JP2017111487A - Extraction method and extraction device for untreated subscriber group having illness - Google Patents

Abstract

PROBLEM TO BE SOLVED: To extract an untreated subscriber having illness by a simple method precisely.SOLUTION: An extraction method of an untreated subscriber having illness includes a first process and a second process. In the first process, from a physical checkup database device 1 for storing physical checkup data of a subscriber of an insurer, physical checkup data for a first period ranging a plurality of months is extracted and a subscriber group 3 having illness suffering from illness to observe is extracted based on the physical checkup data. In the second process, from a receipt database device 4 in which receipt data of the subscriber is stored, receipt data during a second period ranging a plurality of months after the first period is extracted, and based on the extracted receipt data, an untreated subscriber group 6 whose frequency of remedy of illness to observe is equal to or less than a prescribed frequency is extracted from the subscriber group 3 having illness extracted in the first process.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a method and an apparatus for extracting a pre-treatment untreated subscriber group.

  Health insurance associations (hereinafter referred to as health insurance associations), national health insurance, etc. operated by municipalities, special wards, etc., due to an increase in patients with lifestyle-related diseases and their reserves, as well as an increase in the number of elderly persons The medical benefits of those who continue to grow (hereinafter referred to as “insurers”) continue to increase.

  For this reason, some insurers are likely to incur high medical expenses based on medical checkup records (hereinafter referred to as medical checkup records) such as the specific medical checkup records of the subscribers and receipts that are bills for medical expenses. We are trying to identify a risk person in advance and build a mechanism that encourages examination and treatment in advance.

  However, since the conventional receipt and medical examination record still contain a lot of paper information, it is difficult to search electronically, and specialists such as public health nurses browse the paper information of the reception and medical examination record manually, It is necessary to extract high-risk individuals. Since manual work by specialists such as public health nurses takes a great deal of time, it is difficult to extract high-risk persons without omission, and considerable labor costs are required to perform this work.

  In recent years, the digitization of receipts has progressed, but although the digitization of medical checkup records has begun in part, there are still many health insurance societies that still handle the medical checkup records with paper information. In the near future, the medical examination records will also be completely digitalized. However, since the purpose of computerization and the history of system construction differ between the receipt and the medical examination record, the data of the electronic data is different between the reception and the medical examination record. Since the format and storage format are very different, it is not easy to perform so-called “match analysis”, which compares the electronic data of the receipt and the medical examination record. For this reason, it can be said that it is still technically difficult to extract the above-mentioned high-risk person before the medical condition worsens.

  As a related technology, create time-series data that integrates medical checkups and receipts for each individual, and extracts in two stages based on the time of the medical checkup, that is, extracts individuals affected by the disease of interest using the medical checkup data. Among the extracted individuals, there are those that extract affected persons whose number of treatments within a treatment count period after the medical examination period does not satisfy a predetermined number. Thereby, a diseased untreated subscriber who is an afflicted person who did not perform appropriate treatment even though the result of the medical examination was bad is extracted with high accuracy as a high-risk person. However, in particular for small and medium-sized insurers, it is difficult to create individual time-series data because the medical examination and receipt databases are managed separately, and there is a hurdle for implementing this method. It was high.

Japanese Patent Laying-Open No. 2015-156098

  The problem to be solved by the present invention is to extract a diseased untreated subscriber with high accuracy by a simple technique.

  The extraction method of a diseased untreated subscriber group according to the embodiment of the present invention includes a first step and a second step. In the first step, the medical examination data for a first period of a plurality of months is extracted from a medical examination database storing medical examination data of the insurer's subscribers, and a disease to which attention is paid based on the medical examination data. To extract a group of affected patients affected by In the second step, the receipt data of the second period over a plurality of months after the first period is extracted from the receipt database storing the subscriber's receipt data, and based on the extracted receipt data, An untreated subscriber group in which the number of treatments of the disease of interest is equal to or less than a predetermined number is extracted from the group of diseased subscribers extracted in the first step.

The figure which shows one form of the extraction apparatus of a disease untreated person group for implementing the extraction method of a disease untreated person group which concerns on this embodiment. The figure which shows the other form of the extraction apparatus of a disease untreated person group for implementing the extraction method of a disease untreated person group which concerns on this embodiment. The figure for demonstrating the 1st period, the 2nd period, the 3rd period, the 4th period, and the waiting period. The figure explaining the example which extracts a sick subscriber and a sick untreated subscriber. The figure explaining the example which narrows down a disease untreated subscriber group using additional medical examination data. The figure which shows the hardware structural example of the apparatus of FIG. The flowchart of operation | movement of the extraction method of a disease untreated person group which concerns on this embodiment. The figure explaining the Example and comparative example of this invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 shows an embodiment of an apparatus for extracting a group of untreated and untreated patients for carrying out a method for extracting a group of untreated and untreated patients by an insurer and his / her representative. In this embodiment, an untreated person is an insurer's subscriber (insured person) who has an abnormal value indicating illness in medical examination data, and should be examined and treated at a medical institution. Regardless, it does not do it properly. The subscriber may include a dependent of the subscriber.

  The medical examination database device 1 is an electronic data of medical examination records for each medical insurer (hereinafter referred to as insurer) such as national health insurance operated by health insurance associations (hereinafter referred to as health insurance associations), municipalities and special wards. (Medical examination data) is accumulated. The medical examination data is an aggregate of an index for evaluating a health condition obtained by an inquiry and various tests conducted for use in health maintenance and prevention / early detection of diseases. The medical examination data includes, as an example, personal identification information of each subscriber, and at least examination items and result values of medical examination related to the disease of interest. Examples of personal identification information include name, employee code, insurance card number, date of birth, and the like.

  The 1st extraction part 101 sets the 1st period which is a reference period of medical examination data. FIG. 2A shows examples of various periods set in this embodiment. As an example, the first period is 12 months from April to March in a certain year. However, the first period may be a longer period or a shorter period, and may be a period extending over a plurality of months of at least two months.

  The first extraction unit 101 extracts the medical examination data for the first period from the medical examination database device 1 and creates the medical examination database 2. The medical examination data in the first period is the medical examination data of the subscriber whose medical examination day or medical examination month is included in the first period. The extracted medical examination data is a subset of the medical examination data in the medical examination database device 1. The function of the first extraction unit 101 may be included in the medical examination database device 1, or may be a device independent of the medical examination database device 1 having the function of the first extraction unit 101.

  The medical examination database 2 can be realized by an arbitrary physical storage medium such as a memory device provided in a device such as a personal computer, a hard disk, an SSD, or an optical recording device such as a DVD. The medical examination database 2 may be electrically connected to the first extraction unit 101 or the medical examination database device 1. The first extraction unit 101 may write the medical examination data extracted from the health database 1 in a storage medium such as a DVD. At this time, the personal identification information included in the medical examination data can be anonymized. Anonymization means that when the insurer entrusts this extraction work to an external institution, the personal identification information is changed to a unique ID (character string, etc.) so that personal information such as name is not specified by the outsourcer. It means to replace.

  The diseased subscriber group extraction unit 103 is a device that can electronically extract data from the medical examination database 2. The prevalence subscriber group extraction unit 103 determines the level of the medical condition by paying attention to the examination items of the medical examination related to the disease of interest, and has reached the level of the medical condition that requires treatment. The 1st process of extracting is performed. Examples of the disease of interest include, but are not limited to, diabetes or hypertension.

  When the disease of interest is diabetes, 7.4% or more of the NGSP criteria are determined to be a group of pre-existing subscribers based on the value of hemoglobin A1c (HbA1c) as a test item To do. 7.4% is an example, and may be changed as appropriate according to the range deemed necessary for treatment. Further, the index used when determining whether or not treatment is necessary is not necessarily hemoglobin A1c (HbA1c), and other indices such as fasting blood glucose level may be used.

  In addition, when the disease of interest is hypertension, on the basis of the values of systolic blood pressure and diastolic blood pressure as test items, for example, there is a prevalence of afflicting a group of subscribers who exceeded either 160 mmHg or 100 mmHg, respectively. The subscriber group 3 is determined. These numerical values are examples, and may be changed as appropriate according to the range deemed necessary for treatment.

  The receipt database device 4 stores electronic data of receipts (receipt data) for insurer subscribers. Receipt data is issued on a monthly basis by the medical institution where the subscriber receives medical care. Receipt data refers to a collection of medical fee details and dispensing fee details for insurance medical treatment. Specifically, the receipt data includes a target month, a medical institution code, a wound name code, a medication name (medicinal effect classification number), and a medical treatment content (medical practice code). It is possible to determine from the name of the drug (drug classification number) and the medical treatment content (medical practice code) or both, whether the subscriber has received medical care for the disease of interest during the month. In addition, since there may be cases where it is not possible to conclude from the wound name described in the wound name code that treatment has actually been performed that month, in this embodiment, the drug name (drug classification number) and the medical treatment content (medical practice code) or Use both of these. Receipt data issued by the medical institution is transmitted from the medical institution system to the insurer system and stored in the receipt database device 4.

  As shown in FIG. 3A, the second extraction unit 102 sets a second period that is a reference period for the receipt data. The second period is a period after the elapse of the first period, and may be started immediately after the elapse of the first period, or as shown in FIG. 3B, the end month of the first period May be started after one or more months. The length of the second period is preferably 12 months or longer, but can be longer or shorter. The second extraction unit 102 may set a period specified in advance by the administrator of the system as the second period, or obtain information on the first period set by the first extraction unit 101 Then, the second period may be set so as to satisfy a predetermined relationship (for example, a period of 12 months from the month following the end month of the first period) with respect to the first period. As shown in FIG. 3B, the third period, which is the period from the start month of the first period to the month before the second start month, is the difference between the first period and the second period. Called the period.

  The second extraction unit 102 extracts the receipt data in which the target month of the specification is included in the second period from the receipt database device 4 to create the receipt database 5. The extracted receipt data is a subset of the receipt data in the receipt database device 4. The function of the second extraction unit 102 may be included in the reception database device 4, or a device having the function of the second extraction unit 102 may be provided independently of the reception database device 4. .

  The receipt database 5 can be realized by an arbitrary physical medium such as a memory device provided in a device such as a personal computer, a hard disk, an SSD, or an optical recording device such as a CD / DVD disk. The receipt database 5 may be a device that is electrically connected to the second extraction unit 102 or the receipt database device 4.

  The diseased untreated subscriber group extraction unit 104 counts the number of months for which there is a record corresponding to the treatment of the disease of interest for each individual of the diseased subscriber group 3. From the diseased subscriber group 3, a diseased subscriber group having a treatment record of a predetermined number of months or less is extracted as a diseased untreated subscriber group 6. The diseased untreated subscriber group 6 is a subscriber who has not undergone medical examination or treatment at a medical institution even though there is an abnormal value indicating illness in the medical examination data.

  Specifically, if the disease of interest is diabetes, the number of months for which there is a record of the administration of a series of drugs effective for lowering blood glucose from the medication record is counted. If the disease of interest is high blood pressure, the number of months for which there is a record of the administration of a series of drugs effective for lowering blood pressure is counted. If the counted number of months is equal to or less than a predetermined number of months, it is identified as a diseased untreated subscriber.

  In FIG. 4, the diseased subscriber group 3 is identified from the medical examination data of the year X (April X to March X + 1 March) as the first period, and further X + 1 as the second period Based on the receipt data for the fiscal year (April 1 to March 3), identify the unaffected subscriber group 6 who has not received the required number of treatments from the group 3 An example is shown.

  In this example, when the disease of interest is diabetes, the value of hemoglobin A1c (HbA1c) is used as a test item as a reference, and the NGSP standard is 7.4% or more, the diseased subscriber group 3 is specified. In this example, all of the subscribers A, B, C, and D are specified as a group of diseased subscribers because they are 7.4% or more based on the NGSP standard. In addition, from the medical examination data, all of the subscribers A to D have undergone a medical examination in December, and based on the results of the medical examination, an interview by an industrial physician / doctor is received in March. Through interviews, the subscribers A to D receive medical examinations and treatment recommendations.

  For the subscribers A, B, C, and D identified as the diseased subscriber group 3, when the number of treatments within the second period is counted, the subscriber A receives monthly treatment during the second period. The number of months counted is 12. Since subscriber B is receiving treatment only in October, the number of months counted is one. Since subscribers C and D have never received treatment, the number of months counted is zero. When the predetermined number of months is 3, subscribers B, C, and D whose counted months are 3 or less are identified as the diseased untreated subscriber group 6. Note that the predetermined number of months does not have to be 3. For example, when a high-risk patient is to be extracted more reliably, a value larger than 3 may be used. The predetermined number of months may be changed according to the type of disease. Furthermore, as a modification, even if the treatment is performed more times (months) than the predetermined number of months, if the treatment interval is more than a certain period (for example, 6 months or more), it is considered that the treatment is not properly received. It is also possible to extract as a diseased untreated subscriber.

  The extracted information about the untreated subscriber group 6 (may be anonymized personal identification information) may be stored in a recording medium such as a DVD, or in a storage device such as a memory device, a hard disk, or an SSD. It may be stored. When the extraction method of the present embodiment is implemented by an insurer's agent, a CD or DVD storing information on the disease-untreated subscriber group 6 may be sent to the insurer, or a network such as the Internet may be used. Then, it may be transmitted to the insurer's system by secure communication.

  As shown in FIG. 3 (A) or FIG. 3 (B), regarding the time when the diseased untreated subscriber group extraction unit 104 performs extraction, the receipt data of the second period is received from the end of the second period. A waiting period until the database device 4 is prepared may be set in advance. After the waiting period has elapsed since the end of the second period, the generation of the receipt database 5 and the subsequent extraction process may be started.

  When an insurer entrusts a third party to extract a group of untreated patients, the data of the medical checkup database 2 and the receipt database 5 are substituted by the insurer in the form of a recording medium such as a DVD disk. May be given to the person. Alternatively, the data of the health database 2 and the receipt database 5 may be transmitted to the agent's system by secure communication using encryption via a communication network such as the Internet. Alternatively, the data of the health database device 1 and the reception database device 4 may be transferred from the insurer to the agent by a recording medium or secure communication, and the health database 2 and the reception database 5 may be generated by the agent side system.

  The receipt database 5 and the medical examination database 2 are stored in the same optical recording device such as a DVD and provided to a contractor, and the first and subsequent steps are performed by a device capable of electronic extraction at the contractor. Is particularly convenient. In this case, personal identification information such as name and employee code in the medical examination database 2 and the receipt database 5 may be anonymized. As described above, the anonymization is to replace the personal identification information with a unique ID (character string or the like) so that the personal identification information such as the name is not specified at the consignee. A hash function may be used. The consignment source holds information in which the ID and personal identification information are associated with each other, and when receiving the ID of a disease-untreated subscriber from the consignee, the personal identification information such as the name can be restored from the ID. Shall. Furthermore, the medical examination database 2 and the receipt database 5 may be encrypted and stored in a recording medium. Thereby, it can prevent that the content of information leaks to persons other than a consignee.

  FIG. 2 is an extension of the system of FIG. 1, and a third extraction unit 105 and a second diseased untreated subscriber group extraction unit 106 are added. The 3rd extraction part 105 sets the 4th period as an additional reference period of medical examination data after the 1st period which is a reference period of medical examination data. A setting example of the fourth period is illustrated in FIG. The fourth period may start immediately after the first period elapses, or may start after a certain number of months. The fourth period may be determined independently of the second period, which is the receipt data reference period, and all or part of the fourth period may overlap with the second period. The length of the fourth period is not limited to a specific length. As an example, when the first period is from April to March in a certain year, the fourth period is from April to 3 in the next year. It may be a month or a longer period.

  The third extraction unit 105 extracts the medical examination data including the medical examination date or the medical examination month in the fourth period from the medical examination database device 1 and creates the medical examination database 7. This medical examination database 7 is a different set from the medical examination database 2.

  The second diseased / untreated subscriber group extraction unit 106 refers to the medical examination database 7 and includes the diseased / untreated subscriber group 6 extracted by the diseased / untreated subscriber group extraction unit 104. Determine if there are any subscribers who are considered cured of the disease of interest. If such a subscriber exists, the subscriber is excluded from the diseased untreated subscriber group 6 to obtain the final diseased untreated subscriber group 8. As an example of a subscriber who is considered to have treated the disease of interest, it is determined that there is a disease from the medical examination data of the first period, and after that he has not been to a medical institution or the number of treatment (months) is not Despite being sufficient, there are naturally cured subscribers. In this way, by subtracting the subscriber who is considered to have healed the disease of interest from the medical examination data of the fourth period from the group of diseased and untreated subscribers 6, it is possible to join the diseased and untreated subscribers with higher accuracy. Person can be identified.

  FIG. 5 shows an example in which the subscriber C is excluded from the diseased untreated subscriber group 6 based on the medical examination data in the fourth period. As described with reference to FIG. 4, the subscriber C is identified as a diseased subscriber from the medical examination data in the first period, and is further identified as a diseased untreated subscriber from the receipt data in the second period. However, from the health examination data of the fourth period, the value of HbA1c at the health examination in December is 5.6%, which is smaller than 7.4%. Exclude from untreated subscriber group 6. As a result, the subscribers B and D are identified as the final diseased untreated subscriber group 6.

  FIG. 6 is a block diagram illustrating a hardware configuration example of an extraction device that performs the method for extracting a pre-untreated subscriber group according to the present embodiment. The extraction device includes a processor 201, a main storage device 202, an auxiliary storage device 203, a device interface 204, a network interface 205, an input device 206, and an output device 207, which are realized as a computer device connected via a bus 208. it can.

  The processor 201 reads out the program from the auxiliary storage device 203, expands it in the main storage device 202, and executes it, whereby the first extraction unit 101, the second extraction unit 102, the third extraction unit 105, The functions of the disease subscriber group extraction unit 103, the first disease untreated subscriber group extraction unit 104, and the second disease untreated subscriber group extraction unit 106 can be realized. The medical examination database 2 and the receipt database 5 are created by using the first extraction unit 101 and the second extraction unit 102 on the insurer's device, and the patient's system is not treated (the extraction device). When extracting a group of people, the function of the 1st extraction part 101, the 2nd extraction part 102, and the 3rd extraction part 105 is unnecessary for this extraction apparatus.

  The extraction device of the present embodiment may be realized by installing a program executed by the extraction device in a computer device in advance, or the program may be stored in a storage medium such as a CD-ROM or via a network. May be distributed and installed on a computer device as appropriate.

  The main storage device 202 is a memory device that temporarily stores instructions executed by the processor 201, various data, and the like, and may be a volatile memory such as a DRAM or a non-volatile memory such as an MRAM. The auxiliary storage device 203 is a storage device that permanently stores programs, data, and the like, such as a flash memory. The medical examination database 2, the reception database 5, and the medical examination database 7 may be realized on the main storage device 202 or the auxiliary storage device 203. Further, the medical examination database device 1 and the receipt database device 4 may be realized on the auxiliary storage device 203. When extracting a group of pre-untreated patients on the surrogate side, the medical examination database device 1 and the receipt database device 4 may be realized by the insurer side system.

  The device interface 204 is an interface connected to an external storage medium. The external storage medium may be an arbitrary recording medium such as an HDD, a CD-R, a CD-RW, a DVD-RAM, a DVD-R, or a SAN (Storage area network). A DVD in which the medical examination database 2 and the reception database 5 are stored is received from the insurer, and the medical examination database 2 and the reception database 5 are read from the DVD and stored in the main storage device 202 or the auxiliary storage device 203 or both. May be. Further, the final information about the untreated subscriber group 6 acquired by the extraction method may be stored in an external storage medium connected to the device interface 204. When the extraction process is performed on the agent side, a DVD storing information on the final diseased untreated subscriber group 6 may be delivered to the insurer.

  The network interface 205 is an interface for connecting to a communication network such as a wireless LAN. Data of the medical examination database 2 or the reception database 5 may be received from the insurer side device via the interface and stored in the main storage device 202 or the auxiliary storage device 203 or both. The output device 207 is a device that displays data on the screen, and may display information on the medical examination database 2, the receipt database 5, the prevalent subscriber group 3, or the preexisting untreated subscriber group 6, for example. . When displaying the information of the diseased subscriber group 3 or the diseased untreated subscriber group 6, the personal identification information of these persons may be anonymized.

  The input device 206 is a device for inputting various instructions or information by the user. For example, there are a keyboard, a mouse, and a touch pad. Note that not all of the components shown in FIG. 6 are necessary, and some of the components may be excluded.

  FIG. 7 is a flowchart of the operation of the method for extracting a group of pre-untreated patients according to this embodiment. This flowchart shows the flow of operation of the extraction method executed by the extraction apparatus of FIG. In addition, the order of each step demonstrated below is an example, and it does not necessarily need to be performed in this order. As long as the target operation is achieved, the order of the steps may be changed as appropriate.

  The medical examination database 2 is created by extracting the medical examination data of the first period over a plurality of months from the medical examination database device 1 storing the medical examination data of the insurer's subscribers (S101).

  The receipt database 5 is created by extracting the receipt data of the second period over a plurality of months after the first period from the receipt database apparatus 4 storing the subscriber's receipt data (S102).

  Moreover, the medical examination data 7 of the 4th period which is a period after the 1st period is extracted from the medical examination database apparatus 1, and the medical examination database 7 is created (S103).

  Based on the medical checkup data for the first period extracted in step S101, a group of affected subscribers affected by the disease of interest is extracted (S104). This extraction process corresponds to the first step.

  Based on the receipt data of the second period extracted in step S102, the number of treatments for the disease of interest out of the prevalence group extracted in the first step (step S104) is less than a predetermined number. A treatment subscriber group is extracted (S105). This extraction process corresponds to the second step.

  Based on the medical examination data of the fourth period extracted in step S103, a group of subscribers determined to have recovered from the untreated subscriber group extracted in the second step (step S105). By removing the group, the final group of diseased untreated subscribers is identified (S106). This process corresponds to the third step.

  Information for identifying the final diseased untreated subscriber group is output (S107). For example, the information is stored in a recording medium such as a DVD.

  As described above, according to the present embodiment, all or part of the subscribers related to the first period and the second period are extracted from the medical examination database apparatus and the receipt database apparatus held by the insurer. Then, from the medical examination data in the first period, a subscriber who satisfies the criteria of the disease of interest is extracted as a diseased subscriber, and the number of treatments of the disease in the second period among the diseased subscribers. That are less than the predetermined number of times are extracted as diseased untreated subscribers. As a result, it is possible to easily extract a diseased untreated subscriber with high accuracy.

  In this embodiment, since it is not necessary to create time-series data that integrates medical examination and receipt data for each individual, even for small and medium-sized insurers who are difficult to create such individual time-series data, The form disclosed in this specification can be easily implemented. For example, as long as each data of medical examination and receipt is extracted on a yearly basis, it is possible to easily extract a diseased untreated subscriber with high accuracy.

  In addition, for example, when extracting medical checkup and receipt data on a year-by-year basis, there is a possibility that the extraction accuracy of a diseased untreated subscriber may be reduced for those who have a late checkup time. However, in this case as well, by adjusting the second period, it is possible to ensure a high value for the extraction accuracy of the diseased untreated subscriber.

  In addition, according to the present embodiment, even when the insurer entrusts a third party to extract a group of pre-existing untreated patients, the insurer has a medical examination data for a period of a plurality of months as the first period. This can be extracted at a time and passed to the agent, and there is no additional burden on the insurer, such as dividing the medical examination data by month.

  Hereinafter, an embodiment of the present invention will be described with reference to FIG. FIG. 8 shows examples of various periods set in Examples 1 to 4 and Comparative Examples 1 to 3, the presence or absence of the third step, the number of pretreated untreated subscribers extracted in these Examples and Comparative Examples, and the like. . In October 2015, the present inventors extracted unaffected subscribers for 2,011 insured persons aged 40 and over in a medium-sized health insurance association.

  In Example 1, a subset of medical examination data from April 2013 to March 2014 is extracted as a first period for 2,011 insured persons of the health insurance association, and a medical examination database 2 It was created. In addition, a receipt database 5 was created by extracting a subset of the receipt data from April 2014 to March 2015 as the second period. Since the first period is from April 2013 to March 2014, and the second period is from April 2014 to March 2015, the third period, which is the deviation period, is 12 months.

  In general, health checkups in companies are rarely conducted by concentrating all employees in a specific month, and are distributed over multiple months of the year in a reasonable category such as the birth month or department. Is normal. In addition, there are not a few employees who replace the data of the medical checkups received by individuals with legal checkups stipulated by the Industrial Safety and Health Act. Considering the practice that these medical checkup data are sent to the health insurance association as data for specific health checkups and specific health guidance, it is normal to think that the health checkup months of subscribers vary considerably throughout the year. is there.

  In the medical examination database 2 used in Example 1, there were many subscribers whose medical examination months were June, September, December, and March. The medical examination database 2 and the receipt database 5 are stored in a DVD disc that is an optical recording device, copied to a device that can be electronically extracted, and used for the steps after the first step.

  In the first step, 42 subscribers of 7.4% (NGSP) or more were determined to be diseased subscribers based on the value of hemoglobin A1c (HbA1c) in the medical examination database 2. Subsequently, with respect to this group of afflicted subscribers, the receipt database 5 was referred to as the second step. As a treatment related to diabetes, record the administration of a series of drugs effective for hypoglycemia, count the month with a record of treatment related to dialysis by referring to the drug classification number and medical practice code described in the receipt, In the second period of 12 months, subscribers whose counted months were 3 months or less were extracted as diseased untreated subscribers. As a result, there were 20 ill untreated subscribers.

  As Comparative Example 1, when the second period was from April 2013 to March 2014, which is the same as the first period (when the third period is 0 month), the extraction result was 33 people. It was found that the number of untreated and untreated subscribers in Example 1 was smaller than that in Comparative Example 1, and that the untreated and untreated subscribers were extracted more accurately in Example 1. This is because of the following reasons. That is, a subscriber who is newly determined to be ill usually decides to have a medical examination by an interview by the health staff conducted after the medical examination month, and in Comparative Example 1, many subjects are examined from the medical examination month. Treatment records have been counted with reference to the previous receipt database. For this reason, subscribers who have started appropriate treatment afterwards are often mistaken as untreated patients, and the number of diseased untreated subscribers to be extracted increases.

  In Example 2, a medical examination database 2 was created by extracting a subset of medical examination data from April 2013 to March 2014 as the first period. As the second period, a subset of the receipt data from June 2014 to May 2015 was extracted and the receipt database 5 was created.

  In the second embodiment, a third period (a period from the month when the first period starts (April 2013) to the month before the second period starts (June 2014) ( The gap period is 14 months. From another point of view, there is a period of 2 months after the month when the first period ends (March 2014) and before the month when the second period starts (June 2014). Opened.

  As a result, a person who is erroneously determined as untreated in Example 1 may be correctly determined as treated. Specifically, in this example, a medical checkup was received in March 2014, and a subscriber who was judged to be ill for the first time as a result decided to receive a medical checkup in the subsequent health guidance. The subscribers who started and received treatment in October, December, February and April 2015 were correctly determined to have been treated. For this reason, in Example 2, the number of diseased untreated subscribers was 19, which was one less than in Example 1.

  In Example 3, extraction was performed by adding a third step to the result of Example 1. In the third step, a subset of medical examination data from April 2014 to March 2015 after the first period (April 2013 to March 2014) is extracted, and a medical examination database is obtained. 7 was created. The medical examination database 7 was stored in a DVD disk as an optical recording device and delivered to a consignee in the same manner as in Example 1. The contractor copied the DVD disc to a device capable of electronic extraction and performed the work.

  Among the 20 untreated patients who were narrowed down in the second step, those whose hemoglobin A1c (HbA1c) value was improved to 7.0% (NGSP) in the medical checkup in 2014, although not treated It was identified from the medical examination database 7 that there was one person. For this reason, this person was excluded, and the final diseased untreated subscriber was determined to be 19 people.

  Based on the results of this Example 3, the actual consultation recommendation for the ill untreated subscriber was carried out. Consultation encouragement refers to encouraging consultation and treatment at a medical institution to prevent the severity of various diseases. Received a report from a consultation with a doctor that he / she received a medical checkup in March 2014, discovered in Example 2, and continued to receive medical checkups for subscribers who started the checkup in August 2014. It was. Therefore, if the person who made this declaration is excluded from the untreated illness subscribers, as of October 2015, when the extraction of diabetes was conducted among 2,011 insured persons aged 40 years or older of this health insurance association It can be said that there were 18 correct answers for the untreated patients.

  In Example 4, the medical examination database 2 was created with the same first period as in Example 2. In addition, the receipt database 5 was created by extracting a subset of the receipt data by setting the second period from August 2014 to July 2015, which is two months later than Example 2. In the fourth embodiment, a shift period of 16 months is provided as the third period from the start month of the first period to the month before the start month of the second period. Thus, the subscriber who received the medical examination in March 2014 and started the medical examination in August 2014 found in Example 2 was found and excluded in the same manner as in Example 2, and the untreated subscriber Was determined to be 19 people.

  In Comparative Example 2, a medical examination database 2 was created by extracting a subset of medical examination data from April 2013 to March 2014 as the first period. As a second period, a receipt database 5 was created by extracting a subset of the receipt data from October 2013 to March 2014. In this comparative example 2, as the third period from the month when the first period starts to the month before the second period starts, a shift period of 6 months is provided. That is, the period from October 2013 to March 2014 overlaps in the first period and the second period. The first step was performed in the same manner as in Example 1. In the second step, as a treatment related to diabetes, a month with a record of administration of a series of drugs effective for hypoglycemia and a record of treatment related to dialysis is referred to a medicinal effect classification number and a medical practice code described in the receipt. I counted it. And the subscriber who was treated for 2 months or less in the second period of 6 months was identified as a diseased untreated subscriber, and the number was 35 people. Mainly for subscribers whose period of medical examination is from October 2013 to March 2014 (duplicate in the second period) Oops.

  In Comparative Example 3, a medical examination database 2 was created by extracting a subset of medical examination data from April 2013 to September 2013 as the first period. As a second period, a receipt database 5 was created by extracting a subset of the receipt data from October 2013 to March 2014. In Comparative Example 3, a shift period of 6 months is provided as the third period from the month when the first period starts to the month before the second period starts. Cannot refer to health checkup data of almost half of the target population (ie, cannot refer to subjects who have received a medical checkup between October 2013 and March 2014) Could not be extracted.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

1: Medical examination database device 4: Receipt database device 2: Medical examination database 5: Receipt database 3: Prevalence subscriber group 6: Prevalence untreated subscriber group 8: Final prevalence untreated subscriber group 101: 1st extraction unit 102: 2nd extraction unit 103: prevalence subscriber group extraction unit 104: first prevalence untreated subscriber group extraction unit 105: 3rd extraction unit 106: 2nd prevalence Treatment subscriber group extraction unit 201: processor 202: main storage device 203: auxiliary storage device 204: device interface 205: network interface 206: input device 207: output device 208: bus

Claims (8)

Extracting the medical examination data of the first period over a plurality of months from the medical examination database device storing the medical examination data of the insurer's subscribers, and based on the medical examination data, the prevalence disease affected by the disease of interest A first step of extracting a group of people;
Extracting the receipt data of the second period over a plurality of months after the first period from the receipt database device storing the subscriber's receipt data, and extracting in the first step based on the extracted receipt data A second step of extracting an untreated subscriber group in which the number of treatments of the noted disease is equal to or less than a predetermined number of times from the group of diseased subscribers, . Extracting the medical examination data of the period after the first period from the medical examination database device, based on the extracted medical examination data, from the untreated subscriber group extracted in the second step, The method for extracting a diseased untreated subscriber group according to claim 1, further comprising: a third step of removing a subscriber group for which it is determined that the disease has recovered.   3. The length of the third period from the month when the first period starts to the month before the month when the second period starts is 12 months or more, 3. The extraction method of a disease-untreated subscriber group as described in the item.   4. The method for extracting a diseased untreated subscriber group according to claim 3, wherein the length of the first period is 12 months or more.   The method for extracting a diseased untreated subscriber group according to claim 3, wherein the length of the second period is 12 months or more.   The medical examination data of the first period extracted from the medical examination database apparatus and the receipt data of the second period extracted from the receipt database apparatus are recorded in an optical recording device. Item 6. A method for extracting a group of diseased untreated subscribers according to any one of Items 1 to 5.   Anonymizing the personal identification information of the subscriber in the medical examination data, anonymizing the personal identification information of the subscriber in the receipt data, and recording the medical examination data and the receipt data in the optical storage medium The method for extracting a pre-treatment untreated subscriber group according to claim 6. A pre-existing subscriber who extracts a pre-existing subscriber group affected by a disease of interest based on medical check-up data of a first period over a plurality of months in a medical check-up database device storing medical check-up data of an insurer's subscriber A group extraction unit;
Based on the receipt data of the second period over a plurality of months after the first period in the receipt database device storing the subscriber's receipt data, the group of prevalent subscribers extracted in the first step The diseased untreated subscriber group extraction device, comprising: a diseased untreated subscriber group extraction unit that extracts a group of untreated subscribers whose number of treatments of disease is equal to or less than a predetermined number of times.

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