WO2016049795A1 - 一种多维度用药信息处理方法、系统和设备 - Google Patents

一种多维度用药信息处理方法、系统和设备 Download PDF

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WO2016049795A1
WO2016049795A1 PCT/CN2014/087736 CN2014087736W WO2016049795A1 WO 2016049795 A1 WO2016049795 A1 WO 2016049795A1 CN 2014087736 W CN2014087736 W CN 2014087736W WO 2016049795 A1 WO2016049795 A1 WO 2016049795A1
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medication
information
patient
attribute
drug
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PCT/CN2014/087736
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English (en)
French (fr)
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曹庆恒
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曹庆恒
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Priority to PCT/CN2014/087736 priority Critical patent/WO2016049795A1/zh
Priority to CN201480001168.2A priority patent/CN104487972B/zh
Publication of WO2016049795A1 publication Critical patent/WO2016049795A1/zh

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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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2264—Multidimensional index structures
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass

Definitions

  • the present invention relates to the field of medication information processing, and more particularly to a system, method and apparatus for providing medication information from multiple dimensions of different medical or pharmaceutical properties.
  • the invention provides a drug information processing system, method and device in combination with the actual medical industry situation in China.
  • a medication information processing system characterized in that the system comprises:
  • a drug use element information extracting unit for extracting drug use element information
  • a multi-dimensional attribute item dictionary unit for providing a multi-dimensional attribute item dictionary
  • the multi-dimensional processing unit of the medicine use element information processes the medicine use element information according to the multi-dimensional attribute item dictionary unit;
  • the patient medication related element information extraction unit is configured to extract information about the patient medication related elements
  • the multi-dimensional processing unit for patient medication related element information processes the information related to the patient medication according to the multi-dimensional attribute item dictionary unit;
  • the association matching degree processing unit processes the association matching degree of each attribute item information in the medicine use element information and the patient medication related element information
  • the rationality information generating unit generates information on the rationality of the medication.
  • the multi-dimensional attribute item dictionary unit is established according to the medical or pharmaceutical attribute structure of the medication information and the definition of each attribute.
  • the multi-dimensional attribute item dictionary unit is configured to provide a standard dictionary related to various medical or pharmaceutical attributes in the drug use information and the patient medication information.
  • the multi-dimensional attribute item dictionary unit is configured to provide a drug dictionary use information and a drug dictionary information, and different medical source or drug attribute different origins from the standard dictionary.
  • the multi-dimensional attribute item dictionary unit splits the drug use information into the minimum attribute item according to different medical or pharmaceutical attributes, and simultaneously saves the logical relationship between the attribute items, and records the data of each minimum attribute item.
  • a standard medical or pharmaceutical attribute item dictionary library Associated with a standard medical or pharmaceutical attribute item dictionary library.
  • the medical attribute items involved in the multi-dimensional attribute item dictionary include at least: a population-related attribute of the patient, a specific attribute of the patient, a disease-related attribute of the patient, At least one of the patient's past history-related attributes, the patient's operational-related attributes, and the patient's personal life-related attributes.
  • the pharmacy attribute items involved in the multi-dimensional attribute item dictionary include at least: a property related to the purpose of the drug, a related attribute of the patient's allergy history, a related attribute of the patient's medication history, a related attribute of the administration route of the patient, a gene attribute of the patient medication, and the like. At least one of the dimensions.
  • the system further comprises a medication pre-audit information generating unit for generating medication pre-audit information.
  • the pre-audit information generating unit generates pre-audit information for the drug, including at least one of pre-audit information of the drug indication, pre-audit information of the usage and usage conditions, and pre-audit information of the drug contraindication.
  • the system further comprises: an audit result generating unit, which is processed by the professional pharmacist on the basis of the pre-audit result to generate an audit result of the medication information.
  • an audit result generating unit which is processed by the professional pharmacist on the basis of the pre-audit result to generate an audit result of the medication information.
  • the system further comprises: a medication review analysis information generating unit, and generating medication review analysis information.
  • the medicine use information extracting unit is configured to extract information about the indications, the usage and dosage conditions, and the medical/pharmacy attribute elements of the medication contraindications for each medicine use information.
  • the drug use information is derived from at least one of a drug instruction manual, a pharmacopoeia, a national formulary, a treatment/drug guide, an expert consensus, and medication regulations at various levels of medical institutions.
  • the system further comprises a prescription medication message when used in a medical institution or a pharmacy
  • the information processing unit is configured to extract and use the information of the medicines in the processing catalog according to the medicine catalogue of the medical institution or the pharmacy in advance to control the range of the information base and improve the efficiency of data processing.
  • the patient medication related information extracting unit is configured to extract actual information of related attribute elements when the patient takes medication.
  • the source of the patient medication information is at least one of a doctor's prescription, a treatment plan, a medication plan, a medical record, a medical record, a medical record, an examination result, a patient self-reported symptom, and a patient's self-selection drug.
  • the medical pharmacy attribute dictionary pre-processing unit is further included, and each medical and pharmaceutical attribute dictionary used by the medical institution is associated with a system standard dictionary in advance to improve the efficiency of data processing.
  • the manner of generating the matching result is,
  • the drug use element information is K(X i (x1, x2, ..., xn)), and the patient drug related element information is Y (y 1 , y 2 , ..., y n ), and each attribute information is processed by multi-dimensionality.
  • the results are M(x i ) and M(y i ).
  • the system uses a C/S architecture or a B/S architecture.
  • the client of the system may be at least one of a PC terminal, a mobile terminal, a wearable device, a smart medicine box, a smart medicine box, and the like.
  • the system can be used for prescription review, prescription review, medication plan review, medication safety review, prescription quality inspection and management of the medical administrative department, prescription review of the retail pharmacy, and drug expense audit of the medical insurance institution. At least one of self-service review of the patient's medication, and the like.
  • a method for processing medication information characterized in that the method comprises:
  • the multi-dimensional attribute item dictionary is established according to the medical or pharmaceutical attribute structure of the medication information and the definition of each attribute.
  • the multi-dimensional attribute item dictionary is used to provide a standard dictionary related to various medical or pharmaceutical attributes in the drug use information and the patient medication information.
  • the multi-dimensional attribute item dictionary is used to provide the drug source usage information and the drug information of the patient, and the different sources of the medical or pharmaceutical attributes are associated with the standard dictionary.
  • the multi-dimensional attribute item dictionary is based on different medical or pharmaceutical attributes.
  • the information is split into the minimum attribute item, and the logical relationship between each attribute item is saved, and the data of each minimum attribute item is associated with the standard medical or pharmaceutical attribute item dictionary library.
  • the medical attribute items involved in the multi-dimensional attribute item dictionary include at least: a population-related attribute of the patient, a specific attribute of the patient, a disease-related attribute of the patient, a related history attribute of the patient, an operation-related attribute of the patient, At least one of the dimensions of the patient's personal life history related attributes, etc.
  • the pharmacy attribute items involved in the multi-dimensional attribute item dictionary include at least: a property related to the purpose of the drug, a related attribute of the patient's allergy history, a related attribute of the patient's medication history, a related attribute of the administration route of the patient, a gene attribute of the patient medication, and the like. At least one of the dimensions.
  • the method further comprises generating medication pre-audit information.
  • the pre-audit information for the medication is generated, and at least one of the pre-audit information of the indication for the drug, the pre-audit information for the usage and use conditions, and the pre-audit information for the medication contraindication.
  • the method further comprises: processing, by the professional pharmacist, based on the pre-audit result, and generating the audit result.
  • the method further comprises generating medication review analysis information.
  • the information on the use of the medicine is extracted, and the information of the relevant medical/pharmaceutical attribute elements of the indications, the usage and dosage conditions, and the contraindications of the medication are extracted for each medicine use information.
  • the drug use information is derived from at least one of a drug instruction manual, a pharmacopoeia, a national formulary, a treatment/drug guide, an expert consensus, and medication regulations at various levels of medical institutions.
  • the method when the method is applied to a medical institution or a pharmacy, the method further includes a prescription drug information processing unit for extracting and processing the use information of the drug in the catalog according to the drug list of the medical institution or the pharmacy to control the scope of the information base and improve The efficiency of data processing.
  • a prescription drug information processing unit for extracting and processing the use information of the drug in the catalog according to the drug list of the medical institution or the pharmacy to control the scope of the information base and improve The efficiency of data processing.
  • the patient medication related information is extracted for extracting actual information about the relevant attribute elements of the patient medication.
  • the source of the patient medication information is at least one of a doctor's prescription, a treatment plan, a medication plan, a medical record, a medical record, a medical record, an examination result, a patient self-reported symptom, and a patient's self-selection drug.
  • the medical pharmacy attribute dictionary preprocessing unit is further included, and each medical and pharmaceutical attribute dictionary used by the medical institution is associated with a system standard dictionary in advance to improve the efficiency of data processing.
  • the way to generate the associated matching result is,
  • the drug use element information is K(X i (x1, x2, ..., xn)), and the patient drug related element information is Y (y 1 , y 2 , ..., y n ), and each attribute information is processed by multi-dimensionality.
  • the results are M(x i ) and M(y i ).
  • the method can be used for prescription review, prescription review, medication plan review, medication safety audit, prescription quality inspection and management of the medical administrative department, prescription review of the retail pharmacy, and drug expense audit of the medical insurance institution. At least one of self-service review of the patient's medication, and the like.
  • a medication information processing device characterized in that the device comprises:
  • a drug use element information extraction module for extracting drug use element information
  • a multi-dimensional attribute item dictionary module for providing a multi-dimensional attribute item dictionary
  • the multi-dimensional processing module of the drug use element information processes the drug use element information according to the multi-dimensional attribute item dictionary unit;
  • the patient medication related element information extraction module is used for extracting information about the relevant elements of the patient medication
  • the multi-dimensional processing module for patient medication related element information processes the information related to the patient medication according to the multi-dimensional attribute item dictionary unit;
  • the association matching degree processing module processes the association matching degree of each attribute item information in the medicine use element information and the patient medication related element information
  • the rationality information generation module is used to generate rationality information of the medication.
  • the multi-dimensional attribute item dictionary module is established according to the medical or pharmaceutical attribute structure of the medication information and the definition of each attribute.
  • the multi-dimensional attribute item dictionary module is configured to provide a standard dictionary related to various medical or pharmaceutical attributes in the drug use information and the patient medication information.
  • the multi-dimensional attribute item dictionary module is configured to provide a drug dictionary using the drug source information and the patient medication information in different medical or pharmaceutical attributes from different sources and a standard dictionary.
  • the multi-dimensional attribute item dictionary module splits the drug use information into the minimum attribute item according to different medical or pharmaceutical attributes, and simultaneously saves the logical relationship between the attribute items, and records the data of each minimum attribute item.
  • a standard medical or pharmaceutical attribute item dictionary library Associated with a standard medical or pharmaceutical attribute item dictionary library.
  • the medical attribute items involved in the multi-dimensional attribute item dictionary include at least: a population-related attribute of the patient, a specific attribute of the patient, a disease-related attribute of the patient, a related history attribute of the patient, an operation-related attribute of the patient, At least one of the dimensions of the patient's personal life history related attributes, etc.
  • the pharmacy attribute items involved in the multi-dimensional attribute item dictionary include at least: a property related to the purpose of the drug, a related attribute of the patient's allergy history, a related attribute of the patient's medication history, a related attribute of the administration route of the patient, a gene attribute of the patient medication, and the like. At least one of the dimensions.
  • the device further comprises a medication pre-audit information generating module to generate medication pre-audit information.
  • the pre-audit information generating module generates pre-audit information for the drug, including at least one of pre-audit information of the drug indication, pre-audit information of the usage and usage conditions, and pre-audit information of the drug contraindication.
  • the device further comprises: an audit result generating module, which is processed by the professional pharmacist on the basis of the pre-audit result to generate an audit result of the medication information.
  • an audit result generating module which is processed by the professional pharmacist on the basis of the pre-audit result to generate an audit result of the medication information.
  • the device further comprises: a medication review analysis information generation module, and generates medication review analysis information.
  • the medicine use information extraction module is configured to extract each medicine use information Information on the relevant medical/pharmaceutical attributes of the indications, the usage and dosage, and the contraindications for medication.
  • the drug use information is derived from at least one of a drug instruction manual, a pharmacopoeia, a national formulary, a treatment/drug guide, an expert consensus, and medication regulations at various levels of medical institutions.
  • the invention when used in a medical institution or a pharmacy, the invention further comprises a prescription drug information processing module for extracting and processing the use information of the drug in the catalog according to the drug list of the medical institution or the pharmacy to control the scope of the information base and improve data processing. s efficiency.
  • the patient medication related information extraction module is configured to extract actual information of related attribute elements when the patient takes medication.
  • the source of the patient medication information is at least one of a doctor's prescription, a treatment plan, a medication plan, a medical record, a medical record, a medical record, an examination result, a patient self-reported symptom, and a patient's self-selection drug.
  • the medical pharmacy attribute dictionary pre-processing module is further included, and various medical and pharmaceutical attribute dictionaries used by the medical institution are associated with the system standard dictionary in advance to improve the efficiency of data processing.
  • the manner of generating the matching result is,
  • the drug use element information is K(X i (x1, x2, ..., xn)), and the patient drug related element information is Y (y 1 , y 2 , ..., y n ), and each attribute information is processed by multi-dimensionality.
  • the results are M(x i ) and M(y i ).
  • the device may be at least one of a PC terminal, a mobile terminal, a wearable device, a smart medicine box, a smart medicine box, and the like.
  • the device can be used for prescription review, prescription review, medication plan review, medication safety audit, prescription quality inspection and management of the medical administrative department, prescription review of the retail pharmacy, and drug expense audit of the medical insurance institution. At least one of self-service review of the patient's medication, and the like.
  • Figure 1 shows a schematic structural view of a system according to the present invention
  • Figure 2 is a flow chart showing a method of processing medication information according to the present invention
  • Figure 3 is a flow chart showing a pre-audit method of medication information according to the present invention.
  • FIG. 4 is a flow chart showing a method for confirming an audit result according to the medication information of the present invention.
  • Figure 5 is a flow chart showing a medication information review analysis method according to the present invention.
  • Figure 6 is a view showing the functional configuration of a medication information processing system according to the present invention.
  • Figure 7 is a view showing the functional configuration of a medication information pre-audit system according to the present invention.
  • Figure 8 is a view showing the functional configuration of the medication information confirmation audit result system according to the present invention.
  • Fig. 9 is a view showing the functional configuration of the medication information comment analysis system according to the present invention.
  • Fig. 1 shows a system configuration diagram of the present invention.
  • the workstation is connected to the server through the public network or the internal network of the medical institution and the retail pharmacy.
  • the workstation includes a physician workstation, a pharmacist workstation, a medicine dispensing window workstation, a medical insurance workstation, a medical administration workstation, a patient self-service terminal, and the like.
  • the workstation may be at least one of a PC terminal, a mobile terminal, a wearable device, and the like.
  • the network architecture of the workstation and server can be either a C/S architecture or a B/S architecture.
  • FIG. 2 is a flow chart showing the operation of a medication information processing method according to an embodiment of the present invention.
  • step 101 a multi-dimensional attribute item dictionary is established.
  • a multi-dimensional attribute item dictionary for providing a standard dictionary related to various medical or pharmaceutical attributes in drug use information and patient medication information, and a relationship between different source synonyms and a standard dictionary, and is based on medical or pharmaceutical attribute information for drug use information.
  • a structured standard database built with multi-dimensional splits.
  • Drug use information comes from drug instructions, pharmacopoeia, national formulary, treatment/drug guidelines, expert consensus, and medication regulations at all levels of medical institutions. This information can be divided into a variety of information types such as drug indications, usage and dosage conditions, and medication contraindications.
  • Drug indication information refers to all information about the conditions applicable to the drug. The same drug may need to be applied according to different usage and dosage under different conditions, so the applicable conditions of the drug usage and use refer to the different conditions applicable to different usage and dosage of the same drug.
  • Medication contraindications that is, regular medication contraindications, refer to all information on the conditions under which drugs are contraindicated.
  • Information on drug indications, applicable conditions for drug usage and dosage, and sources of drug contraindications include: drug instructions, national formulary, national pharmacopoeia, clinical guidelines, medication guidelines, clinical consensus, medical institution in-house pharmacy, and related literature.
  • Information on drug indications, usage and dosage, and contraindications for medication contain a large amount of data of different medical or pharmaceutical properties, and these data are separated into minimum attribute items according to their different medical or pharmaceutical properties, and each attribute item is saved. Between the logical relationship, and the data of each minimum attribute item is associated with the standard medical or pharmaceutical attribute item dictionary library, the structuring of the medical indications, the usage and dosage conditions, and the medical or pharmaceutical attribute information corresponding to the drug contraindications can be generated.
  • a standard database that builds a dictionary of multi-dimensional attribute items.
  • the indications for indications for aspirin enteric-coated tablets are: prevention of deep vein thrombosis and pulmonary embolism after major surgery; reduction of cardiovascular risk factors (family history of coronary heart disease, diabetes, dyslipidemia, hypertension, obesity, smoking history, age greater than 50) The age of the person) the risk of myocardial infarction.
  • the indications for the drug in the multi-dimensional attribute item dictionary include:
  • Attribute item Property description Corresponding attribute dictionary
  • Type of action prevention prevention operating Major surgery Type of action Reduce the risk of ... reduce risk Family history Family history of coronary heart disease Family history of coronary heart disease diagnosis diabetes diabetes diagnosis
  • Dyslipidemia Hyperlipidemia diagnosis hypertension hypertension
  • Special population obesity Obese people Special population History of smoking Smoking history
  • Atenolol tablets commonly used in adults start each time 6.25 ⁇ 12.5mg, twice a day, as needed and tolerance to gradually increase to 50 ⁇ 200mg.
  • the creatinine clearance rate is less than 15ml / (min.1.73m [sup] 2 [/sup]), 25mg daily; 15 ⁇ 35ml / (min.1.73m [sup] 2 [/sup]) , up to 50mg per day. Or follow the doctor's advice.
  • Child dosage for children should start from a small dose of 0.25 ⁇ 0.5mg / kg, twice a day. Pay attention to monitoring heart rate and blood pressure.
  • the records applicable to the usage of atenolol tablets in the multi-dimensional attribute item dictionary include:
  • Omeprazole enteric-coated capsules are contraindicated in those who are allergic to the drug, those with severe renal insufficiency, and infants.
  • Records of contraindications for omeprazole enteric-coated capsules in the multi-dimensional attribute item dictionary include:
  • the multi-dimensional medical attribute items involved in the multi-dimensional attribute item dictionary include at least: a population-related attribute of the patient, for example, age, gender, weight, height, body surface area, ethnicity, marital status, etc.; specific characteristics of the patient's particular population, for example, Pregnancy, pregnancy, lactation, menopause, etc.; the patient's disease-related attributes, such as the onset, duration, cause, Disease location, diagnosis, symptoms, test results, physiological indicators, concomitant symptoms, sequelae, etc.; past medical history related characteristics of the patient, for example, family history, disease history, chronic disease history, infectious disease history, vaccination history, history of surgical trauma, history of blood transfusion Etc.; patient-related attributes, such as surgery, examination operations, assisted operations, etc.; patient's personal life history related attributes, such as birthplace and long-term residence, living habits, tobacco/wine/food/drugs, etc., occupation Possible health damage with working conditions, etc.
  • a population-related attribute of the patient for example, age, gender, weight
  • the medical attribute items involved in the multi-dimensional attribute item dictionary include at least the patient's population-related attributes, the patient's special population-related attributes, the patient's disease-related attributes, the patient's past medical history-related attributes, the patient's operational-related attributes, and the patient's personal life history. At least one of the related attributes, etc.
  • the multi-dimensional pharmacy attribute items involved in the multi-dimensional attribute item dictionary include at least: related attributes of the purpose of the medication, for example, treatment, prevention, examination, diagnosis, etc.; related attributes of the patient's allergy history, for example, history of drug allergy, history of food allergy, allergens Etc.; related properties of the patient's medication history, for example, using special drugs, taking drugs, adverse drug reactions, etc.; relevant attributes of the route of administration of the patient, for example, oral, topical, injection, inhalation, etc.; .
  • the pharmacy attribute items involved in the multi-dimensional attribute item dictionary include at least one of the attributes related to the purpose of the medication, the attributes related to the history of the patient's allergy, the attributes related to the history of the medication of the patient, the relevant attributes of the administration route of the patient, and the genetic properties of the patient medication. Dimensions.
  • step 102 drug use element information is extracted.
  • the information on the use of drugs mainly includes information on the indications, the conditions of use and dosage, and the relevant medical or pharmaceutical attribute elements of each drug use information.
  • Drug use information comes from drug instructions, pharmacopoeia, national formulary, treatment/drug guidelines, expert consensus, and medical institutions at all levels Medication regulations, etc.
  • Step 103 Process the medicine use element information according to the multi-dimensional attribute item dictionary.
  • Multi-dimensional processing is performed by searching the multi-dimensional attribute item dictionary according to the split information.
  • different diagnostic names of the same disease in different hospitals can be treated differently, for example, Alzheimer's disease and Alzheimer's disease are treated differently; different dosage forms and specifications of the same medicine can also be processed by different names.
  • the merchandise which in turn improves the accuracy of the association match.
  • patient medication related element information is extracted.
  • the actual information used to extract the relevant attribute elements of the patient's medication includes information such as the patient's gender, age, past medical history, disease name, and illness, and in particular, the necessary information in the drug use information of the drug used by the patient.
  • the source of patient medication information is the doctor's prescription, treatment plan, medication plan, medical record, medical record, drug record, test result, patient's self-reported symptoms, and patient's self-selected drugs.
  • Step 105 Process the patient medication related element information according to the multi-dimensional attribute item dictionary. Find the attribute item dictionary based on the split information and perform multi-dimensional processing.
  • Step 106 Associate the processing of the matching degree.
  • the correlation matching degree of each attribute item information in the medicine use element information and the patient drug related element information is processed.
  • the following shows a way to generate an association match.
  • K For a certain drug K, a certain indication for the drug drug specification is K(X i ), and the result of the split is K(X i (x1, x2, ..., xn)).
  • the current physical condition of the patient includes age, special population, doctor's diagnosis, medical history, family history, various test indicators, various imaging indicators, and historical medication information.
  • the patient's physical condition is Y(y 1 , y 2 , ..., y n ).
  • Each attribute is processed by a different name, and the result is M(x i ) and M(y i ). ( i in y i is determined by i in x i )
  • the rationality information of the medication is generated.
  • the rationality information of the medication is generated according to the degree of association matching, and may be, for example, information that is reasonable and unreasonable.
  • the workflow further includes step 108.
  • the medication pre-audit information is generated.
  • Pre-audit information for medication including appropriate medications
  • Pre-audit information for pre-existing conditions pre-audit information for usage and usage conditions
  • pre-audit information for medication contraindications etc. It will be necessary to analyze the disease/diagnosis, medication, and dosage of the medication, according to the disease/diagnosis and drug indications, the applicable conditions of the dosage, and the contraindications of the medication. If there is a disease/diagnosis and the drug indication does not match.
  • the situation is that the suspected indication is unreasonable; if there is a situation in which the disease/diagnosis and the applicable conditions of the drug usage and use are not met, the applicable conditions for the suspected usage and dosage are unreasonable; if there is a contraindication to the use of the drug, the suspected drug is present. Taboo.
  • the pre-audit information can help the drug reviewer to accurately and efficiently review the medication information in combination with the actual application.
  • the workflow further includes step 109 with reference to FIG.
  • step 109 an audit result is generated.
  • the professionals who use the drug review, medication reviews, etc. confirm the results of the system pre-audit, and finally produce the drug review results.
  • the workflow further includes step 110 with reference to FIG.
  • step 110 the medication review analysis information is generated.
  • the pre-audit information and the audit result information are summarized, analyzed and re-processed, so as to provide further evaluation and analysis information on the indications of the medicines in the medicine, the applicable conditions of the usage and dosage, and the contraindications for medication.
  • FIG. 6 is a functional structural diagram of a medication information processing system according to an embodiment of the present invention.
  • the system includes a medicine use element information extraction unit 201, a multi-dimensional attribute item dictionary unit 202, and a medicine use element information multi-dimensional processing unit 203.
  • Drug use element information extracting unit 201 It is used to extract the information of the relevant medical or pharmaceutical attribute elements of indications, usage and dosage conditions, and medication contraindications in each medicine use information. Drug use information comes from drug instructions, pharmacopoeia, national formulary, treatment/drug guidelines, expert consensus, and medication regulations at all levels of medical institutions. .
  • the multi-dimensional attribute item dictionary unit 202 is configured to provide a standard dictionary related to each medical or pharmaceutical attribute in the drug use information and the patient medication information, and a relationship between the different source synonyms and the standard dictionary.
  • Information on drug indications, usage and dosage, and contraindications for medication contain a large amount of data of different medical or pharmaceutical properties, and these data are separated into minimum attribute items according to their different medical or pharmaceutical properties, and each attribute item is saved. Between the logical relationship, and the data of each minimum attribute item is associated with the standard medical or pharmaceutical attribute item dictionary library, the structuring of the medical indications, the usage and dosage conditions, and the medical or pharmaceutical attribute information corresponding to the drug contraindications can be generated.
  • a standard database that builds a multidimensional attribute item dictionary unit.
  • the medicine use element information multi-dimensional processing unit 203 is configured to perform multi-dimensional processing on the split attribute information of the medicine according to the multi-dimensional attribute item dictionary.
  • the multi-dimensional attribute item dictionary unit different diagnostic names of the same disease in different hospitals can also be treated differently, for example, Alzheimer's disease and Alzheimer's disease are treated differently; different dosage forms of the same medicine can also be processed by different names. And the specification of the product, which in turn improves the accuracy of the association match.
  • the patient medication related element information extracting unit 204 is configured to extract actual information of related attribute elements at the time of patient medication. For example, including patient gender, age, past medical history, illness Information such as the name, illness, etc., in particular, should contain the necessary information in the drug use information of the drug used by the patient.
  • the source of patient medication information is the doctor's prescription, treatment plan, medication plan, medical record, medical record, drug record, test result, patient's self-reported symptoms, and patient's self-selected drugs.
  • the patient medication related element information multi-dimensional processing unit 205 performs multi-dimensional processing on the related information according to the multi-dimensional attribute item dictionary. Find the attribute item dictionary based on the split information and perform multi-dimensional processing.
  • the association matching degree processing unit 206 processes the association degree of the association between the medicine use information and each attribute item information in the patient medication information. The following shows a way to generate an association match.
  • K For a certain drug K, a certain indication for the drug drug specification is K(X i ), and the result of the split is K(X i (x1, x2, ..., xn)).
  • a patient Y the current physical condition of the patient includes age, special population, doctor diagnosis, existing medical history, family history, various test indicators, various imaging indicators, historical medication information, and the like.
  • the patient's physical condition is Y(y 1 , y 2 , ..., y n ).
  • Each attribute is processed by a different name, and the result is M(x i ) and M(y i ). ( i in y i is determined by i in x i )
  • the medication rationality information generating unit 207 generates medication rationality information based on the association matching degree, and may be, for example, information that is rational and unreasonable.
  • the system further includes a medication pre-audit information generating unit 208.
  • the medication pre-audit information generating unit generates pre-audit information for medication.
  • Pre-audit information for medication including pre-audit information for drug indications, pre-audit information for usage and dosage, and pre-audit information for medication contraindications. It will be necessary to analyze the disease/diagnosis, medication, and dosage of the medication, according to the disease/diagnosis and drug indications, the applicable conditions of the dosage, and the contraindications of the medication. If there is a disease/diagnosis and the drug indication does not match.
  • the situation is that the suspected indication is unreasonable; if there is a situation in which the disease/diagnosis and the applicable conditions of the drug usage and use are not met, the applicable conditions for the suspected usage and dosage are unreasonable; if there is a contraindication to the use of the drug, the suspected drug is present. Taboo.
  • the pre-audit information can help the drug reviewer to accurately and efficiently review the medication information in combination with the actual application.
  • the system further includes an audit result generating unit 209.
  • the audit result generation unit is confirmed by the professionals who use the drug review and the drug review on the basis of the pre-audit results, and finally the drug review result is generated.
  • the system further includes a medication review analysis information generating unit 210.
  • the medication audit analysis information generating unit generates the medication audit analysis information.
  • the pre-audit information and the audit result information are summarized, analyzed and reprocessed, so as to provide further evaluation and analysis information on the indications of the drugs in the drug, the applicable conditions of the usage and the use.
  • An embodiment of the present invention provides a medication information processing apparatus, which includes a medicine use element information extraction module, a multi-dimensional attribute item dictionary module, a drug use element information multi-dimensional processing module, a patient medication related element information extraction module, and a patient. Medication related element information multi-dimensional processing module, associated matching degree processing module, medication rationality information generating module.
  • the medicine use element information extraction module is used for extracting information about the indications, the usage and dosage conditions, and the medical or pharmaceutical attribute elements of the medication contraindications for each medicine use information.
  • Drug use information comes from drug instructions, pharmacopoeia, national formulary, treatment/drug guidelines, expert consensus, and medication regulations at all levels of medical institutions. .
  • Multi-dimensional attribute item dictionary module for providing drug use information and patient medication information
  • Information on drug indications, usage and dosage, and contraindications for medication contain a large amount of data of different medical or pharmaceutical properties, and these data are separated into minimum attribute items according to their different medical or pharmaceutical properties, and each attribute item is saved.
  • the structuring of the medical indications, the usage and dosage conditions, and the medical or pharmaceutical attribute information corresponding to the drug contraindications can be generated.
  • the multi-dimensional processing module for the drug use element information is used for multi-dimensional processing of the split drug information according to the multi-dimensional attribute item dictionary.
  • different diagnostic names of the same disease in different hospitals can also be treated differently, for example, Alzheimer's disease and Alzheimer's disease are treated differently; different dosage forms of the same drug can also be processed by different names. And the specification of the product, which in turn improves the accuracy of the association match.
  • the patient medication related element information extraction module is used to extract the actual information of the relevant attribute elements when the patient takes medication. For example, it includes information such as the patient's gender, age, past medical history, disease name, and illness, and in particular, the necessary information in the drug use information of the drug used by the patient.
  • the source of patient medication information is the doctor's prescription, treatment plan, medication plan, medical record, medical record, drug record, test result, patient's self-reported symptoms, and patient's self-selected drugs.
  • the patient medication related element information multi-dimensional processing module performs multi-dimensional processing on the related information according to the multi-dimensional attribute item dictionary. Find the attribute item dictionary based on the split information and perform multi-dimensional processing.
  • Associated matching degree processing module processing drug use information and patient drug information The relevance of the sex information. The following shows a way to generate an association match.
  • K For a certain drug K, a certain indication for the drug drug specification is K(X i ), and the result of the split is K(X i (x1, x2, ..., xn)).
  • a patient Y the current physical condition of the patient includes age, special population, doctor diagnosis, existing medical history, family history, various test indicators, various imaging indicators, historical medication information, and the like.
  • the patient's physical condition is Y(y 1 , y 2 , ..., y n ).
  • Each attribute is processed by a different name, and the result is M(x i ) and M(y i ). ( i in y i is determined by i in x i )
  • the medication rationality information generating module generates the rationality information of the medication according to the relevance matching degree, and may be, for example, information that the medication is reasonable and unreasonable.
  • the apparatus further comprises a medication pre-audit information generation module.
  • the pre-audit information generation module is used to generate pre-audit information for medication.
  • Pre-audit information for medication including pre-audit information for drug indications, pre-audit information for usage and dosage, and pre-audit information for medication contraindications. It will be necessary to analyze the disease/diagnosis, medication, and dosage of the medication, according to the disease/diagnosis and drug indications, the applicable conditions of the dosage, and the contraindications of the medication. If there is a disease/diagnosis and the drug indication does not match.
  • the situation is that the suspected indication is unreasonable; if there is a situation in which the disease/diagnosis and the applicable conditions of the drug usage and use are not met, the applicable conditions for the suspected usage and dosage are unreasonable; if there is a contraindication to the use of the drug, the suspected drug is present. Taboo.
  • the pre-audit information can help the drug reviewer to accurately and efficiently review the medication information in combination with the actual application.
  • the apparatus further comprises an audit result generation module.
  • the audit result generation module is confirmed by the professionals who use the drug review and the drug review on the basis of the pre-audit results, and finally the drug review result is generated.
  • the apparatus further comprises a medication review analysis information generation module.
  • the medication audit analysis information generation module generates medication review analysis information.
  • the pre-audit information and the audit result information are summarized, analyzed and reprocessed, so as to provide further evaluation and analysis information on the indications of the drugs in the drug, the applicable conditions of the usage and the use.

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Abstract

一种用药信息处理的方法、系统和设备，该方法包括：建立多维度属性项字典（101）；提取药品使用要素信息（102）；根据多维度属性项字典对药品使用要素信息进行处理（103）；提取患者用药相关要素信息（104）；根据多维度属性项字典对患者用药相关要素信息进行处理（105）；处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度（106）；生成用药合理性信息（107）。基于该用药信息处理方法、系统和设备可以应用信息化技术分析和处理海量用药数据，规范用药的合理性。

Description

一种多维度用药信息处理方法、系统和设备 技术领域
本发明涉及用药信息处理的技术领域，特别是涉及一种提供从不同医学或药学属性多个维度对用药信息进行处理的系统、方法和设备。
背景技术
患者用药是否合理，直接关系到疾病的有效治疗、医疗资源的合理分配、医疗成本的有效控制、患者的用药安全等，是控制医疗质量，保障患者权益的关键。
应用现代信息处理技术，开发合理用药信息系统，处理用药信息，分析药物使用的合理性，达到规范临床合理用药的目的。其中，应用信息系统进行患者用药合理性分析的最大难点在于判断药品使用的适应症、用药禁忌、用法用量适用条件等是否适宜。
在我国的现实医疗情况中普遍存在如下问题：
由于我国各地区医疗发展水平的差异，各医院没有严格使用统一的诊断命名体系，同一种疾病在不同医院使用不同诊断名称的情况时有发生，如果用药信息处理系统仅根据诊断名称进行关联，就大大降低了诊断信息关联的准确性。
另外，在我国现有的药品注册制度下，成分相同的同一通用名药物往往存在由多家药厂生产的不同商品，每种商品往往还有多种剂型和规格。作为用药依据的药物使用说明书，相同通用名的不同商品药 物在适应症和用法用量适用条件的内容和描述也常常存在差异。如果用药信息处理系统仅从药品通用名维度处理数据，就无法处理相同通用名药物的不同商品药物适应症、用药禁忌和用法用量适用条件不同的情况。
目前的合理用药系统中，只是简单的建立药品与诊断标准名称的关系，由此作为判断患者用药的诊断和症状是否符合的基础。难以适用于不同药品说明书内容与不同医疗机构诊断书写习惯之间的对应关系，尤其不能支持药品适应症、用法用量适用条件和用药禁忌中存在多维度的信息时，患者用药适宜性的审核。
要解决现有技术存在的问题，就需要一种能覆盖用药相关信息的各个维度，对其所涉及的各项医学或药学属性信息进行全面提取、分析、比对的系统、方法和设备。建立适应症、用药禁忌、用法用量适用条件等药品使用信息中各项相关医学或药学属性数据结构、定义以及各属性数据的标准字典库，作为处理药品使用信息的框架和标准。并将系统获取的患者实际用药相关数据与经过结构化、标准化处理的药品适应症、用药禁忌、用法用量适用条件各属性项的数据进行比对，以判断患者用药的合理性。
发明内容
本发明结合我国的实际医药行业情况提供了一种用药信息处理系统、方法和设备。
一种用药信息处理系统，其特征在于，所述系统包括：
药品使用要素信息提取单元，用于提取药品使用要素信息；
多维度属性项字典单元，用于提供多维度属性项字典；
药品使用要素信息多维度处理单元，根据多维度属性项字典单元对药品使用要素信息进行处理；
患者用药相关要素信息提取单元，用于提取患者用药相关要素信息；
患者用药相关要素信息多维度处理单元，根据多维度属性项字典单元对患者用药相关要素信息进行处理；
关联匹配度处理单元，处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
用药合理性信息生成单元，生成用药的合理性信息。
较佳的，多维度属性项字典单元，根据用药信息的医学或药学属性结构及各属性定义建立。
较佳的，多维度属性项字典单元，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
较佳的，多维度属性项字典单元，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
较佳的，多维度属性项字典单元，根据不同的医学或药学属性将药品使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
较佳的，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、 患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
较佳的，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
较佳的，该系统进一步包括，用药预审核信息生成单元，生成用药预审核信息。
较佳的，用药预审核信息生成单元生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
较佳的，该系统进一步包括，审核结果生成单元，由专业药师在预审核结果的基础上进行处理，生成用药信息的审核结果。
较佳的，该系统进一步包括，用药审核分析信息生成单元，生成用药审核分析信息。
较佳的，药品使用信息提取单元，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
较佳的，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等的其中至少之一。
较佳的，该系统用于医疗机构或药店时进一步包括处方集用药信 息处理单元，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
较佳的，患者用药相关信息提取单元，用于提取患者用药时相关属性要素的实际信息。
较佳的，患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。
较佳的，该系统用于医疗机构时进一步包括医学药学属性字典预处理单元，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
较佳的，在关联匹配度处理单元中，生成匹配结果的方式为，
Figure PCTCN2014087736-appb-000001
Figure PCTCN2014087736-appb-000002
其中，药品使用要素信息为K(Xi(x1,x2,…,xn))，患者用药相关要素信息为Y(y1，y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
较佳的，该系统使用C/S架构或B/S架构，。
较佳的，系统的客户端可以是PC终端、移动终端、可穿戴设备、智能药箱、智能药盒等的其中至少一项。
较佳的，该系统可用于医疗机构的处方审核、处方点评、用药方案审核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
一种用药信息的处理方法，其特征在于，所述方法包括：
建立多维度属性项字典；
提取药品使用要素信息；
根据多维度属性项字典对药品使用要素信息进行处理；
提取患者用药相关要素信息；
根据多维度属性项字典对患者用药相关要素信息进行处理；
处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
生成用药合理性信息。
较佳的，多维度属性项字典，根据用药信息的医学或药学属性结构及各属性定义建立。
较佳的，多维度属性项字典，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
较佳的，多维度属性项字典，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
较佳的，多维度属性项字典，根据不同的医学或药学属性将药品 使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
较佳的，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
较佳的，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
较佳的，该方法进一步包括，生成用药预审核信息。
较佳的，生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
较佳的，该方法进一步包括，由专业药师在预审核结果的基础上进行处理，生成审核结果。
较佳的，该方法进一步包括，生成用药审核分析信息。
较佳的，提取药品使用信息，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
较佳的，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等的其中至少之一。
较佳的，该方法用于医疗机构或药店时进一步包括处方集用药信息处理单元，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
较佳的，提取患者用药相关信息，用于提取患者用药时相关属性要素的实际信息。
较佳的，患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。
较佳的，该方法用于医疗机构时进一步包括医学药学属性字典预处理单元，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
较佳的，生成关联匹配结果的方式为，
Figure PCTCN2014087736-appb-000003
Figure PCTCN2014087736-appb-000004
其中，药品使用要素信息为K(Xi(x1，x2,…,xn))，患者用药相关要素信息为Y(y1,y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
较佳的，该方法可用于医疗机构的处方审核、处方点评、用药方案审核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
一种用药信息处理设备，其特征在于，所述设备包括：
药品使用要素信息提取模块，用于提取药品使用要素信息；
多维度属性项字典模块，用于提供多维度属性项字典；
药品使用要素信息多维度处理模块，根据多维度属性项字典单元对药品使用要素信息进行处理；
患者用药相关要素信息提取模块，用于提取患者用药相关要素信息；
患者用药相关要素信息多维度处理模块，根据多维度属性项字典单元对患者用药相关要素信息进行处理；
关联匹配度处理模块，处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
用药合理性信息生成模块，生成用药的合理性信息。
较佳的，多维度属性项字典模块，根据用药信息的医学或药学属性结构及各属性定义建立。
较佳的，多维度属性项字典模块，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
较佳的，多维度属性项字典模块，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
较佳的，多维度属性项字典模块，根据不同的医学或药学属性将药品使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
较佳的，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
较佳的，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
较佳的，该设备进一步包括，用药预审核信息生成模块，生成用药预审核信息。
较佳的，用药预审核信息生成模块生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
较佳的，该设备进一步包括，审核结果生成模块，由专业药师在预审核结果的基础上进行处理，生成用药信息的审核结果。
较佳的，该设备进一步包括，用药审核分析信息生成模块，生成用药审核分析信息。
较佳的，药品使用信息提取模块，用于提取每一种药品使用信息 中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
较佳的，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等的其中至少之一。
较佳的，用于医疗机构或药店时进一步包括处方集用药信息处理模块，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
较佳的，患者用药相关信息提取模块，用于提取患者用药时相关属性要素的实际信息。
较佳的，患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。
较佳的，用于医疗机构时进一步包括医学药学属性字典预处理模块，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
较佳的，在关联匹配度处理模块中，生成匹配结果的方式为，
Figure PCTCN2014087736-appb-000005
其中，药品使用要素信息为K(Xi(x1,x2,…,xn))，患者用药相关要素信息为Y(y1,y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
较佳的，该设备可以是PC终端、移动终端、可穿戴设备、智能药箱、智能药盒等的其中至少一项。
较佳的，该设备可用于医疗机构的处方审核、处方点评、用药方案审核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
附图说明
图1示出根据本发明的系统结构示意图；
图2示出根据本发明的用药信息处理方法的流程图；
图3示出根据本发明的用药信息预审核方法的流程图；
图4示出根据本发明的用药信息确认审核结果方法的流程图；
图5示出根据本发明的用药信息点评分析方法的流程图；
图6示出根据本发明的用药信息处理系统的功能结构图；
图7示出根据本发明的用药信息预审核系统的功能结构图；
图8示出根据本发明的用药信息确认审核结果系统的功能结构图；
图9示出根据本发明的用药信息点评分析系统的功能结构图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
图1示出了本发明的系统结构图。其中，工作站通过公网或医疗机构、零售药店的内网连接到服务器。工作站包括医师工作站、药师工作站、发药窗口工作站、医保工作站、医政管理工作站、患者自助终端等。工作站可以是PC终端、移动终端、可穿戴设备等的其中至少一项。工作站与服务器的网络架构可以是C/S架构也可以是B/S架构。
图2为本发明的一个实施例，一种用药信息处理方法的工作流程图。
在步骤101中，建立多维度属性项字典。
多维度属性项字典，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典以及各不同来源异名与标准字典关联关系，是基于医学或药学属性信息对药品使用信息进行多维度的拆分而建立的结构化标准数据库。
药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定中。这些信息又可以分为药品适应症、用法用量适用条件、用药禁忌等多种的信息类型。
药品适应症信息指药物适用条件的所有信息。同一药品在不同条件下可能需要按照不同的用法用量适用，所以药品的用法用量适用条件是指同一药品的不同用法用量所适用的不同条件的信息。用药禁忌信息，即常规的用药禁忌，指药品禁忌使用的条件的所有信息。药品适应症信息、药品用法用量适用条件信息和用药禁忌信息的来源包括：药品说明书、国家处方集、国家药典、临床指南、用药指南、临床共识、医疗机构院内药事会以及相关文献。
药品适应症、用法用量适用条件、用药禁忌的信息中含有大量的不同医学或药学属性的数据，将这些数据根据其不同的医学或药学属性拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联，即可生成药品适应症、用法用量适用条件、用药禁忌各自对应的医学或药学属性信息的结构化标准数据库，建立多维度属性项字典。
以阿司匹林肠溶片的适应症信息为例：
阿司匹林肠溶片的适应症信息节选为：预防大手术后深静脉血栓和肺栓塞；降低心血管危险因素者(冠心病家族史、糖尿病、血脂异常、高血压、肥胖、抽烟史、年龄大于50岁者)心肌梗死发作的风险。
在多维度属性项字典中对于该药的适应症记录包括：
属性项 属性描述 对应属性字典
作用类型 预防 预防
操作 大手术 大手术
作用类型 降低….的风险 降低风险
家族病史 冠心病家族史 冠心病家族史
诊断 糖尿病 糖尿病
诊断 血脂异常 高脂血症
诊断 高血压 高血压
特殊人群 肥胖 肥胖人群
特殊人群 抽烟史 吸烟史人群
年龄人群 年龄大于50岁者 ＞50
再以阿替洛尔片的用法用量适用条件信息为例：
阿替洛尔片成人常用量：开始每次6.25～12.5mg，一日两次，按需要及耐受量渐增至50～200mg。肾功能损害时，肌酐清除率小于15ml/(min.1.73m[sup]2[/sup])者，每日25mg；15～35ml/(min.1.73m[sup]2[/sup])者，每日最多50mg。或遵医嘱。
儿童用量：用于儿童应从小剂量开始0.25～0.5mg/kg，每日二次。注意监测心率、血压。
老人用药：所需剂量可以减少，尤其是肾功能衰退的患者。
在多维度属性项字典中对于阿替洛尔片用法用量适用条件的记录包括：
Figure PCTCN2014087736-appb-000007
Figure PCTCN2014087736-appb-000008
再以奥美拉唑肠溶胶囊的用药禁忌信息为例：
奥美拉唑肠溶胶囊，对该药过敏者、严重肾功能不全者及婴幼儿禁用。
在多维度属性项字典中对于奥美拉唑肠溶胶囊用药禁忌的记录包括：
Figure PCTCN2014087736-appb-000009
多维度属性项字典中涉及的多维度医学属性项至少包括：患者的人群相关属性，例如，年龄、性别、体重、身高、体表面积、民族、婚姻状况等；患者的特殊人群相关属性，例如，待孕、怀孕、哺乳期、更年期等；患者的疾病相关属性，例如，发病情况、病程、致病因、 疾病部位、诊断、症状、检查结果、生理指标、伴随病症、后遗症等；患者的既往病史相关属性，例如，家族病史、疾病史、慢病史、传染病史、预防接种史、手术外伤史、输血史等；患者的操作相关属性，例如，手术、检查操作、辅助操作等；患者的个人生活史相关属性，例如，出生地及长期居留地、生活习惯、烟/酒/食物/药物等嗜好、职业与工作条件的可能健康损害等。多维度属性项字典中涉及的医学属性项至少包括患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
多维度属性项字典中涉及的多维度药学属性项至少包括：用药目的相关属性，例如，治疗、预防、检查、诊断等；患者过敏史相关属性，例如，药物过敏史、食物过敏史、过敏源等；患者用药史相关属性，例如，曾用特殊药物、在服药物、药物不良反应等；患者用药的给药途径相关属性，例如，口服、外用、注射、吸入等；患者用药相关基因属性等。多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
在步骤102中，提取药品使用要素信息。药品使用要素信息，主要包括每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学或药学属性要素信息。药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构 用药规定等。
步骤103，根据多维度属性项字典对药品使用要素信息进行处理。
根据拆分后的信息通过查找多维度属性项字典进行多维度处理。根据多维度属性项字典还可以异名处理同一种疾病在不同医院的不同诊断名称，比如将阿尔茨海默病与老年痴呆进行异名统一处理；也可以异名处理同一药品的不同剂型和规格的商品，进而提高了关联匹配的准确性。
在步骤104中，提取患者用药相关要素信息。用于提取患者用药时相关属性要素的实际信息。例如，包括患者性别、年龄、既往病史、疾病名称，病症等信息，尤其应包含患者所使用药品的药品使用信息中的必要信息。患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等。
步骤105，根据多维度属性项字典对患者用药相关要素信息进行处理。根据拆分后的信息查找属性项字典，进行多维度处理。
步骤106，关联匹配度的处理。
处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度。下面展示了一种生成关联匹配度的方式。
某药品K，商品药物说明书某条适应证为K(Xi)，拆分结果为K(Xi(x1，x2,…,xn))。
某患者Y，该患者的当前身体状况包含年龄、所属特殊人群、医生诊断、既有病史、家族病史、各项检验指标、各类影像指标、历史 服药信息等。该患者的身体状况为Y(y1,y2,…,yn)。各项属性经过异名处理，结果为M(xi)和M(yi)。(yi中的i由xi中的i决定)
xi与yi的匹配结果为：
Figure PCTCN2014087736-appb-000010
药品K的某条适应证K(Xi)与患者Y的匹配结果为：
Figure PCTCN2014087736-appb-000011
患者Y，是否能够使用药物K的结果为：
Figure PCTCN2014087736-appb-000012
步骤107，生成用药合理性信息。根据关联匹配度生成用药合理性信息，例如可以是用药合理、不合理等信息。
根据本发明的另一个实施例，参照图3，该工作流程进一步包括步骤108。
步骤108，生成用药预审核信息。用药预审核信息，包括药品适 应症预审核信息，用法用量适用条件预审核信息，用药禁忌预审核信息等。将需要分析用药的疾病/诊断、用药药物以及用法用量适用条件，根据疾病/诊断与药品适应症、用法用量适用条件、用药禁忌匹配关系进行对比，如果存在疾病/诊断与药品适应症不符合的情况，即为疑似适应症不合理；如果存在疾病/诊断与药品用法用量适用条件不符合的情况，即为疑似用法用量适用条件不合理；如果存在药品使用用药禁忌的情况，即为疑似存在用药禁忌。
该预审核信息可以帮助用药审核人员，结合实际应用，准确、高效的对用药信息进行审核。
根据本发明的另一个实施例，参照图4该工作流程进一步包括步骤109。
步骤109，生成审核结果。由用药审核、用药点评等的专业人员在系统预审核结果的基础上进行确认，最终生成用药审核结果。
根据本发明的另一个实施例，参照图5该工作流程进一步包括步骤110。
步骤110，生成用药审核分析信息。对预审核信息和审核结果信息进行汇总、分析和再处理，从而对用药中药品适应症、用法用量适用条件、用药禁忌等提供进一步的点评分析信息。
图6为本发明的一个实施例，一种用药信息处理系统的功能结构图，该系统包括药品使用要素信息提取单元201，多维度属性项字典单元202，药品使用要素信息多维度处理单元203，患者用药相关要 素信息提取单元204，患者用药相关要素信息多维度处理单元205，关联匹配度处理单元206，用药合理性信息生成单元207。
药品使用要素信息提取单元201。用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学或药学属性要素信息。药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等。。
多维度属性项字典单元202，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典以及各不同来源异名与标准字典关联关系。药品适应症、用法用量适用条件、用药禁忌的信息中含有大量的不同医学或药学属性的数据，将这些数据根据其不同的医学或药学属性拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联，即可生成药品适应症、用法用量适用条件、用药禁忌各自对应的医学或药学属性信息的结构化标准数据库，建立多维度属性项字典单元。
药品使用要素信息多维度处理单元203，用于根据多维度属性项字典对拆分后的药品各属性信息进行多维度处理。根据多维度属性项字典单元，还可以异名处理同一种疾病在不同医院的不同诊断名称，比如将阿尔茨海默病与老年痴呆进行异名统一处理；也可以异名处理同一药品的不同剂型和规格的商品，进而提高了关联匹配的准确性。
患者用药相关要素信息提取单元204，用于提取患者用药时相关属性要素的实际信息。例如，包括患者性别、年龄、既往病史、疾病 名称，病症等信息，尤其应包含患者所使用药品的药品使用信息中的必要信息。患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等。
患者用药相关要素信息多维度处理单元205，根据多维度属性项字典对相关信息进行多维度处理。根据拆分后的信息查找属性项字典，进行多维度处理。
关联匹配度处理单元206，处理药品使用信息与患者用药信息中各属性项信息的关联匹配度。下面展示了一种生成关联匹配度的方式。
某药品K，商品药物说明书某条适应证为K(Xi)，拆分结果为K(Xi(x1,x2,…,xn))。
某患者Y，该患者的当前身体状况包含年龄、所属特殊人群、医生诊断、既有病史、家族病史、各项检验指标、各类影像指标、历史服药信息等。该患者的身体状况为Y(y1,y2,…,yn)。各项属性经过异名处理，结果为M(xi)和M(yi)。(yi中的i由xi中的i决定)
xi与yi的匹配结果为：
Figure PCTCN2014087736-appb-000013
药品K的某条适应证K(Xi)与患者Y的匹配结果为：
Figure PCTCN2014087736-appb-000014
患者Y，是否能够使用药物K的结果为：
Figure PCTCN2014087736-appb-000015
用药合理性信息生成单元207，根据关联匹配度生成用药合理性信息，例如可以是用药合理、不合理等信息。
根据本发明的另一个实施例，参照图7，该系统进一步包括用药预审核信息生成单元208。
用药预审核信息生成单元，生成用药预审核信息。用药预审核信息，包括药品适应症预审核信息，用法用量适用条件预审核信息，用药禁忌预审核信息等。将需要分析用药的疾病/诊断、用药药物以及用法用量适用条件，根据疾病/诊断与药品适应症、用法用量适用条件、用药禁忌匹配关系进行对比，如果存在疾病/诊断与药品适应症不符合的情况，即为疑似适应症不合理；如果存在疾病/诊断与药品用法用量适用条件不符合的情况，即为疑似用法用量适用条件不合理；如果存在药品使用用药禁忌的情况，即为疑似存在用药禁忌。
该预审核信息可以帮助用药审核人员，结合实际应用，准确、高效的对用药信息进行审核。
根据本发明的另一个实施例，参照图8，该系统进一步包括审核结果生成单元209。
审核结果生成单元，由用药审核、用药点评等的专业人员在预审核结果的基础上进行确认，最终生成用药审核结果。
根据本发明的另一个实施例，参照图9，该系统进一步包括用药审核分析信息生成单元210。
用药审核分析信息生成单元，生成用药审核分析信息。对预审核信息和审核结果信息进行汇总、分析和再处理，从而对用药中药品适应症、用法用量适用条件等提供进一步的点评分析信息。
本发明的一个实施例，提供一种用药信息处理设备，该设备包括药品使用要素信息提取模块，多维度属性项字典模块，药品使用要素信息多维度处理模块，患者用药相关要素信息提取模块，患者用药相关要素信息多维度处理模块，关联匹配度处理模块，用药合理性信息生成模块。
药品使用要素信息提取模块，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学或药学属性要素信息。药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等。。
多维度属性项字典模块，用于提供药品使用信息和患者用药信息 中各项医学或药学属性涉及的标准字典以及各不同来源异名与标准字典关联关系。药品适应症、用法用量适用条件、用药禁忌的信息中含有大量的不同医学或药学属性的数据，将这些数据根据其不同的医学或药学属性拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联，即可生成药品适应症、用法用量适用条件、用药禁忌各自对应的医学或药学属性信息的结构化标准数据库，建立多维度属性项字典单元。
药品使用要素信息多维度处理模块，用于根据多维度属性项字典对拆分后的药品信息进行多维度处理。根据多维度属性项字典模块，还可以异名处理同一种疾病在不同医院的不同诊断名称，比如将阿尔茨海默病与老年痴呆进行异名统一处理；也可以异名处理同一药品的不同剂型和规格的商品，进而提高了关联匹配的准确性。
患者用药相关要素信息提取模块，用于提取患者用药时相关属性要素的实际信息。例如，包括患者性别、年龄、既往病史、疾病名称，病症等信息，尤其应包含患者所使用药品的药品使用信息中的必要信息。患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等。
患者用药相关要素信息多维度处理模块，根据多维度属性项字典对相关信息进行多维度处理。根据拆分后的信息查找属性项字典，进行多维度处理。
关联匹配度处理模块，处理药品使用信息与患者用药信息中各属 性项信息的关联匹配度。下面展示了一种生成关联匹配度的方式。
某药品K，商品药物说明书某条适应证为K(Xi)，拆分结果为K(Xi(x1,x2,…,xn))。
某患者Y，该患者的当前身体状况包含年龄、所属特殊人群、医生诊断、既有病史、家族病史、各项检验指标、各类影像指标、历史服药信息等。该患者的身体状况为Y(y1,y2,…,yn)。各项属性经过异名处理，结果为M(xi)和M(yi)。(yi中的i由xi中的i决定)
xi与yi的匹配结果为：
Figure PCTCN2014087736-appb-000016
药品K的某条适应证K(Xi)与患者Y的匹配结果为：
Figure PCTCN2014087736-appb-000017
患者Y，是否能够使用药物K的结果为：
Figure PCTCN2014087736-appb-000018
用药合理性信息生成模块，根据关联匹配度生成用药合理性信息，例如可以是用药合理、不合理等信息。
根据本发明的另一个实施例，该设备进一步包括用药预审核信息生成模块。
用药预审核信息生成模块，生成用药预审核信息。用药预审核信息，包括药品适应症预审核信息，用法用量适用条件预审核信息，用药禁忌预审核信息等。将需要分析用药的疾病/诊断、用药药物以及用法用量适用条件，根据疾病/诊断与药品适应症、用法用量适用条件、用药禁忌匹配关系进行对比，如果存在疾病/诊断与药品适应症不符合的情况，即为疑似适应症不合理；如果存在疾病/诊断与药品用法用量适用条件不符合的情况，即为疑似用法用量适用条件不合理；如果存在药品使用用药禁忌的情况，即为疑似存在用药禁忌。
该预审核信息可以帮助用药审核人员，结合实际应用，准确、高效的对用药信息进行审核。
根据本发明的另一个实施例，该设备进一步包括审核结果生成模块。
审核结果生成模块，由用药审核、用药点评等的专业人员在预审核结果的基础上进行确认，最终生成用药审核结果。
根据本发明的另一个实施例，该设备进一步包括用药审核分析信息生成模块。
用药审核分析信息生成模块，生成用药审核分析信息。对预审核信息和审核结果信息进行汇总、分析和再处理，从而对用药中药品适应症、用法用量适用条件等提供进一步的点评分析信息。
以上对本发明的具体实施例进行了描述。需要理解的是，本发明并不局限于上述特定实施方式，本领域技术人员可以在权利要求的范围内做出各种变形或修改，这并不影响本发明的实质内容。

Claims (54)

  1. 一种用药信息处理系统，其特征在于，所述系统包括：
    药品使用要素信息提取单元，用于提取药品使用要素信息；
    多维度属性项字典单元，用于提供多维度属性项字典；
    药品使用要素信息多维度处理单元，根据多维度属性项字典单元对药品使用要素信息进行处理；
    患者用药相关要素信息提取单元，用于提取患者用药相关要素信息；
    患者用药相关要素信息多维度处理单元，根据多维度属性项字典单元对患者用药相关要素信息进行处理；
    关联匹配度处理单元，处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
    用药合理性信息生成单元，生成用药的合理性信息。
  2. 根据权利要求1的用药信息处理系统，其特征在于，多维度属性项字典单元，根据用药信息的医学或药学属性结构及各属性定义建立。
  3. 根据权利要求2的用药信息处理系统，其特征在于，多维度属性项字典单元，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
  4. 根据权利要求2的用药信息处理系统，其特征在于，多维度属性项字典单元，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
  5. 根据权利要求2的用药信息处理系统，其特征在于，多维度 属性项字典单元，根据不同的医学或药学属性将药品使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
  6. 根据权利要求5的用药信息处理系统，其特征在于，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
  7. 根据权利要求5的用药信息处理系统，其特征在于，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
  8. 根据权利要求1的用药信息处理系统，其特征在于，该系统进一步包括，用药预审核信息生成单元，生成用药预审核信息。
  9. 根据权利要求8的用药信息处理系统，其特征在于，用药预审核信息生成单元生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
  10. 根据权利要求8的用药信息处理系统，其特征在于，该系统进一步包括，审核结果生成单元，由专业药师在预审核结果的基础上进行处理，生成用药信息的审核结果。
  11. 根据权利要求10的用药信息处理系统，其特征在于，该系 统进一步包括，用药审核分析信息生成单元，生成用药审核分析信息。
  12. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，药品使用信息提取单元，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
  13. 根据权利要求12的用药信息处理系统，其特征在于，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等的其中至少之一。用于医疗机构或药店时进一步包括处方集用药信息处理单元，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
  14. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，患者用药相关信息提取单元，用于提取患者用药时相关属性要素的实际信息。
  15. 根据权利要求14的用药信息处理系统，其特征在于，患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。用于医疗机构时进一步包括医学药学属性字典预处理单元，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
  16. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，在关联匹配度处理单元中，生成匹配结果的方式为，
    Figure PCTCN2014087736-appb-100001
    Figure PCTCN2014087736-appb-100002
    其中，药品使用要素信息为K(Xi(x1,x2,…,xn))，患者用药相关要素信息为Y(y1,y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
  17. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，该系统使用C/S架构或B/S架构，。
  18. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，系统的客户端可以是PC终端、移动终端、可穿戴设备、智能药箱、智能药盒等的其中至少一项。
  19. 根据权利要求1-11中任意一项的用药信息处理系统，其特征在于，该系统可用于医疗机构的处方审核、处方点评、用药方案审核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
  20. 一种用药信息的处理方法，其特征在于，所述方法包括：
    建立多维度属性项字典；
    提取药品使用要素信息；
    根据多维度属性项字典对药品使用要素信息进行处理；
    提取患者用药相关要素信息；
    根据多维度属性项字典对患者用药相关要素信息进行处理；
    处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
    生成用药合理性信息。
  21. 根据权利要求20的用药信息处理方法，其特征在于，多维度属性项字典，根据用药信息的医学或药学属性结构及各属性定义建立。
  22. 根据权利要求21的用药信息处理方法，其特征在于，多维度属性项字典，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
  23. 根据权利要求21的用药信息处理方法，其特征在于，多维度属性项字典，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
  24. 根据权利要求21的用药信息处理方法，其特征在于，多维度属性项字典，根据不同的医学或药学属性将药品使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
  25. 根据权利要求24的用药信息处理方法，其特征在于，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、 患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
  26. 根据权利要求24的用药信息处理方法，其特征在于，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
  27. 根据权利要求20的用药信息处理方法，其特征在于，该方法进一步包括，生成用药预审核信息。
  28. 根据权利要求27的用药信息处理方法，其特征在于，生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
  29. 根据权利要求27的用药信息处理方法，其特征在于，该方法进一步包括，由专业药师在预审核结果的基础上进行处理，生成审核结果。
  30. 根据权利要求29的用药信息处理方法，其特征在于，该方法进一步包括，生成用药审核分析信息。
  31. 根据权利要求20-30中任意一项的用药信息处理方法，其特征在于，提取药品使用信息，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
  32. 根据权利要求31的用药信息处理方法，其特征在于，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、 专家共识、以及各级医疗机构用药规定等的其中至少之一。用于医疗机构或药店时进一步包括处方集用药信息处理单元，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
  33. 根据权利要求20-30中任意一项的用药信息处理方法，其特征在于，提取患者用药相关信息，用于提取患者用药时相关属性要素的实际信息。
  34. 根据权利要求33的用药信息处理方法，其特征在于，患者用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。用于医疗机构时进一步包括医学药学属性字典预处理单元，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
  35. 根据权利要求20-30中任意一项的用药信息处理方法，其特征在于，生成关联匹配结果的方式为，
    Figure PCTCN2014087736-appb-100003
    Figure PCTCN2014087736-appb-100004
    其中，药品使用要素信息为K(Xi(x1,x2,…,xn))，患者用药相关要素 信息为Y(y1,y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
  36. 根据权利要求20-30中任意一项的用药信息处理方法，其特征在于，该方法可用于医疗机构的处方审核、处方点评、用药方案审核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
  37. 一种用药信息处理设备，其特征在于，所述设备包括：
    药品使用要素信息提取模块，用于提取药品使用要素信息；
    多维度属性项字典模块，用于提供多维度属性项字典；
    药品使用要素信息多维度处理模块，根据多维度属性项字典单元对药品使用要素信息进行处理；
    患者用药相关要素信息提取模块，用于提取患者用药相关要素信息；
    患者用药相关要素信息多维度处理模块，根据多维度属性项字典单元对患者用药相关要素信息进行处理；
    关联匹配度处理模块，处理药品使用要素信息与患者用药相关要素信息中各属性项信息的关联匹配度；
    用药合理性信息生成模块，生成用药的合理性信息。
  38. 根据权利要求37的用药信息处理设备，其特征在于，多维度属性项字典模块，根据用药信息的医学或药学属性结构及各属性定 义建立。
  39. 根据权利要求38的用药信息处理设备，其特征在于，多维度属性项字典模块，用于提供药品使用信息和患者用药信息中各项医学或药学属性涉及的标准字典。
  40. 根据权利要求38的用药信息处理设备，其特征在于，多维度属性项字典模块，用于提供药品使用信息和患者用药信息中各项医学或药学属性不同来源异名与标准字典关联关系。
  41. 根据权利要求38的用药信息处理设备，其特征在于，多维度属性项字典模块，根据不同的医学或药学属性将药品使用信息拆分到最小属性分项，同时保存各属性项之间的逻辑关系，并将各最小属性分项的数据与标准医学或药学属性项字典库关联。
  42. 根据权利要求41的用药信息处理设备，其特征在于，多维度属性项字典中涉及的医学属性项至少包括：患者的人群相关属性、患者的特殊人群相关属性、患者的疾病相关属性、患者的既往病史相关属性、患者的操作相关属性、患者的个人生活史相关属性等的其中至少一个维度。
  43. 根据权利要求41的用药信息处理设备，其特征在于，多维度属性项字典中涉及的药学属性项至少包括：用药目的相关属性、患者过敏史相关属性、患者用药史相关属性、患者用药的给药途径相关属性、患者用药相关基因属性等的其中至少一个维度。
  44. 根据权利要求37的用药信息处理设备，其特征在于，该设备进一步包括，用药预审核信息生成模块，生成用药预审核信息。
  45. 根据权利要求44的用药信息处理设备，其特征在于，用药预审核信息生成模块生成用药预审核信息，包括药品适应症预审核信息、用法用量适用条件预审核信息、用药禁忌预审核信息等的其中至少一项。
  46. 根据权利要求44的用药信息处理设备，其特征在于，该设备进一步包括，审核结果生成模块，由专业药师在预审核结果的基础上进行处理，生成用药信息的审核结果。
  47. 根据权利要求46的用药信息处理设备，其特征在于，该设备进一步包括，用药审核分析信息生成模块，生成用药审核分析信息。
  48. 根据权利要求37-47中任意一项的用药信息处理设备，其特征在于，药品使用信息提取模块，用于提取每一种药品使用信息中适应症、用法用量适用条件、用药禁忌的相关医学/药学属性要素信息。
  49. 根据权利要求48的用药信息处理设备，其特征在于，药品使用信息来源于药品说明书、药典、国家处方集、治疗/用药指南、专家共识、以及各级医疗机构用药规定等的其中至少之一。用于医疗机构或药店时进一步包括处方集用药信息处理模块，用于预先根据医疗机构或药店的药品目录提取处理目录内药品的使用信息，以控制信息库的范围，提高数据处理的效率。
  50. 根据权利要求37-47中任意一项的用药信息处理设备，其特征在于，患者用药相关信息提取模块，用于提取患者用药时相关属性要素的实际信息。
  51. 根据权利要求50的用药信息处理设备，其特征在于，患者 用药信息的来源为医生处方、治疗方案、用药方案、病历、病案、药历、检查结果、患者自述症状、患者自主选药等其中的至少之一。用于医疗机构时进一步包括医学药学属性字典预处理模块，预先将医疗机构使用的各项医学、药学属性字典与系统标准字典进行关联，以提高数据处理的效率。
  52. 根据权利要求37-47中任意一项的用药信息处理设备，其特征在于，在关联匹配度处理模块中，生成匹配结果的方式为，
    Figure PCTCN2014087736-appb-100005
    Figure PCTCN2014087736-appb-100006
    其中，药品使用要素信息为K(Xi(x1,x2,…,xn))，患者用药相关要素信息为Y(y1,y2,…,yn)，各项属性信息经过多维度处理，结果为M(xi)和M(yi)。
  53. 根据权利要求37-47中任意一项的用药信息处理设备，其特征在于，该设备可以是PC终端、移动终端、可穿戴设备、智能药箱、智能药盒等的其中至少一项。
  54. 根据权利要求37-47中任意一项的用药信息处理设备，其特征在于，该设备可用于医疗机构的处方审核、处方点评、用药方案审 核、用药安全审核，医疗行政管理部门的处方质量检测、管理，零售药店的处方审核，医疗保险机构的用药费用审核，患者用药的自助审查等的其中至少一项。
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