細について処理が完了していると判定されれば、制御部１０１は、表示画面生成部１０５を起動する。表示画面生成部１０５は、ステップＳ１９０２で算出された各薬剤の全使用症例数と、ステップＳ１９０３で算出された副作用症例における使用数と、ステップＳ１９０５で算出された危険処方日数と、ステップＳ１９０６で算出された副作用リスクとを使用して、全ての分析処理結果を表示する統計解析結果参照画面２６００を生成し（Ｓ１９０８）、生成した画面データを出力する（Ｓ１９０９）。 If it is determined in step S1903 that the processing for all the side effect details has been completed, the control unit 101 activates the display screen generation unit 105. The display screen generation unit 105 calculates the total number of use cases of each drug calculated in step S1902, the number of use cases in the side effect case calculated in step S1903, the risk prescription days calculated in step S1905, and the calculation in step S1906. A statistical analysis result reference screen 2600 that displays all analysis processing results is generated using the side effect risk that has been generated (S1908), and the generated screen data is output (S1909).
　図２０は、統計解析部１１４が薬効評価を算出する処理のフローチャートである。 FIG. 20 is a flowchart of a process in which the statistical analysis unit 114 calculates the efficacy evaluation.
　まず、統計解析部１１４は、薬効情報テーブル１１００及び処方情報テーブル３００を読み出し、メモリ１０３に記憶する（Ｓ２００１）。 First, the statistical analysis unit 114 reads the medicinal effect information table 1100 and the prescription information table 300 and stores them in the memory 103 (S2001).
　次に、統計解析部１１４は、処方情報テーブル３００より各薬剤の全ての使用症例数を集計する（Ｓ２００２）。なお、既にステップＳ１９０２において各薬剤の使用症例数が集計されている場合、ステップＳ１９０２で算出した各薬剤の全使用症例数を用いてもよい。 Next, the statistical analysis unit 114 counts the number of all use cases of each drug from the prescription information table 300 (S2002). Note that when the number of use cases of each drug has already been counted in step S1902, the total number of use cases of each drug calculated in step S1902 may be used.
　次に、統計解析部１１４は、薬効情報テーブル１１００の効果を全て取得し、全ての効果について処理が完了しているかを判定する（Ｓ２００３）。その結果、一部の効果について処理が完了していなければ、効果毎にステップＳ２００４からＳ２００６の処理を実行する。 Next, the statistical analysis unit 114 acquires all the effects of the medicinal effect information table 1100, and determines whether the processing has been completed for all the effects (S2003). As a result, if the processing has not been completed for some effects, the processing from step S2004 to S2006 is executed for each effect.
　次に、統計解析部１１４は、当該効果に関する薬剤の総処方日数及び改善度を集計する（Ｓ２００４）。 Next, the statistical analysis unit 114 totals the total prescription days and the improvement degree of the drug related to the effect (S2004).
　次に、統計解析部１１４は、薬効分析の統計手法選択欄２１０９３で選択された統計手法によって薬効評価を算出する（Ｓ２００５）。例えば、薬効分析の統計手法選択欄２１０９３で「ロジスティック回帰」が選択されている場合、ステップＳ２００２で集計した各薬剤の全使用症例数と、ステップＳ２００４で集計した各薬剤の総処方日数及び改善度を用いて、ロジスティック回帰により算出したオッズ比を、薬効評価とする。薬効評価は、ステップＳ２００５で集計した改善度の総和を、ステップＳ２００５で集計した総処方日数で除した値を用いるなど、他の手法によって算出した値を用いてもよい。 Next, the statistical analysis unit 114 calculates a medicinal effect evaluation by the statistical method selected in the statistical method selection column 21093 of the medicinal effect analysis (S2005). For example, when “Logistic regression” is selected in the statistical analysis method selection column 21093 for the medicinal effect analysis, the total number of use cases of each drug calculated in step S2002, the total prescription days and the improvement degree of each drug calculated in step S2004 The odds ratio calculated by logistic regression is used as the drug efficacy evaluation. For the medicinal effect evaluation, a value calculated by another method may be used, such as a value obtained by dividing the sum total of the degree of improvement totaled in step S2005 by the total prescription days totaled in step S2005.
　次に、統計解析部１１４は、各薬剤の薬剤名と全使用症例数と薬効評価とを薬効評価テーブル１３００に登録する（Ｓ２００６）。 Next, the statistical analysis unit 114 registers the drug name, the number of all used cases, and the drug efficacy evaluation of each drug in the drug efficacy evaluation table 1300 (S2006).
　ステップＳ２００３において、全ての効果について処理が完了していると判定されれば、制御部１０１は、表示画面生成部１０５を起動する。表示画面生成部１０５は、ステップＳ２００２で算出された各薬剤の全使用症例数と、ステップＳ２００５で算出された薬効評価とを使用して、全ての分析処理結果を表示する統計解析結果参照画面２６００に出力する画面を生成し（Ｓ２００７）、生成した画面データを出力する（Ｓ２００８）。 If it is determined in step S2003 that the processing has been completed for all the effects, the control unit 101 activates the display screen generation unit 105. The display screen generation unit 105 uses the total number of use cases of each drug calculated in step S2002 and the medicinal effect evaluation calculated in step S2005 to display a statistical analysis result reference screen 2600 that displays all analysis processing results. A screen to be output is generated (S2007), and the generated screen data is output (S2008).
　このとき、副作用リスク算出処理（図１９）によって副作用リスクが既に算出されている場合、表示画面生成部１０５は、ステップＳ１９０２で算出された各薬剤の全使用症例数と、ステップＳ１９０３で算出された副作用症例における使用数と、ステップＳ１９０５で算出された危険処方日数と、ステップＳ１９０６で算出された副作用リスクと、ステップＳ２００５で算出された薬効評価とを使用して、全ての分析処理結果を統計解析結果参照画面２６００に出力する画面を生成する。 At this time, when the side effect risk has already been calculated by the side effect risk calculation process (FIG. 19), the display screen generation unit 105 calculates the total number of use cases of each drug calculated in step S1902 and the calculation in step S1903. Statistical analysis of all analysis results using the number of side effect cases used, risk prescription days calculated in step S1905, side effect risk calculated in step S1906, and medicinal efficacy evaluation calculated in step S2005 A screen to be output to the result reference screen 2600 is generated.
　図２４は、第１実施例において、ユーザが統計解析結果を参照するときに入出力端末１３０が表示する統計解析結果参照画面２６００の例を示す図である。 FIG. 24 is a diagram showing an example of a statistical analysis result reference screen 2600 displayed by the input / output terminal 130 when the user refers to the statistical analysis result in the first embodiment.
　画面例２６００は、副作用選択欄２６０１と、副作用詳細選択欄２６０２と、薬効選択欄２６０３と、整列基準選択エリア２６０４と、統計解析結果表示エリア２６０６とを含む。 Screen example 2600 includes a side effect selection field 2601, a side effect detail selection field 2602, a medicinal effect selection field 2603, an alignment criterion selection area 2604, and a statistical analysis result display area 2606.
　ユーザは、副作用選択欄２６０１のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用から統計解析結果表示エリア２６０６に表示する副作用を選択する。ユーザは、副作用選択欄２６０１に、副作用を直接入力することができてもよい。 The user operates the drop-down button in the side-effect selection field 2601 to select a side effect to be displayed in the statistical analysis result display area 2606 from a plurality of side effects displayed in a drop-down list (not shown). The user may be able to directly input a side effect in the side effect selection field 2601.
　ユーザは、副作用詳細選択欄２６０２のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用詳細から統計解析結果表示エリア２６０６に表示する副作用詳細を選択する。ユーザは、副作用詳細選択欄２６０２に、副作用詳細を直接入力することができてもよい。 The user operates the drop-down button in the side-effect detail selection field 2602 to select the side-effect details to be displayed in the statistical analysis result display area 2606 from a plurality of side-effect details displayed in a drop-down list (not shown). The user may be able to directly input the side effect details in the side effect detail selection field 2602.
　ユーザは、薬効選択欄２６０３のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の薬効から統計解析結果表示エリア２６０６に表示する薬効を選択する。ユーザは、薬効選択欄２６０３に、薬効を直接入力することができてもよい。 The user operates the drop-down button in the medicinal effect selection field 2603 to select the medicinal effect to be displayed in the statistical analysis result display area 2606 from the plural medicinal effects displayed in the drop-down list (not shown). The user may be able to directly enter the medicinal effect in the medicinal effect selection field 2603.
　ユーザは、整列基準選択エリア２６０４の項目選択欄２６０４２のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の項目から整列の基準を選択する。ユーザは、項目選択欄２６０４２に、整列の基準となる項目を直接入力することができてもよい。 The user operates the drop-down button in the item selection field 26042 in the alignment reference selection area 2604, and selects an alignment reference from a plurality of items displayed in a drop-down list (not shown). The user may be able to directly input an item as an alignment reference in the item selection field 26042.
　ユーザは、整列基準選択エリア２６０４に付属する順列設定エリア２６０４３のラジオボタンを選択し、整列の順列を昇順又は降順とするかを選択する。 The user selects a radio button in the permutation setting area 26043 attached to the alignment reference selection area 2604, and selects whether the permutation of the alignment is ascending or descending.
　ユーザが整列基準選択エリア２６０４の「整列設定」ボタン２６０４１を操作すると、整列制御部１１５が起動し、項目選択欄２６０４２で選択された項目について、順列設定エリア２６０４３で選択された順列に従い、統計解析結果表示エリア２６０６に表示される統計解析結果を並び替える。 When the user operates the “alignment setting” button 26041 in the alignment reference selection area 2604, the alignment control unit 115 is activated, and the item selected in the item selection field 26042 is statistically analyzed according to the permutation selected in the permutation setting area 26043. The statistical analysis results displayed in the result display area 2606 are rearranged.
　統計解析結果表示エリア２６０６には、副作用選択欄２６０１で選択した副作用と、副作用詳細選択欄２６０２で選択した副作用詳細と、薬効選択欄２６０３で選択した薬効とに該当する薬剤の全使用数と、副作用患者における使用数と、副作用率と、副作用リスクと、危険処方日数と、薬効評価とが表示される。 In the statistical analysis result display area 2606, the side effects selected in the side effect selection field 2601, the side effect details selected in the side effect detail selection field 2602, the total number of drugs used corresponding to the drug effect selected in the drug effect selection field 2603, The number of uses, the rate of side effects, the risk of side effects, the number of days for prescribing a risk, and the evaluation of drug efficacy are displayed.
　以上に説明したように、第１実施例の副作用分析支援システムでは、多種多様な投薬治療における薬剤の副作用リスクと危険処方日数と薬効評価とを可視化して１画面で表示することによって、適切な投薬治療を支援することができる。 As described above, in the side effect analysis support system of the first embodiment, the side effect risk, risk prescription days, and drug efficacy evaluation of drugs in various medications are visualized and displayed on a single screen. Can support medication.
　また、副作用分析支援システムは、検査値の変動から副作用の改善期間を算出し、処方されている薬剤のうち、改善期間の開始後に継続して処方されている薬剤以外の薬剤が副作用と関係することを示す副作用原因薬剤情報１００６を算出し、当該副作用と関係する薬剤の情報を表示するための画面２１００のデータを出力するので、副作用改善期間の開始日の前後に処方された薬剤を切り分けることができ、副作用の原因となった薬剤を推定することができる。 In addition, the side effect analysis support system calculates the side effect improvement period from the fluctuation of the test value, and among the prescribed drugs, drugs other than those prescribed continuously after the start of the improvement period are related to the side effects. Side effect cause drug information 1006 is calculated, and the data of the screen 2100 for displaying the drug information related to the side effect is output, so that the prescribed drug is separated before and after the start date of the side effect improvement period. The drug that caused the side effect can be estimated.
　また、副作用分析支援システムは、分析対象の副作用と同じ症状が発生する疾患の患者を除外して、副作用の改善期間を算出するので、副作用症例を適切に選択することができる。 Also, since the side effect analysis support system calculates a period of improvement of side effects by excluding patients with diseases in which the same symptoms as the side effects to be analyzed occur, it is possible to appropriately select side effect cases.
　また、副作用分析支援システムは、薬剤の処方開始日を処方情報テーブル３００から取得し、検査情報テーブル４００に記録された検査結果の変動から副作用開始日及び副作用終了日を算出し、薬剤の処方開始日と副作用開始日との差（ＤＤＳＡ）を算出し、前記算出されたＤＤＳＡを統計処理することによって、複数の症例において、危険処方日数を算出するので、適切な投薬治療を支援することができる。 Further, the side effect analysis support system acquires the prescription start date of the drug from the prescription information table 300, calculates the side effect start date and the side effect end date from the variation of the test result recorded in the test information table 400, and starts the prescription of the drug By calculating the difference between the day and the start date of side effect (DDSA) and statistically processing the calculated DDSA, the risk prescription days are calculated in a plurality of cases, so that appropriate medication can be supported. .
　また、副作用分析支援システムは、副作用開始日及び副作用終了日によって特定される副作用期間が複数見出される場合、副作用期間の回数毎に副作用原因薬剤情報及び改善期間を算出するので、検査値が増減を繰り返した場合でも、副作用の原因となった薬剤を適確に推定することができる。 The side effect analysis support system calculates side effect causal agent information and improvement period for each number of side effect periods when multiple side effect periods specified by the side effect start date and side effect end date are found. Even when it is repeated, it is possible to accurately estimate the drug that caused the side effect.
　また、副作用分析支援システムは、副作用期間中に検査値が単調減少している期間及び単調増加している期間を抽出し、抽出された期間のうち一つの副作用期間中の最後の期間を改善期間とするので、検査値が増減を繰り返した場合でも、改善期間を正確に算出することができる。 In addition, the side effect analysis support system extracts the period during which the test value monotonously decreases and the period during which the test value monotonously increases during the side effect period, and improves the last period in one side effect period among the extracted periods. Therefore, even when the test value repeatedly increases and decreases, the improvement period can be accurately calculated.
　また、副作用分析支援システムは、既知の副作用を発生させる薬剤を除外して、副作用と関係する薬剤を選択するので、副作用と関係ない薬剤を副作用原因薬剤候補から除外することが可能となり、副作用の原因となった薬剤を適確に推定することができる。さらに、既知の副作用を発生させる薬剤のみについて副作用と関係する薬剤を選択することができ、副作用を発生する既知の薬剤から副作用の原因となった薬剤を推定することができる。 In addition, since the side effect analysis support system excludes drugs that cause known side effects and selects drugs that are related to side effects, it is possible to exclude drugs that are not related to side effects from drug candidates that cause side effects. The causative drug can be estimated accurately. Furthermore, a drug related to a side effect can be selected for only a drug that causes a known side effect, and the drug that caused the side effect can be estimated from the known drug that causes the side effect.
　また、副作用分析支援システムは、副作用開始日より前の抽出対象期間に処方された薬剤の中から副作用と関係する薬剤を選択するので、副作用の原因となった薬剤を適確に推定することができる。特に、薬剤の処方から副作用の発生までの期間が副作用によって異なることから、処方から早く発生する副作用と遅く発生する副作用とを切り分けて分析することができる。 In addition, since the side effect analysis support system selects the drug related to the side effect from the drugs prescribed in the extraction target period before the start date of the side effect, it is possible to accurately estimate the drug that caused the side effect. it can. In particular, since the period from the prescription of a drug to the occurrence of a side effect varies depending on the side effect, it is possible to analyze by analyzing the side effect that occurs early from the prescription and the side effect that occurs late.
　また、副作用分析支援システムは、複数の患者の副作用原因薬剤情報と薬剤の使用症例数とを統計処理することによって、当該薬剤の副作用リスクを算出するので、院内の全症例を用いて薬剤の副作用リスクを算出でき、副作用が発生する危険度を正確に見積もることができる。 In addition, the side effect analysis support system calculates the side effect risk of the drug by statistically processing the side effect cause drug information and the number of drug use cases of multiple patients. Risk can be calculated and the risk of side effects can be estimated accurately.
　また、副作用分析支援システムは、薬剤の処方終了日から副作用開始日までの経過日数（ＤＤＳＥ）を算出し、ＤＤＳＥが最小の薬剤の副作用リスクを算出するので、副作用が発生した直前に処方されている薬剤を適確に選択することができる。 In addition, the side effect analysis support system calculates the number of days (DDSE) from the end date of drug prescription to the start date of side effect, and calculates the side effect risk of the drug with the smallest DDSE. The appropriate drug can be selected accurately.
　また、副作用分析支援システムは、ＤＤＳＥが負である場合、ＤＤＳＥを０日に設定するので、副作用と関係がる全ての薬剤を抽出することができる。 In addition, since the side effect analysis support system sets DDSE to 0 when DDSE is negative, it is possible to extract all drugs related to side effects.
　また、副作用分析支援システムは、副作用の改善期間と薬剤の処方期間とを同じ時系列に表示するための症例分析画面２５００を出力するので、薬剤の処方期間と検査値の変化と副作用の改善期間とを可視化することができる。すなわち、図２３に示す分析画面の例では、薬剤Ｂ及びＣが副作用と関係がないと判断することができる。 Also, the side effect analysis support system outputs a case analysis screen 2500 for displaying the side effect improvement period and the drug prescription period in the same time series, so the drug prescription period, the change in the test value, and the side effect improvement period And can be visualized. That is, in the example of the analysis screen shown in FIG. 23, it can be determined that the drugs B and C are not related to side effects.
　また、副作用分析支援システムは、検査値の変動から検査値の改善期間を算出し、処方されている薬剤のうち、改善期間の前に処方されている薬剤が、当該副作用の改善と関係することを示す薬効情報を算出するので、薬効を発生させる薬剤を特定することができる。また、薬効を数値化することによって、薬剤による改善度を定量的に評価することができる。また、副作用リスクと薬効評価とを１画面で分かりやすく表示することができる。 The side effect analysis support system calculates the improvement period of the test value from the fluctuation of the test value, and among the prescribed drugs, the drug prescribed before the improvement period is related to the improvement of the side effect. Since the medicinal effect information indicating the medicinal effect is calculated, it is possible to identify the drug that causes medicinal effect. Moreover, the improvement degree by a chemical | medical agent can be quantitatively evaluated by quantifying a medicinal effect. In addition, the risk of side effects and the evaluation of drug efficacy can be easily displayed on one screen.
　また、副作用分析支援システムは、複数の患者の薬効情報と薬剤の使用症例数とを統計処理することによって、当該薬剤の薬効の程度を示す評価値を算出するので、院内の全症例を用いて薬効を算出でき、薬効を正確に見積もることができる。 In addition, the side effect analysis support system calculates the evaluation value indicating the degree of efficacy of the drug by statistically processing the drug efficacy information and the number of use cases of the drug. The medicinal effect can be calculated and the medicinal effect can be accurately estimated.
　＜実施例２＞
　第２実施例の副作用分析支援システムは、第１実施例と同様に、症例毎に各薬剤の副作用及び効果を分析し、分析結果を可視化して出力する計算機システムであり、病院情報システム１２０と、ネットワーク１４０と、入出力端末１３０とで構成される。なお、第２実施例において、前述した第１実施例と異なる部分のみを説明し、第１実施例と同じ構成及び機能には同じ符号を付し、それらの説明は省略する。 <Example 2>
As in the first embodiment, the side effect analysis support system of the second embodiment is a computer system that analyzes the side effects and effects of each drug for each case, visualizes and outputs the analysis results, and includes a hospital information system 120 and The network 140 and the input / output terminal 130 are configured. In the second embodiment, only parts different from the first embodiment described above will be described, the same components and functions as those in the first embodiment will be denoted by the same reference numerals, and description thereof will be omitted.
　第２実施例において、知識データベース１１７は、副作用後分析対象日数設定テーブル２７００を含む。図２５は、副作用後分析対象日数設定テーブル２７００の構成例を示す図である。 In the second embodiment, the knowledge database 117 includes a post-side effect analysis target days setting table 2700. FIG. 25 is a diagram showing a configuration example of the post-side effect analysis target days setting table 2700.
　副作用後分析対象日数設定テーブル２７００は、副作用原因薬剤情報に「１」が登録された薬剤について、別の副作用期間においても副作用原因薬剤情報に「１」が登録されているかを分析する対象の副作用詳細２７０１及び当該副作用詳細に対応する副作用後の分析をする日数２７０２のフィールドを含む。例えば、レコード２７００Ａは、「ＡＬＴ（ＧＰＴ）の上昇」の副作用詳細の副作用後の分析をする日数は３６５日であることを示す。 The post-side effect analysis target days setting table 2700 is a target side effect analysis for analyzing whether “1” is registered in the side-effect-causing drug information even during another side-effect period for a drug registered with “1” in the side-effect-causing drug information. It includes a field 2702 for details 2701 and the number of days 2702 for analysis after a side effect corresponding to the side effect detail. For example, the record 2700A indicates that the number of days after the side effect analysis of the side effect details of “increased ALT (GPT)” is 365 days.
　図２６は、第２実施例の副作用原因情報算出部１１１が副作用原因情報を更新する処理のフローチャートである。図２６に示す副作用原因情報更新処理は、副作用原因情報算出部１１１が、副作用の原因を算出（図１７）した後、自動的に実行してもよいし、ユーザの指示によって処理を実行してもよい。 FIG. 26 is a flowchart of processing in which the side effect cause information calculation unit 111 of the second embodiment updates side effect cause information. The side effect cause information update process shown in FIG. 26 may be automatically executed after the side effect cause information calculation unit 111 calculates the cause of the side effect (FIG. 17), or the process is executed according to a user instruction. Also good.
　まず、副作用原因情報算出部１１１は、統合データベース１１６に格納された処方情報テーブル３００と、ステップ１４０４とステップ１４０５で作成された副作用症例テーブル９００と、ステップ１４０６で作成された副作用薬剤テーブル１０００とをメモリ１０３に読み出す（Ｓ２８０１）。 First, the side effect cause information calculation unit 111 includes the prescription information table 300 stored in the integrated database 116, the side effect case table 900 created in steps 1404 and 1405, and the side effect drug table 1000 created in step 1406. Reading to the memory 103 (S2801).
　次に、副作用原因情報算出部１１１は、知識データベース１１７に格納された副作用後分析対象日数設定テーブル２７００をメモリ１０３に読み出す（Ｓ２８０２）。 Next, the side effect cause information calculation unit 111 reads the post-side effect analysis target days setting table 2700 stored in the knowledge database 117 into the memory 103 (S2802).
　次に、副作用原因情報算出部１１１は、副作用薬剤テーブル１０００の全ての患者ＩＤのレコードを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ２８０３）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ２８０４からＳ２８０６の処理を繰り返し実行する。 Next, the side effect cause information calculation unit 111 acquires records of all patient IDs in the side effect medicine table 1000, and determines whether the processing has been completed for all patient IDs (S2803). As a result, if the processing is not completed for some patient IDs, the processing from step S2804 to S2806 is repeatedly executed for each patient ID.
　次に、副作用原因情報算出部１１１は、副作用薬剤テーブル１０００の全ての副作用詳細のレコードを取得し、各副作用詳細についてステップＳ２８０５からＳ２８０６の処理を繰り返し実行する（Ｓ２８０４）。なお、全ての副作用詳細について処理が完了していれば、ステップＳ２８０３に戻り、次の患者ＩＤについて処理をする。 Next, the side effect cause information calculation unit 111 acquires all side effect detail records in the side effect drug table 1000, and repeatedly executes the processing from step S2805 to S2806 for each side effect detail (S2804). If all the side effect details have been processed, the process returns to step S2803 to process the next patient ID.
　次に、ステップＳ２８０５において、副作用原因情報算出部１１１は、副作用後分析対象日数設定テーブル２７００から、ステップＳ２８０４で選択された副作用詳細の副作用後分析対象日数を取得する。 Next, in step S2805, the side effect cause information calculation unit 111 acquires the number of days after analysis of side effects of the side effect details selected in step S2804 from the analysis target days setting table after post side effect 2700.
　次に、副作用原因情報算出部１１１は、ステップＳ２８０３で選択された患者ＩＤ及びステップＳ２８０４で選択された副作用詳細に対応する副作用回数について、副作用回数が小さい順に、ステップＳ２８０７からＳ２８０９の処理を繰り返し実行する（Ｓ２８０６）。 Next, the side effect cause information calculation unit 111 repeatedly executes the processes of steps S2807 to S2809 in ascending order of the number of side effects for the side effect number corresponding to the patient ID selected in step S2803 and the side effect details selected in step S2804. (S2806).
　次に、ステップ２８０７において、副作用原因情報算出部１１１は、ステップＳ２８０６で選択された副作用回数に対応する副作用終了日を取得し、取得した副作用終了日から、ステップ２８０５で取得した副作用分析対象日数が経過するまでの期間を副作用後分析対象区間として設定する。例えば、副作用終了日が２０１４年４月２５日であり、副作用分析対象日数が３６５日である場合、２０１４年４月２６日から２０１５年４月２５日の３６５日間を副作用後分析対象区間として設定する。そして、設定された分析対象区間内にステップ２８０６で選択された以外の副作用回数の副作用開始日が含まれるかを判定する。ステップ２８０６で選択された以外の副作用回数の副作用開始日が分析対象区間内に含まれない場合、ステップＳ２８０６に戻る。一方、ステップ２８０６で選択された以外の副作用回数の副作用開始日が分析対象区間内に含まれる場合、副作用原因情報算出部１１１は、副作用後分析対象区間の副作用回数で副作用原因薬剤情報に１が登録されている回数を薬剤ごとに集計する（Ｓ２８０８）。 Next, in step 2807, the side effect cause information calculation unit 111 acquires a side effect end date corresponding to the number of side effects selected in step S2806, and from the acquired side effect end date, the side effect analysis target days acquired in step 2805 is calculated. The period until elapses is set as the analysis target section after the side effect. For example, when the side effect end date is April 25, 2014 and the side effect analysis target number is 365 days, 365 days from April 26, 2014 to April 25, 2015 are set as the post-side effect analysis target section. To do. Then, it is determined whether the side effect start date of the number of side effects other than that selected in step 2806 is included in the set analysis target section. If the side effect start date for the number of side effects other than that selected in step 2806 is not included in the analysis target section, the process returns to step S2806. On the other hand, when the side effect start date of the number of side effects other than that selected in step 2806 is included in the analysis target section, the side effect cause information calculating unit 111 sets 1 in the side effect cause drug information by the side effect count in the post-side effect analysis target section. The number of registered times is counted for each medicine (S2808).
　次に、副作用原因情報算出部１１１は、集計結果及び設定画面（図２２）で設定された副作用後除外条件に基づいて各薬剤の副作用原因薬剤情報を更新する（ステップ２８０９）。例えば、副作用後除外条件として「副作用後分析対象区間内に１回以上処方があり、副作用後分析対象区間内の副作用期間における副作用原因薬剤情報に１が登録されている回数が０回である場合」が設定されている場合を考える。副作用原因情報算出部１１１は、ステップ２８０６で選択された副作用回数において副作用原因薬剤情報に「１」が登録されている薬剤のうち、副作用後分析対象区間に１回以上の処方があり、かつ、副作用後分析対象区間内の副作用期間における副作用原因薬剤情報に「１」が登録されていない薬剤について、副作用原因薬剤情報を「１」から「０」に更新する。ここで、副作用後除外条件は、「副作用後分析対象区間内に１回以上処方があり、副作用後分析対象区間内の副作用期間における副作用原因薬剤情報に１が登録されている回数が副作用後分析対象区間内の副作用回数の半数を上回った場合」などでもよい。 Next, the side effect cause information calculation unit 111 updates the side effect cause drug information of each drug based on the counting result and the post-side effect exclusion condition set on the setting screen (FIG. 22) (step 2809). For example, the exclusion condition after side effect is “when there is a prescription at least once in the post-side effect analysis target section and the number of times 1 is registered in the side effect cause drug information in the side effect period in the post-side effect analysis target section is zero. ”Is set. The side effect cause information calculation unit 111 has one or more prescriptions in the post-side effect analysis target section among the drugs registered with “1” in the side effect cause drug information in the number of side effects selected in step 2806, and The side effect cause drug information is updated from “1” to “0” for the drug for which “1” is not registered in the side effect cause drug information in the side effect period in the post-side effect analysis target section. Here, the post-adverse reaction exclusion condition is “the number of prescriptions that have been prescribed once or more in the post-side-effect analysis target section and 1 is registered in the side-effect cause drug information in the side-effect period in the post-side-effect analysis target section. It may be “when more than half of the number of side effects in the target section”.
　ここで、図２２を用いて、第２実施例において、ユーザが副作用詳細に対応する副作用後除外条件及び副作用後分析対象に数を変更するときに設定画面について説明する。設定画面の副作用後分析設定変更エリア３００１は、副作用詳細の選択欄３００１１と、副作用後除外条件の選択欄３００１２と、副作用後分析日数の定義欄３００１３とを含む。 Here, with reference to FIG. 22, the setting screen will be described when the user changes the number to the post-side effect exclusion condition and the post-side effect analysis target corresponding to the side effect details in the second embodiment. The post-side effect analysis setting change area 3001 of the setting screen includes a side effect detail selection column 30011, a post-side effect exclusion condition selection column 30012, and a post-side effect analysis days definition column 30013.
　ユーザは、副作用後分析設定変更エリア３００１のドロップボタン３００１１を操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用詳細から副作用後に分析する副作用詳細を選択する。また、ユーザは、ドロップボタン３００１２を操作し、ドロップダウンリスト（図示省略）に表示される複数の除外条件から、分析から除外される副作用後除外条件を選択する。また、ユーザはアップダウンボタン３００１３を操作し、副作用後に分析する日数を変更する。又は、ユーザは、副作用後分析設定変更エリア３００１に直接、設定したい副作用詳細の副作用後分析対象日数を入力することもできる。 The user operates the drop button 30011 in the post-side effect analysis setting change area 3001 to select side effect details to be analyzed after side effects from a plurality of side effect details displayed in a drop-down list (not shown). Further, the user operates the drop button 30012 to select a post-side effect exclusion condition to be excluded from the analysis from a plurality of exclusion conditions displayed in a drop-down list (not shown). In addition, the user operates the up / down button 30013 to change the number of days to be analyzed after a side effect. Alternatively, the user can directly input the number of days after the side effect analysis target of the side effect details to be set directly into the post-side effect analysis setting change area 3001.
　ユーザは、変更した設定値を保存する場合は「保存」ボタン２３０２を操作し、保存しない場合は「キャンセル」ボタン２３０３を操作する。いずれか一方のボタンが操作された場合、設定画面２３００は終了し、分析画面２１００に戻る。 The user operates the “Save” button 2302 when saving the changed setting value, and operates the “Cancel” button 2303 when not saving. If any one of the buttons is operated, the setting screen 2300 is terminated and the analysis screen 2100 is returned to.
　図２７は、第２実施例において、ユーザが症例毎の詳細な分析結果を参照するときに入出力端末１３０が表示する症例分析画面３１００の例を示す図である。 FIG. 27 is a diagram illustrating an example of a case analysis screen 3100 displayed by the input / output terminal 130 when the user refers to a detailed analysis result for each case in the second embodiment.
　症例分析画面３１００は、副作用選択欄３１０１と、副作用詳細選択欄３１０２と、患者ＩＤ選択欄３１０４と、「患者一覧に戻る」ボタン３１０５と、検査結果表示エリア３１０６と、処方薬剤表示エリア３１０７と、副作用原因薬剤候補表示エリア３１０８と、副作用後分析区間における副作用回数を選択する副作用後分析対象区間選択欄３１０９とを含む。 The case analysis screen 3100 includes a side effect selection field 3101, a side effect detail selection field 3102, a patient ID selection field 3104, a “return to patient list” button 3105, a test result display area 3106, a prescription drug display area 3107, A side effect causal drug candidate display area 3108 and a post side effect analysis target section selection field 3109 for selecting the number of side effects in the post side effect analysis section are included.
　ユーザは、副作用選択欄３１０１のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用から分析及び可視化をする副作用を選択する。ユーザは、副作用選択欄３１０１に、副作用を直接入力することができてもよい。 The user operates a drop-down button in the side-effect selection column 3101 to select a side-effect to be analyzed and visualized from a plurality of side-effects displayed in a drop-down list (not shown). The user may be able to directly input a side effect in the side effect selection field 3101.
　ユーザは、副作用詳細選択欄３１０２のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の副作用詳細から分析及び可視化をする副作用詳細を選択する。ユーザは、副作用詳細選択欄３１０２に、副作用詳細を直接入力することができてもよい。 The user operates the drop-down button in the side-effect detail selection column 3102 to select side-effect details to be analyzed and visualized from a plurality of side-effect details displayed in a drop-down list (not shown). The user may be able to directly input the side effect details in the side effect detail selection field 3102.
　ユーザは、患者ＩＤ選択欄３１０４のドロップダウンボタンを操作し、ドロップダウンリスト（図示省略）に表示される複数の患者ＩＤから分析及び可視化をする患者ＩＤを選択する。ユーザは、患者ＩＤ選択欄３１０４に、直接患者ＩＤを入力することもできてもよい。 The user operates a drop-down button in the patient ID selection field 3104 to select a patient ID to be analyzed and visualized from a plurality of patient IDs displayed in a drop-down list (not shown). The user may be able to input the patient ID directly in the patient ID selection field 3104.
　ユーザは、症例毎の詳細な分析を終了する場合、「患者一覧に戻る」ボタン３１０５を操作することによって、症例分析画面３１００を終了し、分析画面２１００を表示する。 When ending the detailed analysis for each case, the user operates the “return to patient list” button 3105 to end the case analysis screen 3100 and display the analysis screen 2100.
　検査結果表示エリア３１０６には、副作用詳細選択欄３１０２で選択された副作用詳細に対応する検査項目の検査値の推移を示すグラフ３１０６２と、当該検査値が異常かを判定するための閾値を示す線３１０６３と、ステップＳ１４０３で選択された薬効に対応する検査項目の検査値の推移を示すグラフ例３１０６１と、副作用後分析対象区間選択欄３１０９で選択された副作用回数の副作用後の分析区間を示す線３１０６４と、が表示される。また、処方薬剤表示エリア３１０７には、患者ＩＤ選択欄３１０４で選択された症例に処方された薬剤の処方開始日と処方終了日の時系列情報が表示される。また、副作用原因薬剤候補表示エリア３１０８には、処方薬剤表示エリア３１０７に表示される薬剤のうち、副作用詳細選択欄３１０２で副作用詳細に対応する副作用原因薬剤情報に「１」が格納されている薬剤が表示される。 The test result display area 3106 includes a graph 31062 indicating the transition of the test value of the test item corresponding to the side effect details selected in the side effect detail selection field 3102 and a line indicating a threshold value for determining whether the test value is abnormal. 31063, a graph example 31061 indicating the transition of the test value of the test item corresponding to the medicinal effect selected in step S1403, and a line indicating the analysis interval after the side effect of the number of side effects selected in the post-side effect analysis target interval selection field 3109 31064 is displayed. In the prescription drug display area 3107, time series information of the prescription start date and prescription end date of the drug prescribed for the case selected in the patient ID selection field 3104 is displayed. In the side effect cause drug candidate display area 3108, among the drugs displayed in the prescription drug display area 3107, the drug in which “1” is stored in the side effect cause drug information corresponding to the side effect details in the side effect detail selection field 3102 Is displayed.
　画面例３１００には、患者ＩＤが「＃＃＃４」の患者は、副作用「肝機能障害」の副作用詳細「ＡＬＴ（ＧＰＴ）の上昇」が２回発生しており、１回目の副作用の期間が「副作用期間１」、２回目の副作用の期間が「副作用期間２」と表示され、各副作用の改善期間が示されている。また、患者ＩＤが「＃＃＃４」の患者には、薬剤Ａ、Ｂ、Ｃ、Ｄ、Ｅがそれぞれ処方されていることが示されている。 In the screen example 3100, the patient whose patient ID is “## 4” has the side effect “liver dysfunction” and the side effect detail “ALT (GPT) rise” twice, and the duration of the first side effect “Side effect period 1”, the second side effect period is displayed as “side effect period 2”, and the improvement period of each side effect is shown. In addition, it is shown that medicines A, B, C, D, and E are respectively prescribed to a patient whose patient ID is “## 4”.
　この患者において、薬剤Ａは、副作用期間１の副作用開始日前に処方されているため、副作用回数が１の副作用原因薬剤情報には「１」が記録される。 In this patient, since the drug A is prescribed before the side effect start date in the side effect period 1, “1” is recorded in the side effect cause drug information with the number of side effects of 1.
　薬剤Ｂは、副作用期間１の副作用開始日前から、副作用期間１の改善期間以降まで継続して処方されているため、副作用回数が１の副作用原因薬剤情報には「０」が記録される。 Since drug B is continuously prescribed from the start date of the side effect in the side effect period 1 to the improvement period in the side effect period 1, “0” is recorded in the side effect cause drug information with the side effect count of 1.
　薬剤Ｃは、副作用期間１の副作用開始日後に処方が開始しているため、副作用回数が１の副作用原因薬剤情報には「０」が記録される。 Since drug C has started prescribing after the side effect start date of side effect period 1, “0” is recorded in the side effect cause drug information with 1 side effect.
　薬剤Ｄは、副作用期間１の副作用開始日前と、副作用期間１の改善期間開始日以降に処方されている。このとき、副作用原因薬剤情報更新処理（図２６）において、副作用後除外条件として「副作用後分析対象区間内に１回以上処方があり、副作用後分析対象区間内の副作用期間における副作用原因薬剤情報に１が登録されている回数が０回である場合」が設定されている場合を考える。副作用期間１の副作用後分析対象区間内に副作用期間２が含まれており、副作用期間２の副作用開始日より前のみに薬剤Ｄが処方されていることから、副作用期間２における薬剤Ｄの副作用原因薬剤情報には「１」が記録される。そのため、薬剤Ｄは副作用後除外条件に該当しないため、副作用期間１における薬剤Ｄの副作用原因薬剤情報には「１」が記録される。 Drug D is prescribed before the side effect start date of side effect period 1 and after the start date of improvement period of side effect period 1. At this time, in the side effect-causing drug information update process (FIG. 26), the post-side effect exclusion condition is that “there is one or more prescriptions in the post-side effect analysis target section, Consider a case where “when the number of times 1 is registered is 0” is set. The side effect period 2 is included in the post-side effect analysis target section of the side effect period 1, and the drug D is prescribed only before the start date of the side effect in the side effect period 2, so the side effect cause of the drug D in the side effect period 2 “1” is recorded in the medicine information. Therefore, since the drug D does not meet the post-side effect exclusion condition, “1” is recorded in the side effect cause drug information of the drug D in the side effect period 1.
　薬剤Ｅは、副作用期間１の副作用開始日前と、副作用期間１の改善期間終了日以降に処方されている。薬剤Ｄと同様に、副作用原因薬剤情報更新処理（図２６）において、副作用後除外条件として「副作用後分析対象区間内に１回以上処方があり、副作用後分析対象区間内の副作用期間における副作用原因薬剤情報に１が登録されている回数が０回である場合」が設定されている場合を考える。副作用期間１の副作用後分析対象区間内に副作用期間２が含まれているが、薬剤Ｅは副作用期間２の副作用の改善期間開始日以降に処方されていることから、副作用期間２における薬剤リストの中には薬剤Ｅが含まれない。そのため、薬剤Ｅは副作用後除外条件に該当し、副作用期間１における薬剤Ｅの副作用原因薬剤情報には「０」が記録される。 Drug E is prescribed before the side effect start date of side effect period 1 and after the end date of improvement period of side effect period 1. As in the case of the drug D, in the side effect cause drug information update process (FIG. 26), the post-adverse effect exclusion condition is “there is one or more prescriptions in the post-side effect analysis target section, Consider a case where “when the number of times 1 is registered in the drug information is 0” is set. The side effect period 2 is included in the post-side effect analysis target section of the side effect period 1, but since the drug E is prescribed after the start date of the side effect improvement period of the side effect period 2, the drug list in the side effect period 2 Drug E is not included in the inside. Therefore, the drug E corresponds to the post-side effect exclusion condition, and “0” is recorded in the side effect cause drug information of the drug E in the side effect period 1.
　そのため、副作用原因薬剤候補表示エリア３１０８には薬剤Ａと薬剤Ｄが表示される。 Therefore, drug A and drug D are displayed in the side effect cause drug candidate display area 3108.
　このように、第２実施例の副作用分析支援システムは、第１副作用の終了後に発生した第２副作用の副作用期間と、第１副作用の終了後に処方されている薬剤の処方期間とを用いて、処方されている薬剤のうち、第２副作用の改善期間の後に継続して処方されている薬剤以外の薬剤を、第２副作用と関係する薬剤として選択するので、副作用の再現性が分析可能となり、副作用原因薬剤情報を高精度に算出することができる。 Thus, the side effect analysis support system of the second embodiment uses the side effect period of the second side effect that occurred after the end of the first side effect, and the prescription period of the drug prescribed after the end of the first side effect, Among the prescribed drugs, drugs other than those prescribed continuously after the second side effect improvement period are selected as drugs related to the second side effect, so the reproducibility of the side effects can be analyzed, Side-effect-causing drug information can be calculated with high accuracy.
　＜実施例３＞
　次に、本発明の第３実施例について説明する。 <Example 3>
Next, a third embodiment of the present invention will be described.
　図１を参照して、第３実施例の副作用分析支援システムの構成を説明する。 Referring to FIG. 1, the configuration of the side effect analysis support system of the third embodiment will be described.
　第３実施例の副作用分析支援システムは、症例毎の個々の薬剤の副作用、効果、薬剤及び副作用別の診療コスト、薬剤別の予測診療コストを分析し、その結果を可視化して出力する計算機システムであり、病院情報システム１２０と、ネットワーク１４０と、入出力端末１３０とによって構成される。なお、第３実施例において、前述した第１又は第２実施例と異なる部分のみを説明し、第１又は第２実施例と同じ構成及び機能には同じ符号を付し、それらの説明は省略する。 The side effect analysis support system of the third embodiment is a computer system that analyzes side effects, effects, medical costs for each drug and side effects, predicted medical costs for each drug, and visualizes and outputs the results. The hospital information system 120, the network 140, and the input / output terminal 130 are included. In the third embodiment, only the parts different from the first or second embodiment described above will be described, and the same configurations and functions as those in the first or second embodiment will be denoted by the same reference numerals, and the description thereof will be omitted. To do.
　第３実施例のデータサーバ１００は、第１実施例の構成の他、診療コスト算出部１５１を有する。 The data server 100 of the third embodiment has a medical cost calculation unit 151 in addition to the configuration of the first embodiment.
　診療コスト算出部１５１は、データサーバ１００の機能を実現するための処理を実行する処理部であり、専用のハードウェアによって実現されてもよいし、ソフトウェアによって実現されてもよい。ソフトウェアによって各処理部を実現する場合、以下の説明において、診療コスト算出部１５１が実行する処理は、実際には、制御部１０１がメモリ１０３に格納されたプログラムに記述された命令に従って実行する。診療コスト算出部１５１によって実行される処理の詳細については後述する。 The medical cost calculation unit 151 is a processing unit that executes processing for realizing the function of the data server 100, and may be realized by dedicated hardware or software. When each processing unit is realized by software, in the following description, the processing executed by the medical cost calculation unit 151 is actually executed by the control unit 101 in accordance with an instruction described in a program stored in the memory 103. Details of processing executed by the medical cost calculation unit 151 will be described later.
　第３実施例の病院情報システム１２０は、症例データベース１２１、検査情報データベース１２２、処方情報データベース１２３及び診療行為情報データベース１２４を含む。これらのデータベースは、例えば、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）のような、病院情報システム１２０内の別の記憶装置（図示省略）に格納されてもよい。診療行為情報データベース１２４は、診療行為コスト情報テーブル３３００（図２８参照）と、処置情報テーブル３５００（図２９参照）とを含む。これらのテーブルの構成は、各図を用いて後述する。また、前述したように、診療行為情報データベース１２４は、薬剤名と対応疾患との関係を記録するテーブルを含んでもよい。 The hospital information system 120 of the third embodiment includes a case database 121, an examination information database 122, a prescription information database 123, and a medical practice information database 124. These databases may be stored in another storage device (not shown) in the hospital information system 120 such as an HDD (Hard Disk Drive). The medical practice information database 124 includes a medical practice cost information table 3300 (see FIG. 28) and a treatment information table 3500 (see FIG. 29). The configuration of these tables will be described later with reference to each drawing. Further, as described above, the medical practice information database 124 may include a table that records the relationship between drug names and corresponding diseases.
　以下では、統合データベース１１６を構成するテーブルの構造を説明する。第３実施例の統合データベース１１６は、症例毎の基本情報等を管理する症例情報テーブル２００（図２）と、症例毎の処方情報を記録する処方情報テーブル３００（図３）と、症例毎の検査情報を格納する検査情報テーブル４００（図４）と、診療行為コスト情報テーブル３３００（図２８）と、処置情報テーブル３５００（図２９）とで構成される。また、統合データベース１１６は、薬剤名と対応疾患との関係を記録する薬剤対応疾患情報テーブルを含んでもよい。 Hereinafter, the structure of the table constituting the integrated database 116 will be described. The integrated database 116 of the third embodiment includes a case information table 200 (FIG. 2) for managing basic information for each case, a prescription information table 300 (FIG. 3) for recording prescription information for each case, The examination information table 400 (FIG. 4) for storing examination information, a medical practice cost information table 3300 (FIG. 28), and a treatment information table 3500 (FIG. 29). The integrated database 116 may also include a drug-compatible disease information table that records the relationship between drug names and corresponding diseases.
　図２８は、診療行為コスト情報テーブル３３００の構成例を示す図である。 FIG. 28 is a diagram showing a configuration example of the medical practice cost information table 3300.
　診療行為コスト情報テーブル３３００は、診療行為情報データベース１２４から取得した診療行為のコストレコードを格納するテーブルであり、診療行為３３０１、種別３３０２及びコスト３３０３のフィールドを含む。診療行為３３０１は診療行為の名称であり、診療行為毎に診療行為コスト情報テーブル３３００のレコードが記録される。種別３３０２は、当該診療行為の種別である。コスト３３０３は、当該診療行為コストである。例えば、図２８に示す診療行為コスト情報テーブル３３００のレコード３３００Ａは、診療行為として、薬剤Ａが処方されており、薬剤Ａのコストが１０００であることを示す。 The medical practice cost information table 3300 is a table for storing a cost record of a medical practice acquired from the medical practice information database 124, and includes fields of a medical practice 3301, a type 3302, and a cost 3303. The medical practice 3301 is a name of the medical practice, and a record of the medical practice cost information table 3300 is recorded for each medical practice. The type 3302 is a type of the medical practice. The cost 3303 is the medical practice cost. For example, the record 3300A of the medical practice cost information table 3300 shown in FIG. 28 indicates that the medicine A is prescribed as the medical practice and the cost of the medicine A is 1000.
　図２９は、処置情報テーブル３５００の構成例を示す図である。 FIG. 29 is a diagram showing a configuration example of the treatment information table 3500.
　処置情報テーブル３５００は、診療行為情報データベース１２４から取得した処置レコードを格納するテーブルであり、患者ＩＤ３５０１、処置名３５０２及び実施日３５０３のフィールドを含む。患者ＩＤ３５０１は、患者を一意に識別するための識別情報である。処置名３５０２は、当該患者に実施した処置の名前である。実施日３５０３は、当該患者に処置を実施した日である。例えば、図２９に示す処置情報テーブル３５００のレコード３５００Ａは、患者ＩＤが「＃＃＃１」の患者に、手術Ｚの処置が２０１４年４月３日に実施されたことを示す。 The treatment information table 3500 is a table for storing treatment records acquired from the medical practice information database 124, and includes fields for a patient ID 3501, a treatment name 3502, and an implementation date 3503. The patient ID 3501 is identification information for uniquely identifying a patient. The treatment name 3502 is the name of the treatment performed on the patient. An implementation date 3503 is a date when treatment is performed on the patient. For example, the record 3500A of the treatment information table 3500 illustrated in FIG. 29 indicates that the treatment of the operation Z was performed on April 3, 2014 for the patient whose patient ID is “## 1”.
　以下では、副作用分析データベース１１８を構成するテーブルの構造を説明する。第３実施例の副作用分析データベース１１８は、副作用症例テーブル９００（図９）と、副作用薬剤テーブル１０００（図１０）と、薬効情報テーブル１１００（図１１）と、副作用リスクテーブル１２００（図１２）と、薬効評価テーブル１３００（図１３）と、診療コスト算出部１５１が算出した副作用別の平均診療コストを格納する副作用別診療コストテーブル３７００（図３０）と、診療コスト算出部１５１が算出した薬剤別の診療コストを格納する薬剤別診療コストテーブル３８００（図３１）とで構成される。 Hereinafter, the structure of the table constituting the side effect analysis database 118 will be described. The side effect analysis database 118 of the third embodiment includes a side effect case table 900 (FIG. 9), a side effect drug table 1000 (FIG. 10), a medicinal effect information table 1100 (FIG. 11), and a side effect risk table 1200 (FIG. 12). The medicinal effect evaluation table 1300 (FIG. 13), the side-by-side treatment cost table 3700 (FIG. 30) storing the average side-by-side treatment cost calculated by the medical cost calculation unit 151, and the medicine by the medical cost calculation unit 151 And a medical treatment cost table 3800 (FIG. 31) for storing medical costs.
　図３０は、副作用別診療コストテーブル３７００の構成例を示す図である。 FIG. 30 is a diagram showing a configuration example of the side effect medical cost table 3700.
　副作用別診療コストテーブル３７００は、疾患名３７０１、副作用詳細３７０２、副作用有無３７０３、患者数３７０４及び平均診療コスト（副作用）３７０５のフィールドを含む。疾患名３７０１は、疾患の名称である。副作用詳細３７０２は、副作用の詳細な情報である。副作用有無３７０３は、副作用の有り又は無しを示す情報である。患者数は、疾患名３７０１と副作用詳細３７０２と副作用有無３７０３との組に該当する患者の数である。平均診療コスト（副作用）３７０５は、診療コストの副作用毎の平均値である。 The side effect medical cost table 3700 includes fields of disease name 3701, side effect details 3702, side effect presence / absence 3703, number of patients 3704, and average medical cost (side effect) 3705. The disease name 3701 is the name of the disease. The side effect details 3702 are detailed information on side effects. The side effect presence / absence 3703 is information indicating the presence or absence of a side effect. The number of patients is the number of patients corresponding to the set of disease name 3701, side effect details 3702, and side effect presence / absence 3703. The average medical cost (side effect) 3705 is an average value for each side effect of the medical cost.
　例えば、図３０に示す副作用別診療コストテーブル３７００のレコード３７００Ａは、糖尿病を診断されている患者のうち、ＡＬＴ（ＧＰＴ）が上昇する副作用が発生していない人数は１２０名であり、この１２０名の診療コストの平均は１４３８０であることを示す。 For example, the record 3700A of the side-by-side treatment cost table 3700 shown in FIG. 30 shows that among the patients diagnosed with diabetes, there are 120 people who have no side effect of increasing ALT (GPT). This shows that the average medical cost is 14380.
　図３１は、薬剤別診療コストテーブル３８００の構成例を示す図である。 FIG. 31 is a diagram showing a configuration example of the medical treatment cost table 3800 for each medicine.
　薬剤別診療コストテーブル３８００は、疾患名３８０１、主薬剤名３８０２、副作用詳細３８０３、副作用有無３８０４、患者数３８０５、予測／平均フラグ３８０６及び診療コスト（薬剤）３８０７のフィールドを含む。疾患名３８０１は、疾患の名称である。主薬剤名は、当該疾患に主に処方される薬剤の名称である。副作用詳細３８０３は、副作用の詳細な情報である。副作用有無３８０４は、副作用の有り又は無しを示す情報である。患者数３８０５は、疾患名３８０１と主薬剤名３８０２と副作用詳細３８０３と副作用有無３８０４との組に該当する患者の数である。予測／平均フラグ３８０６は、診療コストが平均値であるか予測値であるかを示す情報である。診療コスト（薬剤）３８０７は、診療コストの薬剤毎の平均値又は予測値である。 The medical treatment cost table 3800 by drug includes fields of disease name 3801, main drug name 3802, side effect details 3803, presence / absence of side effects 3804, number of patients 3805, prediction / average flag 3806, and medical cost (drug) 3807. The disease name 3801 is the name of the disease. The main drug name is a name of a drug mainly prescribed for the disease. The side effect details 3803 is detailed information on side effects. Side effect presence / absence 3804 is information indicating the presence or absence of a side effect. The number of patients 3805 is the number of patients corresponding to the set of disease name 3801, main drug name 3802, side effect details 3803, and side effect presence / absence 3804. The prediction / average flag 3806 is information indicating whether the medical cost is an average value or a prediction value. The medical cost (medicine) 3807 is an average value or a predicted value for each medical cost.
　例えば、図３１に示す薬剤別診療コストテーブル３８００のレコード３８００Ａは、糖尿病と診断されている患者のうち、薬剤Ａが主に処方され、ＡＬＴ（ＧＰＴ）が上昇する副作用が発生していない人数は４０名であり、この４０人の診療コストの平均値が１２５００であることを示す。また、レコード３９００Ａは、糖尿病と診断されている患者のうち、薬剤Ａを処方した場合の診療コストの予測値は１３８４１であることを示す。 For example, the record 3800A of the medical treatment cost table 3800 shown in FIG. 31 shows that among the patients diagnosed with diabetes, the number of patients who are prescribed mainly for the drug A and have no side effect of increasing ALT (GPT). It shows that the average value of the medical costs of 40 people is 12,500. Further, the record 3900A indicates that, among patients diagnosed with diabetes, the predicted value of the medical cost when the medicine A is prescribed is 13841.
　次に、第３実施例の制御部１０１が診療コストを算出する処理を説明する。 Next, a process in which the control unit 101 according to the third embodiment calculates medical costs will be described.
　図３２は、第３実施例の制御部１０１が診療コストを算出する処理のフローチャートである。 FIG. 32 is a flowchart of processing in which the control unit 101 according to the third embodiment calculates medical costs.
　まず、制御部１０１は、統合データベース１１６から症例データを読み出し、メモリ１０３に格納する（Ｓ４００１）。以下では、症例に関するデータ（例えば、症例を識別する情報、症例に対応する処方薬の情報、症例に対応する検査結果の情報等）を総称して症例データと記載する。制御部１０１は、統合データベース１１６に含まれる全てのデータを読み出し、メモリ１０３に格納してもよい。 First, the control unit 101 reads out case data from the integrated database 116 and stores it in the memory 103 (S4001). Hereinafter, data relating to cases (for example, information for identifying cases, information on prescription drugs corresponding to cases, information on test results corresponding to cases, etc.) will be collectively referred to as case data. The control unit 101 may read all data included in the integrated database 116 and store it in the memory 103.
　次に、制御部１０１は、副作用分析データベース１１８から副作用データを読み出し、メモリ１０３に格納する（Ｓ４００２）。例えば、制御部１０１は、副作用分析データベース１１８に含まれる全てのデータを読み出し、メモリ１０３に格納してもよい。 Next, the control unit 101 reads side effect data from the side effect analysis database 118 and stores it in the memory 103 (S4002). For example, the control unit 101 may read all data included in the side effect analysis database 118 and store it in the memory 103.
　次に、制御部１０１は、知識データベース１１７から知識データを読み出し、メモリ１０３に格納する（Ｓ４００３）。例えば、制御部１０１は、知識データベース１１７に含まれる全てのデータを読み出し、メモリ１０３に格納してもよい。 Next, the control unit 101 reads knowledge data from the knowledge database 117 and stores it in the memory 103 (S4003). For example, the control unit 101 may read all the data included in the knowledge database 117 and store it in the memory 103.
　次に、制御部１０１は、診療コスト算出部１５１を起動する（Ｓ４００４）。 Next, the control unit 101 activates the medical cost calculation unit 151 (S4004).
　図３３は、診療コスト算出部１５１が平均診療コストを算出する処理のフローチャートである。 FIG. 33 is a flowchart of processing in which the medical cost calculation unit 151 calculates an average medical cost.
　まず、診療コスト算出部１５１は、症例情報テーブル２００のデータをメモリ１０３から読み出す（Ｓ４１０１）。 First, the medical cost calculation unit 151 reads the data of the case information table 200 from the memory 103 (S4101).
　次に、診療コスト算出部１５１は、症例情報テーブル２００の全ての患者ＩＤのレコードを取得し、全ての患者ＩＤについて処理が完了しているかを判定する（Ｓ４１０２）。その結果、一部の患者ＩＤについて処理が完了していなければ、患者ＩＤ毎にステップＳ４１０３からＳ４１０８の処理を繰り返し実行する。 Next, the medical cost calculation unit 151 acquires records of all patient IDs in the case information table 200, and determines whether processing has been completed for all patient IDs (S4102). As a result, if the processing has not been completed for some patient IDs, the processing from step S4103 to S4108 is repeatedly executed for each patient ID.
　次に、診療コスト算出部１５１は、ステップＳ４１０２で選択した患者ＩＤの入院年月日２０５と退院年月日２０６を全て取得し、入院年月日２０５と退院年月日２０６とのいずれも「０」が記録されていないレコード（すなわち、入院経験があり既に退院した患者の入院年月日、退院年月日及び疾患名）を選択し、入院年月日から退院年月日までの入院期間をメモリ１０３に格納する（Ｓ４１０３）。複数回入退院している患者では、複数の入院期間が選択される。 Next, the medical cost calculation unit 151 acquires all the hospitalization date 205 and the discharge date 206 of the patient ID selected in Step S4102, and both the hospitalization date 205 and the discharge date 206 are “ Select the record that is not recorded as “0” (that is, the hospitalization date, discharge date, and disease name of a patient who has been hospitalized and has been discharged), and the hospitalization period from the hospitalization date to the discharge date Is stored in the memory 103 (S4103). For patients who have been discharged and discharged multiple times, multiple hospital stays are selected.
　次に、診療コスト算出部１５１は、ステップＳ４１０３で選択した入院期間の各々の中で実施された処方、検査及び処置の情報の全てを処方情報テーブル３００と、検査情報テーブル４００と、処置情報テーブル３５００とから取得する（Ｓ４１０４）。 Next, the medical cost calculation unit 151 stores all information on the prescription, examination, and treatment performed in each hospitalization period selected in step S4103, the prescription information table 300, the examination information table 400, and the treatment information table. It is acquired from 3500 (S4104).
　次に、診療コスト算出部１５１は、メモリ１０３に格納された診療行為コスト情報テーブル３３００の全てのレコードを取得し、ステップＳ４１０４で取得した入院期間中に実施された処方、検査及び処置の情報と、診療行為コスト情報テーブル３３００に記録された診療行為に対応するコストとを用いて、各入院期間中における合計の診療コストを算出し、患者ＩＤと入院年月日と退院年月日と疾患名と算出した合計診療コストとを関連付けてメモリ１０３に設けた合計診療コストテーブル（図示省略）に格納する（Ｓ４１０５）。合計診療コストは、日本における診療報酬請求時に使用される保険点数を用いてもよいし、ヘルスケアプロバイダが定めたコストを用いてもよい。 Next, the medical cost calculation unit 151 acquires all records of the medical service cost information table 3300 stored in the memory 103, and information on prescriptions, examinations, and treatments performed during the hospitalization period acquired in step S4104. The total medical cost during each hospitalization period is calculated using the cost corresponding to the medical practice recorded in the medical practice cost information table 3300, and the patient ID, hospitalization date, discharge date, and disease name are calculated. And the calculated total medical cost are stored in a total medical cost table (not shown) provided in the memory 103 (S4105). The total medical cost may be the insurance score used when requesting medical fees in Japan, or may be a cost determined by a healthcare provider.
　次に、診療コスト算出部１５１は、メモリ１０３に格納された副作用症例テーブル９００の全てのレコードを取得し、ステップＳ４１０３で選択した入院年月日及び退院年月日から、入院期間中における副作用詳細の有無を、副作用詳細ごとに判定する（Ｓ４１０６）。 Next, the medical cost calculation unit 151 acquires all records of the side effect case table 900 stored in the memory 103, and details of side effects during the hospitalization period from the hospitalization date and discharge date selected in step S4103. Is determined for each side effect detail (S4106).
　次に、診療コスト算出部１５１は、メモリ１０３に格納された薬剤対応疾患情報テーブルの全てのレコードを取得し、ステップＳ４１０３で抽出した入院期間の疾患名と、入院期間中の処方の情報とを用いて、各入院期間中に主に処方された主薬剤を決定する（Ｓ４１０７）。入院期間中に疾患名に対応する複数の薬剤が処方されていた場合、入院期間中処方開始日が入院年月日に近い薬剤を主薬剤に決定してもよいし、入院期間中の処方回数が最も多い薬剤を主薬剤に決定してもよい。 Next, the medical cost calculation unit 151 acquires all the records of the drug-corresponding disease information table stored in the memory 103, and obtains the disease name of the hospitalization period extracted in step S4103 and the prescription information during the hospitalization period. Used to determine the main drug prescribed mainly during each hospital stay (S4107). If multiple drugs corresponding to the disease name were prescribed during the hospitalization period, the drug whose start date during hospitalization is close to the date of hospitalization may be determined as the main drug, and the number of prescriptions during the hospitalization period The drug with the largest number may be determined as the main drug.
　次に、診療コスト算出部１５１は、ステップＳ４１０５で算出した入院期間中の合計診療コストと、患者ＩＤと、入院年月日と、主薬剤名と、副作用詳細と、副作用の有無と、副作用詳細とを関連付けて、メモリ１０３に格納する（Ｓ４１０８）。そして、ステップＳ４１０２に戻り、次患者ＩＤについて処理をする。 Next, the medical cost calculation unit 151 calculates the total medical cost during the hospitalization period calculated in step S4105, the patient ID, the hospitalization date, the main drug name, the side effect details, the presence or absence of side effects, and the side effect details. Are stored in the memory 103 (S4108). Then, the process returns to step S4102, and the next patient ID is processed.
　ステップＳ４１０２において、全ての患者ＩＤについて処理が完了していると判定されれば、診療コスト算出部１５１は、メモリ１０３に格納された各患者ＩＤの各入院期間の合計診療コストと、副作用の有無と、副作用詳細とを用いて、疾患名と副作用有無と副作用詳細との組に該当する患者数を集計し、疾患名と副作用有無と副作用詳細との組の平均診療コスト（副作用）を算出し、疾患名と副作用の有無と副作用詳細と患者数との平均診療コスト（副作用）とを関連付けて副作用別診療コストテーブル３７００に格納する（Ｓ４１０９）。例えば、平均診療コスト（副作用）は、疾患名と副作用有無と副作用詳細との組に該当する患者の合計診療コストの総和を、疾患名と副作用有無と副作用詳細との組に該当する患者数で除することによって算出することができる。 If it is determined in step S 4102 that the processing has been completed for all the patient IDs, the medical cost calculation unit 151 determines whether the total medical costs for each hospitalization period of each patient ID stored in the memory 103 and the presence or absence of side effects. And the side effect details, the total number of patients corresponding to the combination of disease name, presence / absence of side effect and side effect details is calculated, and the average medical treatment cost (side effect) of the set of disease name, side effect presence / absence and side effect details is calculated. The disease name, the presence / absence of side effects, the side effect details, and the average medical cost (side effects) of the number of patients are associated and stored in the side-by-side clinical cost table 3700 (S4109). For example, the average medical treatment cost (side effects) is the sum of the total medical treatment costs of patients who fall under the combination of disease name, presence / absence of side effects and side effect details, and the number of patients who fall under the combination of disease name, presence / absence of side effects and side effect details. It can be calculated by dividing.
　次に、診療コスト算出部１５１は、メモリ１０３に格納された患者ＩＤ及び入院期間の組の合計診療コストと、副作用の有無と、主薬剤とを用いて、疾患名と主薬剤と副作用詳細と副作用の有無との組に該当する患者数を集計し、平均診療コスト（薬剤）を算出し、疾患名と主薬剤と副作用詳細と副作用の有無と患者数と平均診療コスト（薬剤）とを関連付けて、薬剤別診療コストテーブル３８００に格納する（Ｓ４１１０）。例えば、平均診療コスト（薬剤）は、疾患名と副作用有無と副作用詳細と主薬剤との組に該当する患者の合計診療コストの総和を、疾患名と副作用有無と疾患名との組に該当する患者数で除することによって算出することができる。 Next, the medical cost calculation unit 151 uses the total medical cost of the set of the patient ID and hospitalization period stored in the memory 103, the presence / absence of side effects, and the main drug, and the disease name, main drug, and side effect details, Calculate the average medical cost (drug) by counting the number of patients that fall under the group of the presence or absence of side effects, and correlate the disease name, main drug, side effect details, the presence or absence of side effects, the number of patients, and the average medical cost (drug) And stored in the medical treatment cost table 3800 for each medicine (S4110). For example, the average medical cost (drug) is the sum of the total medical costs of patients who fall under the combination of disease name, presence / absence of side effect, detailed side effect and main drug, and falls under the combination of disease name, presence / absence of side effect and disease name It can be calculated by dividing by the number of patients.
　次に、診療コスト算出部１５１は、疾患名と、主薬剤と、副作用詳細と、副作用の有無と、該当する患者数と、平均診療コスト（薬剤）とを用いて、疾患名と主薬剤に対応する予測診療コストを算出し、疾患名と主薬剤と予測診療コストとを関連付けて、薬剤別診療コストテーブル３８００に格納する（Ｓ４１１１）。例えば、疾患名と主薬剤との組に対応する予測診療コストは、数式（３）で算出することができる。 Next, the medical cost calculation unit 151 uses the disease name, main drug, side effect details, presence / absence of side effects, number of corresponding patients, and average medical cost (drug) to determine the disease name and main drug. A corresponding predicted medical cost is calculated, and the disease name, main drug, and predicted medical cost are associated with each other and stored in the drug-specific medical cost table 3800 (S4111). For example, a predicted medical cost corresponding to a combination of a disease name and a main drug can be calculated by Equation (3).
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
　次に、制御部１０１は、表示画面生成部１０５を起動する。表示画面生成部１０５は、ステップＳ４００４で算出された診療コストに基づいて、分析結果を出力する画面を生成する（Ｓ４００５）。 Next, the control unit 101 activates the display screen generation unit 105. The display screen generation unit 105 generates a screen for outputting the analysis result based on the medical cost calculated in step S4004 (S4005).
　図３４は、ユーザが統計解析結果を参照するときに入出力端末１３０が表示する統計解析結果参照画面４２００の例を示す図である。 FIG. 34 is a diagram showing an example of a statistical analysis result reference screen 4200 displayed by the input / output terminal 130 when the user refers to the statistical analysis result.
　統計解析結果参照画面４２００は、疾患選択欄４２０１と、分析結果一覧表示エリア４２０２、分析結果グラフ１の表示エリア４２０３と、分析結果グラフ１のＸ軸選択欄４２０４と、分析結果グラフ１のＹ軸選択欄４２０５と、分析結果グラフ２の表示エリア４２０６と、分析結果グラフ２のＸ軸選択欄４２０７と、分析結果グラフ２のＹ軸選択欄４２０８とを含む。 The statistical analysis result reference screen 4200 includes a disease selection column 4201, an analysis result list display area 4202, a display area 4203 of the analysis result graph 1, an X-axis selection column 4204 of the analysis result graph 1, and a Y-axis of the analysis result graph 1. A selection field 4205, an analysis result graph 2 display area 4206, an analysis result graph 2 X-axis selection field 4207, and an analysis result graph 2 Y-axis selection field 4208 are included.
　ユーザは、疾患選択欄４２０１のドロップダウンボックスを操作し、ドロップダウンボックス（図示省略）に表示される複数の疾患名から分析結果一覧表示エリア４２０２に分析結果を表示する疾患を選択する。 The user operates a drop-down box in the disease selection field 4201 to select a disease whose analysis result is displayed in the analysis result list display area 4202 from a plurality of disease names displayed in the drop-down box (not shown).
　分析結果一覧表示エリア４２０２は、薬剤別診療コストテーブル３８００から取得した、疾患選択欄４２０１で選択された疾患のデータを表示する。 The analysis result list display area 4202 displays the data of the disease selected in the disease selection column 4201 acquired from the medical treatment cost table 3800 for each drug.
　ユーザは、分析結果グラフ１のＸ軸選択欄４２０４及びＹ軸選択欄４２０５を操作することによって、分析結果グラフ１の表示エリア４２０３に表示するグラフを変更する。分析結果グラフ１の表示エリア４２０３は、副作用別診療コストテーブル３７００と、薬剤別診療コストテーブル３８００とから取得した、疾患選択欄４２０１で選択された疾患のデータを、分析結果グラフ１のＸ軸選択欄４２０４及びＹ軸選択欄４２０５で選択された軸のグラフ上に表示する。 The user changes the graph displayed in the display area 4203 of the analysis result graph 1 by operating the X axis selection column 4204 and the Y axis selection column 4205 of the analysis result graph 1. The analysis result graph 1 display area 4203 displays the data of the disease selected in the disease selection column 4201 acquired from the side-by-side treatment cost table 3700 and the medicine-by-medical treatment cost table 3800, and the X-axis selection in the analysis result graph 1 Displayed on the graph of the axis selected in the column 4204 and the Y-axis selection column 4205.
　ユーザは、分析結果グラフ２のＸ軸選択欄４２０７及びＹ軸選択欄４２０８を操作することによって、分析結果グラフ２の表示エリア４２０６に表示するグラフを変更する。分析結果グラフ２の表示エリア４２０６は、副作用別診療コストテーブル３７００と、薬剤別診療コストテーブル３８００とから取得した、疾患選択欄４２０１で選択された疾患のデータを、分析結果グラフ２のＸ軸選択欄４２０７及びＹ軸選択欄４２０８で選択された軸のグラフ上に表示する。 The user changes the graph displayed in the display area 4206 of the analysis result graph 2 by operating the X axis selection column 4207 and the Y axis selection column 4208 of the analysis result graph 2. The analysis result graph 2 display area 4206 displays the data of the disease selected in the disease selection column 4201 acquired from the side-by-side medical treatment cost table 3700 and the medicine-by-medical medical cost table 3800, and the X-axis selection of the analysis result graph 2 Displayed on the graph of the axis selected in the column 4207 and the Y-axis selection column 4208.
　図３４に示す統計解析結果参照画面４２００では、二つの分析結果グラフを表示するが、三つ以上の分析結果グラフを表示してもよい。 In the statistical analysis result reference screen 4200 shown in FIG. 34, two analysis result graphs are displayed, but three or more analysis result graphs may be displayed.
　例えば、図３４に示す統計解析結果参照画面４２００では、疾患が「糖尿病」の平均診療コスト及び予測診療コストが分析結果一覧表示エリア４２０２に表示されており、薬剤Ａの予測診療コストが薬剤Ｓの予測診療コストより小さいことが表示されている。 For example, in the statistical analysis result reference screen 4200 shown in FIG. 34, the average medical cost and the predicted medical cost of the disease “diabetes” are displayed in the analysis result list display area 4202, and the predicted medical cost of the drug A is the drug S. It is displayed that it is smaller than the predicted medical cost.
　また、分析結果グラフ１の表示エリア４２０３には、Ｘ軸が「薬効」、Ｙ軸が「予測診療コスト」であるグラフが表示されており、薬剤Ａは、薬剤Ｓより薬効が低く、予測診療コストが低いことが表示されている。 In addition, in the display area 4203 of the analysis result graph 1, a graph with the “medicinal effect” on the X axis and the “predicted medical cost” on the Y axis is displayed. It is displayed that the cost is low.
　また、分析結果グラフ２の表示エリア４２０６には、Ｘ軸が「薬効」、Ｙ軸が「副作用」であるグラフが表示されている。薬剤Ａは、薬剤Ｓより薬効が低いが、副作用リスクも低いことが表示されている。 In addition, in the display area 4206 of the analysis result graph 2, a graph in which the X-axis is “medicinal effect” and the Y-axis is “side effect” is displayed. Although drug A is less effective than drug S, it is indicated that the risk of side effects is also low.
　このように、第３実施例の副作用分析支援システムは、入院期間中の患者の診療行為の費用を合計して合計診療コストを算出し、合計診療コストを副作用毎に合計して、当該合計値を当該副作用毎の患者数で除することによって診療コスト（副作用）を算出し、合計診療コストを副作用及び薬剤の組毎に合計して、当該合計値を当該副作用及び薬剤毎の患者数で除することによって診療コスト（薬剤）を算出するので、薬剤の処方による予測診療コスト、薬効及び副作用を統合的に表示することができる。このため、これらの情報を総合的に考慮しながら、使用する薬剤を選択することが可能となる。 As described above, the side effect analysis support system of the third example calculates the total medical cost by totaling the cost of the medical treatment of the patient during the hospitalization period, sums the total medical cost for each side effect, and calculates the total value. Is divided by the number of patients for each side effect, and the total cost of treatment is calculated for each side effect and drug group, and the total value is divided by the number of patients for each side effect and drug. Thus, since the medical cost (drug) is calculated, the predicted medical cost, drug efficacy, and side effects due to the prescription of the drug can be displayed in an integrated manner. For this reason, it becomes possible to select the medicine to be used while comprehensively considering these pieces of information.
　また、分析結果グラフ２のＸ軸選択欄４２０７で「危険処方日数」を選択すると、副作用リスク及び危険処方日数との関係を表示することができ、予測診療コストが低い薬剤を始めに処方し、危険処方日数に達した後に、副作用リスクが低い薬剤に切り替えるなどの、診療コスト及び副作用リスクの両方を考慮した投薬治療計画を作成することができる。 In addition, by selecting “dangerous prescription days” in the X-axis selection field 4207 of the analysis result graph 2, it is possible to display the relationship between the risk of side effects and the number of days of dangerous prescriptions, prescribing drugs with low predicted medical costs first, After reaching the risk prescription days, a medication treatment plan that considers both the cost of treatment and the risk of side effects, such as switching to a drug with a low risk of side effects, can be created.
　以上に説明したように、本発明の各実施例の副作用分析支援システムでは、多種の薬剤の副作用リスクと危険処方日数と薬効評価と予測診療コストを同時に可視化し、適切な投薬治療を支援する情報システムを提供することができる。 As described above, in the side effect analysis support system of each embodiment of the present invention, information that supports the appropriate medication treatment by simultaneously visualizing the side effect risk of various drugs, the risk prescription days, the drug efficacy evaluation, and the predicted medical cost. A system can be provided.
　なお、本発明は前述した実施例に限定されるものではなく、添付した特許請求の範囲の趣旨内における様々な変形例及び同等の構成が含まれる。例えば、前述した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに本発明は限定されない。また、ある実施例の構成の一部を他の実施例の構成に置き換えてもよい。また、ある実施例の構成に他の実施例の構成を加えてもよい。また、各実施例の構成の一部について、他の構成の追加・削除・置換をしてもよい。 The present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the scope of the appended claims. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to those having all the configurations described. A part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Moreover, you may add the structure of another Example to the structure of a certain Example. In addition, for a part of the configuration of each embodiment, another configuration may be added, deleted, or replaced.
　また、前述した各構成、機能、処理部、処理手段等は、それらの一部又は全部を、例えば集積回路で設計する等により、ハードウェアで実現してもよく、プロセッサがそれぞれの機能を実現するプログラムを解釈し実行することにより、ソフトウェアで実現してもよい。 In addition, each of the above-described configurations, functions, processing units, processing means, etc. may be realized in hardware by designing a part or all of them, for example, with an integrated circuit, and the processor realizes each function. It may be realized by software by interpreting and executing the program to be executed.
　各機能を実現するプログラム、テーブル、ファイル等の情報は、メモリ、ハードディスク、ＳＳＤ（Solid State Drive）等の記憶装置、又は、ＩＣカード、ＳＤカード、ＤＶＤ等の記録媒体に格納することができる。 Information such as programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, and an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, and a DVD.
　また、制御線や情報線は説明上必要と考えられるものを示しており、実装上必要な全ての制御線や情報線を示しているとは限らない。実際には、ほとんど全ての構成が相互に接続されていると考えてよい。 Also, the control lines and information lines indicate what is considered necessary for the explanation, and do not necessarily indicate all control lines and information lines necessary for mounting. In practice, it can be considered that almost all the components are connected to each other.

Claims (15)

  1. 　プロセッサと、前記プロセッサに接続される記憶装置とを備える分析システムであって、
    　前記記憶装置は、患者の疾患及び入院期間を含む症例情報と、前記患者への薬剤の処方期間を含む処方情報と、前記患者の検査結果を含む検査情報と、前記検査結果に基づいて副作用が発生しているかを判定するための判定条件を含む知識情報とを保持し、
    　前記プロセッサは、
    　前記知識情報を参照して、前記検査情報に含まれる検査結果の変動から副作用の改善期間を算出し、
    　前記処方期間を前記処方情報から取得し、
    　前記処方されている薬剤のうち、前記改善期間の開始後に継続して処方されている薬剤以外の薬剤が前記副作用と関係することを示す副作用原因薬剤情報を算出し、
    　前記副作用と関係する薬剤の情報を出力することを特徴とする分析システム。     An analysis system comprising a processor and a storage device connected to the processor,
    The storage device has side effects based on case information including a patient's disease and hospitalization period, prescription information including a prescription period of a drug to the patient, test information including a test result of the patient, and the test result. Holding knowledge information including judgment conditions for judging whether it has occurred,
    The processor is
    With reference to the knowledge information, the side effect improvement period is calculated from the variation of the test result included in the test information,
    Obtaining the prescription period from the prescription information;
    Of the prescription drugs, calculate side effect causative drug information indicating that a drug other than a prescription drug continuously prescribed after the start of the improvement period is related to the side effect,
    An analysis system for outputting information on a drug related to the side effect.
  2. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、前記知識情報を参照して、前記副作用と同じ症状が発生する疾患の患者を除外して、前記副作用の改善期間を算出することを特徴とする分析システム。     The analysis system according to claim 1,
    The analysis system is characterized in that the processor refers to the knowledge information and excludes patients with diseases in which the same symptoms as the side effects occur, and calculates an improvement period of the side effects.
  3. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、
    　前記薬剤の処方開始日を前記処方情報から取得し、
    　前記知識情報を参照して、前記検査情報に含まれる検査結果の変動から副作用開始日及び副作用終了日を算出し、
    　前記薬剤の処方開始日と前記副作用開始日との差を算出し、
    　前記算出された差を統計処理することによって、複数の症例において、前記算出された差を代表する値を決定し、
    　前記決定された値を危険処方日数として出力することを特徴とする分析システム。     The analysis system according to claim 1,
    The processor is
    Obtaining the prescription start date of the drug from the prescription information;
    With reference to the knowledge information, the side effect start date and the side effect end date are calculated from the variation of the test result included in the test information,
    Calculate the difference between the drug prescription start date and the side effect start date,
    By statistically processing the calculated difference, in a plurality of cases, to determine a value representative of the calculated difference,
    An analysis system, characterized in that the determined value is output as a risk prescription day.
  4. 　請求項３に記載の分析システムであって、
    　前記プロセッサは、前記算出された副作用開始日及び副作用終了日によって特定される副作用期間が複数見出される場合、前記副作用期間の回数毎に前記副作用原因薬剤情報及び前記改善期間を算出することを特徴とする分析システム。     The analysis system according to claim 3,
    The processor, when a plurality of side effect periods identified by the calculated side effect start date and side effect end date is found, calculates the side effect cause drug information and the improvement period for each number of the side effect periods. Analysis system.
  5. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、
    　前記副作用期間中に検査値が単調減少している期間及び単調増加している期間を抽出し、
    　前記抽出された期間のうち、一つの副作用期間中の最後の期間を前記改善期間とすることを特徴とする分析システム。     The analysis system according to claim 1,
    The processor is
    Extract the period during which the test value monotonously decreases and the period during which the test value monotonously increases during the side effect period,
    Of the extracted periods, the last period in one side effect period is set as the improvement period.
  6. 　請求項１に記載の分析システムであって、
    　前記知識情報は、前記薬剤の既知の副作用の情報を含み、
    　前記プロセッサは、前記既知の副作用を発生させる薬剤を除外して、副作用と関係する薬剤を選択することを特徴とする分析システム。     The analysis system according to claim 1,
    The knowledge information includes information on known side effects of the drug,
    The analysis system, wherein the processor excludes drugs that cause the known side effects and selects a drug related to the side effects.
  7. 　請求項１に記載の分析システムであって、
    　前記知識情報は、副作用と関係するかが判定される薬剤が処方されていた期間である分析期間の情報を含み、
    　前記プロセッサは、前記副作用開始日より前の前記分析期間の間に処方された薬剤の中から副作用と関係する薬剤を選択することを特徴とする分析システム。     The analysis system according to claim 1,
    The knowledge information includes information on an analysis period that is a period during which a drug to be determined whether it is related to side effects is prescribed,
    The analysis system, wherein the processor selects a drug related to a side effect from drugs prescribed during the analysis period before the start date of the side effect.
  8. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、複数の患者の前記副作用原因薬剤情報と前記薬剤の使用症例数とを統計処理することによって、当該薬剤の副作用リスクを算出することを特徴とする分析システム。     The analysis system according to claim 1,
    The said processor calculates the side effect risk of the said drug by carrying out the statistical process of the said side effect cause drug information and the number of use cases of the said drug of several patients.
  9. 　請求項８に記載の分析システムであって、
    　前記プロセッサは、
    　前記知識情報を参照して、前記検査情報に含まれる検査結果の変動から副作用開始日を算出し、
    　前記薬剤の処方終了日から前記副作用開始日までの経過日数を算出し、
    　前記算出された経過日数が最小の薬剤について、前記副作用リスクを算出することを特徴とする分析システム。     The analysis system according to claim 8,
    The processor is
    Referring to the knowledge information, the side effect start date is calculated from the variation of the test result included in the test information,
    Calculate the elapsed days from the prescription end date of the drug to the side effect start date,
    The analysis system characterized in that the side effect risk is calculated for the drug having the minimum calculated elapsed days.
  10. 　請求項９に記載の分析システムであって、
    　前記プロセッサは、前記経過日数が負である場合、当該経過日数を０日とすることを特徴とする分析システム。     The analysis system according to claim 9, wherein
    If the elapsed days are negative, the processor sets the elapsed days to 0 days.
  11. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、
    　前記薬剤が処方されている期間を前記処方情報から取得し、
    　前記副作用の改善期間と前記処方期間とを同じ時系列に表示するための画面データを出力することを特徴とする分析システム。     The analysis system according to claim 1,
    The processor is
    Obtaining from the prescription information the period during which the drug is prescribed;
    An analysis system for outputting screen data for displaying the improvement period of the side effect and the prescription period in the same time series.
  12. 　請求項１に記載の分析システムであって、
    　前記プロセッサは、第１副作用の終了後に発生した第２副作用の副作用期間と、前記第１副作用の終了後に処方されている薬剤の処方期間とを用いて、前記処方されている薬剤のうち、前記第２副作用の改善期間の後に継続して処方されている薬剤以外の薬剤を、前記第２副作用と関係する薬剤として選択することを特徴とする分析システム。     The analysis system according to claim 1,
    The processor uses the side effect period of the second side effect that occurs after the end of the first side effect and the prescription period of the drug that is prescribed after the end of the first side effect, An analysis system, wherein a drug other than a drug prescribed continuously after an improvement period of a second side effect is selected as a drug related to the second side effect.
  13. 　請求項１に記載の分析システムであって、
    　前記記憶装置は、診療行為に要した費用を含むコスト情報を保持し、
    　前記プロセッサは、
    　入院期間中の患者の診療行為の費用を合計して第１合計値を算出し、
    　前記算出された第１合計値を副作用毎に合計して第２合計値を算出し、前記第２合計値を当該費用がかかった患者数で除することによって、副作用毎の平均診療コストを算出し、
    　前記算出された第１合計値を副作用及び薬剤の組毎に合計して第３合計値を算出し、前記第３合計値を当該費用がかかった患者数で除することによって、薬剤毎の平均診療コストを算出し、
    　前記算出された副作用毎の平均診療コスト及び薬剤毎の平均診療コストを出力することを特徴とする分析システム。     The analysis system according to claim 1,
    The storage device stores cost information including expenses required for medical practice,
    The processor is
    Calculate the first total by summing the costs of the patient's medical treatment during hospitalization,
    The calculated first total value is summed for each side effect to calculate a second total value, and the second total value is divided by the number of patients who have spent the cost, thereby calculating an average medical cost for each side effect. And
    The calculated first total value is totaled for each side effect and drug group to calculate a third total value, and the third total value is divided by the number of patients who have spent the cost, thereby calculating the average for each drug. Calculate medical costs,
    An analysis system for outputting the calculated average medical cost for each side effect and average medical cost for each drug.
  14. 　プロセッサと、前記プロセッサに接続される記憶装置とを備える分析システムであって、
    　前記記憶装置は、患者の疾患及び入院期間を含む症例情報と、前記患者への薬剤の処方期間を含む処方情報と、前記患者の検査結果を含む検査情報と、前記検査結果に基づいて副作用が発生しているかを判定するための判定条件を含む知識情報とを保持し、
    　前記プロセッサは、
    　前記検査情報に含まれる検査結果の変動から検査値の改善期間を算出し、
    　前記処方期間を前記処方情報から取得し、
    　前記処方されている薬剤のうち、前記改善期間の前に処方されている薬剤が、前記検査値の改善と関係することを示す薬効情報を算出し、
    　前記検査値の改善と関係する薬剤の情報を出力することを特徴とする分析システム。     An analysis system comprising a processor and a storage device connected to the processor,
    The storage device has side effects based on case information including a patient's disease and hospitalization period, prescription information including a prescription period of a drug to the patient, test information including a test result of the patient, and the test result. Holding knowledge information including judgment conditions for judging whether it has occurred,
    The processor is
    Calculate the improvement period of the inspection value from the variation of the inspection result included in the inspection information,
    Obtaining the prescription period from the prescription information;
    Of the prescribed drugs, the drug prescribed before the improvement period calculates medicinal effect information indicating that it is related to the improvement of the test value,
    An analysis system for outputting information on a drug related to improvement of the test value.
  15. 　請求項１４に記載の分析システムであって、
    　前記プロセッサは、複数の患者の薬効情報と薬剤の使用症例数とを統計処理することによって、当該薬剤の薬効の程度を示す評価値を算出することを特徴とする分析システム。     15. The analysis system according to claim 14, wherein
    The said processor calculates the evaluation value which shows the grade of the medicinal effect of the said chemical | medical agent by statistically processing the medicinal effect information of several patients, and the number of use cases of a chemical | medical agent.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110289069A (en) * 2019-06-25 2019-09-27 中国人民解放军军事科学院军事医学研究院 Analysis method, device, computer equipment and the storage medium of clinical data
JP2020160698A (en) * 2019-03-26 2020-10-01 株式会社医療情報技術研究所 Medicine prescription supporting system
JP2021060648A (en) * 2019-10-03 2021-04-15 キヤノンメディカルシステムズ株式会社 Diagnosis support system, diagnosis support device, and program
US20220051795A1 (en) * 2018-09-12 2022-02-17 Hitachi, Ltd. Analysis system and analysis method
JP7253123B1 (en) * 2021-12-22 2023-04-05 株式会社エクサウィザーズ Information processing method, computer program and information processing apparatus
WO2023167089A1 (en) * 2022-03-03 2023-09-07 Ｓｏｍｐｏケア株式会社 Method, information processing device, system, and program

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06309384A (en) * 1993-04-22 1994-11-04 Dainippon Printing Co Ltd Medical diagnosis support system
JP2001344343A (en) * 2000-05-31 2001-12-14 Iryo Fukushi System Kenkyusho:Kk Drug efficacy evaluating means for electronic medical record
JP2003067489A (en) * 2001-08-22 2003-03-07 Medica 21 Nippon Kenko Iryo Kenkyusho:Kk Inspection result data output system
JP2006309740A (en) * 2005-03-28 2006-11-09 Yuyama Manufacturing Co Ltd Medical support system, and computer program for medical support
JP2007328751A (en) * 2006-06-07 2007-12-20 Japan Medical Information Research Institute Inc Data analysis processing device
JP2014071592A (en) * 2012-09-28 2014-04-21 Fujifilm Corp System, control method and control program for dosage effect prediction

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06309384A (en) * 1993-04-22 1994-11-04 Dainippon Printing Co Ltd Medical diagnosis support system
JP2001344343A (en) * 2000-05-31 2001-12-14 Iryo Fukushi System Kenkyusho:Kk Drug efficacy evaluating means for electronic medical record
JP2003067489A (en) * 2001-08-22 2003-03-07 Medica 21 Nippon Kenko Iryo Kenkyusho:Kk Inspection result data output system
JP2006309740A (en) * 2005-03-28 2006-11-09 Yuyama Manufacturing Co Ltd Medical support system, and computer program for medical support
JP2007328751A (en) * 2006-06-07 2007-12-20 Japan Medical Information Research Institute Inc Data analysis processing device
JP2014071592A (en) * 2012-09-28 2014-04-21 Fujifilm Corp System, control method and control program for dosage effect prediction

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220051795A1 (en) * 2018-09-12 2022-02-17 Hitachi, Ltd. Analysis system and analysis method
JP2020160698A (en) * 2019-03-26 2020-10-01 株式会社医療情報技術研究所 Medicine prescription supporting system
CN110289069A (en) * 2019-06-25 2019-09-27 中国人民解放军军事科学院军事医学研究院 Analysis method, device, computer equipment and the storage medium of clinical data
JP2021060648A (en) * 2019-10-03 2021-04-15 キヤノンメディカルシステムズ株式会社 Diagnosis support system, diagnosis support device, and program
JP7383440B2 (en) 2019-10-03 2023-11-20 キヤノンメディカルシステムズ株式会社 Diagnostic support system, diagnostic support device and program
JP7253123B1 (en) * 2021-12-22 2023-04-05 株式会社エクサウィザーズ Information processing method, computer program and information processing apparatus
WO2023119465A1 (en) * 2021-12-22 2023-06-29 株式会社エクサウィザーズ Information processing method, computer program, and information processing device
WO2023167089A1 (en) * 2022-03-03 2023-09-07 Ｓｏｍｐｏケア株式会社 Method, information processing device, system, and program
JP7411855B1 (en) 2022-03-03 2024-01-11 Ｓｏｍｐｏケア株式会社 Method, information processing device, system, and program

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