JP2015184864A - Information processor, display device, display method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processor, a display device, a display method and a program capable of preventing reduction of effect in health examination recommendation to be made on an object person.SOLUTION: The information processor includes; storage means; input means; accumulation means; and output means. The storage means stores a data relevant to an object person of a health examination recommendation. The input means inputs a response data representing a response of the object person to the health examination recommendation. The accumulation means accumulates the input response data. The output means outputs an object person display data for displaying the object person data stored in the storage means and the response data accumulated by the accumulation means.

Description

  Embodiments described herein relate generally to an information processing apparatus, a display apparatus, a display method, and a program for displaying data related to a person who is recommended to receive a medical examination.

  Conventionally, medical remuneration statements (hereinafter referred to as “receipt”) and medical checkups conducted by business owners (hereinafter referred to as “health check records”) Was stored on a paper medium (paper base).

  On the other hand, in recent years, electronic digitization of receipts has progressed, and digitization of medical examination records has also started. According to this, it is possible to collate various electronic data (receipt and medical examination record electronic data) held by the health insurance association or business owner, etc. (match analysis). Attempts have been made to extract high-risk individuals for diseases. A high-risk person is a person who has a high risk of developing a medical condition such as a lifestyle-related disease because he / she has not received medical care (diagnosis or treatment) even though his / her health condition is poor (that is, he / she has not been treated) Person).

  The purpose of this kind of high-risk person (hereinafter referred to as the target person) is to consult with a doctor such as a family doctor at an early stage, and to prevent the onset of lifestyle-related diseases such as hypertension and diabetes and the onset of complications. Encouraged consultations are made.

  For example, an intervention service for improving the health level is proposed from the result of the medical examination. However, in this case, the purpose is to improve health and not a recommendation for medical treatment for untreated patients, so you agree to receive the service for the current year and fall under the next year's service target. This is basically irrelevant, and even if the service is used in the current year, the next year can be the target of the intervention service, and even if the service is rejected, the next year is not necessarily the target of the intervention service. Therefore, management that spans multiple years focusing on consent and denial of service provision is not performed. If a service is used in the current year, it can be subject to intervention services in the next year, and handling in the case of denial of service is not considered.

JP 2004-341611 A

  In general, an insurer or the like cannot force a subject to undergo a medical examination. For this reason, for example, even if a consultation recommendation is implemented for a subject, there are a certain percentage of subjects who do not receive a consultation for various reasons such as busy work. In addition, there are subjects who decline or even refuse to receive a consultation recommendation.

  Since these subjects do not receive medical treatment as a result, most of them are likely to be extracted as high-risk individuals in the next fiscal year. For these subjects, consultation recommendations will be implemented every year, but even if similar consultation recommendations are implemented each year, there are few candidates who will start consultation. In addition, for example, in the first year, a medical examination started in response to a medical examination recommendation, but even if a medical examination recommendation is implemented every time the fiscal year is repeated, the medical examination is often not received. There is also concern that the number of people who refuse will increase.

  Considering that the purpose of the consultation recommendation is to start the consultation with the target person, the target person who declines or refuses to receive the consultation recommendation as mentioned above, and the target who does not start the consultation even if the consultation recommendation is implemented The increase in the number of people means that the effectiveness of the consultation recommendation is decreasing. This leads to an increase in the medical condition of the subject and further causes an increase in future medical expenses.

  Therefore, it is desired to construct a mechanism that allows different measures to be taken for such subjects, and to suppress a decrease in the effectiveness of consultation recommendation.

  Therefore, the problem to be solved by the present invention is to provide an information processing device, a display device, a display method, and a program that are useful in order to suppress a decrease in the effect of the consultation recommendation conducted on the subject. .

  The information processing apparatus according to the embodiment includes a storage unit, an input unit, a storage unit, and an output unit. The storage means stores target person data related to a target person who is recommended for medical examination. The input means inputs correspondence data indicating the correspondence of the subject to the consultation recommendation. The storage means stores the input correspondence data. The output means outputs the target person display data for displaying the target person data stored in the storage means and the corresponding data stored in the storage means.

1 is a block diagram mainly showing an example of a functional configuration of an information processing apparatus according to an embodiment. The flowchart which shows an example of the process sequence of the subject display process of the first year. The figure which shows an example of the data structure of the subject data stored in a subject data storage part in the first year. The figure which shows an example of the data structure of the corresponding | compatible data storage part in which the corresponding | compatible data of the first year were accumulate | stored. The flowchart which shows an example of the process sequence of a subject extraction process. The flowchart which shows an example of the process sequence of the subject display process after the 2nd fiscal year. The figure which shows an example of the data structure of the subject data stored in the subject data storage part in 2 years. The figure which shows an example of the display screen at the time of subject display data being displayed in this embodiment. The figure which shows an example of the data structure of the corresponding data of 2 years input by the input part. The figure which shows an example of the data structure of the corresponding | compatible data storage part in which the corresponding | compatible data of 2 years were accumulate | stored. The figure for demonstrating the effect of consultation recommendation at the time of using the information processing apparatus which concerns on this embodiment. The figure for demonstrating the effect of the consultation recommendation in case the information processing apparatus which concerns on this embodiment is not used. The figure which shows an example of the data structure of the corresponding | compatible data storage part in which the correspondence data of the first year in 2nd Embodiment were accumulate | stored. The flowchart which shows an example of the process sequence of the subject display process after the 2nd fiscal year. The figure which shows an example of the display screen at the time of subject display data being displayed in this embodiment. The figure which shows an example of the data structure of the corresponding data of 2 years input in the input part. The figure which shows an example of the data structure of the corresponding | compatible data storage part in which the corresponding | compatible data of 2 years were accumulate | stored. The figure for demonstrating the effect of consultation recommendation at the time of using the information processing apparatus which concerns on this embodiment. The figure for demonstrating the effect of the consultation recommendation in case the information processing apparatus which concerns on this embodiment is not used. The figure which shows an example of the display screen at the time of subject display data being displayed in 3rd Embodiment. The figure for demonstrating the effect of consultation recommendation at the time of using the information processing apparatus which concerns on this embodiment.

  Hereinafter, each embodiment will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a block diagram mainly showing a functional configuration of the information processing apparatus according to the present embodiment. The information processing apparatus according to the present embodiment is used to display data related to a target person when a recommendation is made to a target person (high risk person) who is at a high risk of getting a medical condition. Note that this information processing apparatus is used by, for example, a medical health staff of a health insurance association that encourages consultation.

  As illustrated in FIG. 1, the information processing apparatus 10 includes a health data storage unit 11, a target person extraction unit 12, a target person data storage unit 13, an input unit 14, a corresponding data storage unit 15, a display data generation unit 16, and an output unit. 17 is included.

  Of these units 11 to 17, the health data storage unit 11, the subject data storage unit 13, and the corresponding data storage unit 15 are stored in, for example, an external storage device (not shown) provided in the information processing apparatus 10. May be. Further, the target person extraction unit 12, the input unit 14, the display data generation unit 16, and the output unit 17 may be realized by a computer of the information processing apparatus 10 executing a program. This program can be stored and distributed in advance in a computer-readable storage medium, or may be downloaded to the information processing apparatus 10 via a network, for example.

  In the health data storage unit 11, for example, data on health for each insurance subscriber such as health insurance (hereinafter referred to as health data) is stored. The health data stored in the health data storage unit 11 includes receipt data and medical examination data.

  The receipt data is electronic data of a medical remuneration statement (receipt) that the medical institution charges to the insurer (for example, health insurance association) when the insurance subscriber uses the medical institution. The receipt data includes, for example, the date when the insurance subscriber used the medical institution, the contents of the medical care received at the medical institution, and the like.

  The medical checkup data is electronic data of the result of the medical checkup (checkup record) for the insurance subscriber. In addition to height and weight, the medical examination data includes, for example, values that serve as indices for determining the degree of progression of various diseases (hereinafter referred to as index values related to diseases).

  Based on the health data (receipt data and medical examination data) for each insurance subscriber stored in the health data storage unit 11, the target person extraction unit 12 makes the above-mentioned medical condition serious among the insurance subscribers. Extract high-risk subjects. Here, the target person extracted by the target person extraction unit 12 includes, for example, a person who is not receiving medical care (diagnosis or treatment) even though his / her health condition is poor (that is, an untreated patient).

  The subject extraction unit 12 stores the extracted data related to the subject (hereinafter referred to as subject data) in the subject data storage unit 13. In this subject data, for example, in addition to identification information for identifying the subject (hereinafter referred to as subject ID) and the name of the subject (hereinafter referred to as subject name), the subject's health checkup Results (hereinafter referred to as medical examination contents) and the like.

  The input unit 14 is a target person identified by a target person ID and a target person name included in the target person data stored in the target person data storage unit 13 (that is, a target person extracted by the target person extraction unit 12). Enter the correspondence data indicating the correspondence to the consultation recommendation. This correspondence data is assumed to be input in response to an operation of an administrator (for example, a medical health staff) of the information processing apparatus 10 with respect to, for example, a keyboard.

  Corresponding data input by the input unit 14 is stored in the corresponding data storage unit 15. The correspondence data accumulation unit 15 accumulates correspondence data every time a recommendation is made.

  The display data generation unit 16 acquires target person data regarding the target person to be encouraged from the target person data storage unit 13 and generates target person display data for displaying the target person data. In addition, when the correspondence data is stored in the correspondence data storage unit 15 as described above, the display data generation unit 16 displays the correspondence data indicating the past correspondence to the consultation recommendation of the subject of the consultation recommendation. Obtained from the storage unit 15. In this case, the display data generation unit 16 generates target person display data for displaying the target person data and the acquired corresponding data.

  The display data generated by the display data generation unit 16 is output by the output unit 17.

  Next, the operation of the information processing apparatus 10 according to the present embodiment will be described. In the following explanation, for example, it is assumed that a health examination of each employee or the like is performed once a year in a company, and a recommendation is made several months after the health examination. In other words, consultation recommendations are to be implemented annually. In this case, it is assumed that the health data storage unit 11 stores the health data of each employee of the company.

  In the following, the processing of the information processing device 10 (hereinafter referred to as target person display processing in the first year) when the consultation recommendation for the first year (first year) is carried out, and the examination recommendation for the second year or later (after the second year) The process of the information processing apparatus 10 when the process is performed (hereinafter referred to as a target person display process after the second year) will be described.

  First, with reference to the flowchart of FIG. 2, the process sequence of the subject display process in the first year will be described. Here, since it is the first year, it is assumed that no consultation recommendation has been performed in the past, and no corresponding data is stored in the corresponding data storage unit 15.

  In this case, the subject extraction unit 12 performs subject extraction processing based on the health data stored in the health data storage unit 11 (step S1). According to this subject extraction process, subjects who are at high risk of getting a serious medical condition are extracted from the employees of the company. Details of the target person extraction process will be described later.

  Next, the target person extraction unit 12 stores target person data including the target person ID, the target person name, and the medical examination contents of the target person in the target person data storage unit 13 for identifying the extracted target person. (Step S2). Note that the target person ID and the target person name may be held inside the information processing apparatus 10 or may be acquired from another apparatus (system) or the like. The contents of the medical examination of the subject are acquired from the health data (health examination data) of the subject stored in the health data storage unit 11.

  Here, FIG. 3 is a diagram illustrating an example of the data structure of the subject data stored in the subject data storage unit 13.

  As shown in FIG. 3, the target person data stored in the target person data storage unit 13 includes a target person ID, a target person name, and medical examination contents in association with each other. The target person ID is identification information for identifying the target person extracted by the above-described target person extracting unit 12. The subject name is the name of the subject identified by the subject ID. The contents of the medical examination are the result of the medical examination for the subject identified by the subject ID. Although omitted in FIG. 3, the contents of the medical examination include an index value (for example, blood pressure value) and the like regarding the height, weight, and various diseases of the subject.

  In the example illustrated in FIG. 3, the target person data storage unit 13 stores target person data regarding the target persons A to L. For example, the target person data related to the target person A includes the target person ID “1”, the target person name “target person A”, and the health examination content “health examination content A”.

  Further, the subject data shown in FIG. 3 may include other data such as a treatment status (history).

  Returning to FIG. 2 again, when the target person data is stored in the target person data storage unit 13 as described above, for example, medical health staff in charge of consultation recommendation (hereinafter simply referred to as a person in charge) In order to implement a medical examination recommendation for the patient, it is possible to instruct the display of the subject data by operating the information processing apparatus 10.

  In this case, since the corresponding data is not stored in the corresponding data storage unit 15 as described above, the display data generation unit 16 displays the target person based on the target person data stored in the target person data storage unit 13. Data is generated (step S3). In this case, the display data generation unit 16 generates target person display data for displaying a list of target persons (data), for example.

  The output unit 17 outputs the target person display data generated by the display data generation unit 16 (step S4). In this case, the target person display data is displayed on the display device by being output to a display device such as a display provided in the information processing apparatus 10, for example.

  The above-mentioned person in charge confirms the displayed target person display data, grasps the target person who is the target of the consultation recommendation, and uses the contents of the medical examination included in the target person display data to recommend the patient. Can be implemented. Generally, the subject's health risk (risk that the medical condition becomes serious), a lifestyle interview questionnaire, etc. are notified to the subject. Thereafter, consultation is recommended by telephone or interview. In the case of a telephone call, first, the results of a lifestyle interview questionnaire are interviewed, and thereafter, consultation recommendations or lifestyle improvement guidance are provided by telephone. In the case of interviews, lifestyle interviews, consultation recommendations, etc. are conducted at the first interview, and then consultation recommendations or lifestyle improvement guidance are provided over the phone. By implementing such a consultation recommendation, the subject can appropriately understand the risk of his / her medical condition becoming more serious, for example, so the subject is expected to actively start the consultation. . Whether or not a consultation is recommended by telephone or interview, the subject is first asked whether he / she can receive a consultation recommendation, and is not implemented if he / she refuses to receive the recommendation.

  Next, the input unit 14 inputs data indicating the correspondence of the subject to the consultation recommendation based on, for example, the input by the person in charge of the consultation recommendation for the subject (step S5). The data input by the input unit 14 includes reaction data. Response data is based on whether or not the patient is eligible to receive a consultation recommendation (ie, whether or not to receive a consultation recommendation). As a result, he / she agrees to receive the recommendation or refuses to receive the recommendation. This is data indicating that the Correspondence data (response data) is input for each target person who is recommended for medical examination.

  The correspondence data may be input by operating the keyboard or the like, for example, correspondence data (electronic file) created on a system different from the information processing apparatus 10 may be input from the system. I do not care.

  The correspondence data input by the input unit 14 is accumulated (stored) in the correspondence data accumulation unit 15 (step S6).

  Here, FIG. 4 is a diagram showing an example of the data structure of the corresponding data storage unit 15 in which the corresponding data for the first year is stored. In the correspondence data storage unit 15, data indicating the correspondence of the subject to the consultation recommendation is accumulated for each subject.

  As shown in FIG. 4, the correspondence data includes a subject ID, a subject name, and reaction data. The target person ID and the target person name are as described above. The response data includes “consent” indicating that he / she has agreed to receive the consultation recommendation and “refusal” indicating that he / she has refused to receive the consultation recommendation. The reaction data may be a code indicating “agreement” or “rejection”.

  In the example illustrated in FIG. 4, for example, correspondence data including a subject ID “1”, a subject name “subject A”, and reaction data “agreement” is accumulated. This correspondence data indicates that the subject A has agreed to receive a consultation recommendation. In addition, for example, correspondence data including a subject ID “2”, a subject name “subject B”, and reaction data “reject” is accumulated. According to this correspondence data, it is shown that the subject B refuses to receive a consultation recommendation. Here, the correspondence data indicating the correspondence between the subjects A and B has been described, but the same applies to the correspondence data indicating the correspondence between the other subjects, and detailed description thereof will be omitted. Note that the correspondence data storage unit 15 stores correspondence data for all the subjects who are candidates for consultation in the first year.

  As described above, according to the target person display process in the first year, data related to the person who is the subject of the consultation recommendation can be presented to the person in charge, and data indicating the correspondence of the subject to the recommendation is stored. It can be stored in the unit 15.

  Next, with reference to the flowchart of FIG. 5, the processing procedure of the above-described subject extraction process (the process of step S1 shown in FIG. 2) will be described.

  First, the subject extraction unit 12 accesses the health data storage unit 11 to obtain the medical examination data of the employees of the company described above (step S11). Note that the medical examination data acquired by the subject extraction unit 12 includes index values and the like related to various diseases as described above.

  The target person extraction unit 12 is configured to select a plurality of items according to the progress state (pathology) of the target disease based on an index value related to a specific disease (hereinafter referred to as target disease) included in the acquired medical checkup data of the employee. The employee is classified (i.e., hierarchized) into the hierarchy (step S12). Here, the division into two layers is included in the hierarchization.

  Next, the subject extraction unit 12 determines whether the index value related to the target disease is abnormal with respect to a predetermined reference value (recommendation determination value) according to the hierarchized result (hereinafter, referred to as an employee) , Expressed as a sick person) (step S13).

  Here, assuming that the target disease is diabetes, in step S13, for example, an employee whose hemoglobin A1c (HbA1c), which is an index value related to diabetes, is higher than 7.0% (In other words, a person whose diabetes is likely to become severe at an early stage) is extracted as a patient.

  Next, the following processes in steps S14 to S16 are executed for each of the affected persons extracted in step S13. Here, the sick who is the target of the processing in steps S14 to S16 is referred to as the target sick.

  In this case, the subject extraction unit 12 accesses the health data storage unit 11 to acquire the receipt data included in the health data of the subject affected person (step S14). In this case, it is assumed that the subject extraction unit 12 acquires, for example, receipt data for 6 months from the 4th month to the 9th month from the month when the medical examination is performed (month in which the medical examination is performed).

  The subject extraction unit 12 determines whether or not the subject affected person is a treatment continuator based on the acquired receipt data (step S15). In the process of step S15, treatment history such as insulin injection and oral fluid administration related to the treatment of the target disease (here, diabetes) is identified from the receipt data (medical receipt and dispensing receipt), and for the above six months. When the number of months with at least one treatment history is 3 or more, it is determined that the person to be processed is a treatment continuator. On the other hand, if the number of months with at least one treatment history in six months is two or less, it is determined that the person to be treated is not a treatment continuator. A value different from the above can be set for the number of months having a treatment history when judging a treatment continuation person.

  When it is determined that the target patient is a continuation of treatment (YES in step S15), the target patient is excluded from the recommended consultation (step S16). On the other hand, when it is determined that the target patient is not a treatment continuator (NO in step S15), the process in step S16 is not executed, and the target patient is not excluded from the consultation recommendation target and is recommended for the consultation. It becomes the target person of.

  Next, it is determined whether or not the above-described processing of steps S14 to S16 has been executed for all the affected persons extracted in step S13 (step S17). When it is determined that the processing has not been executed for all the sick persons (NO in step S17), the process returns to the above-described step S14 and is repeated. In this case, the process of steps S14 to S16 is executed with a patient who has not been processed as a target patient. That is, the processing of steps S14 to S16 is executed in a loop for each of all the patients. On the other hand, when it is determined that the process has been executed for all the prevalent persons (YES in step S17), the target person extraction process is terminated.

  As described above, according to the subject extraction process, based on each employee's health data (receipt data and medical examination data), for example, a person who has a high risk of becoming seriously affected by the disease of the target disease such as diabetes. A person who is not a continuator (that is, a patient who has not been treated) can be extracted as a candidate for a consultation recommendation.

  Although the target disease has been described as being diabetic here, diseases other than diabetes (such as lifestyle-related diseases) may be the target disease, or a plurality of diseases may be the target disease. Further, the processing described in FIG. 5 is an example, and other processing may be executed as long as it is possible to extract a target person who should be a subject of recommendation based on health data.

  Next, with reference to the flowchart of FIG. 6, the process procedure of the subject display process after the second year will be described. Here, the case where a consultation recommendation for the second year is implemented will be described. That is, it is assumed that the correspondence data storage unit 15 stores the correspondence data shown in FIG. 4 (that is, the correspondence data for the first year).

  In this case, the processes of steps S21 and S22 corresponding to the processes of steps S1 and S2 shown in FIG. 2 are executed. That is, the target person data regarding the target person extracted in step S21 is stored in the target person data storage unit 13 in step S22. FIG. 7 shows an example of the data structure of the subject data stored in the subject data storage unit 13 in step S22. As shown in FIG. 7, the subjects extracted in step S22 include those who were extracted as high-risk people (subjects) even in the first year, such as subjects B, F, H, J, and K. In many cases, a person who is extracted as a high-risk person (target person) for the first time in two years, such as the target persons M to S, is included.

  As described above, when the target person data is stored in the target person data storage unit 13, the person in charge operates the information processing apparatus 10 in order to encourage the target person to receive the consultation. Display can be instructed. In this case, the following steps S23 and S24 are executed for each of the subjects extracted in step S21. In the description of steps S23 and S24, the target person to be processed in steps S23 and S24 is referred to as a process target person.

  The display data generation unit 16 acquires correspondence data associated with the person to be processed among the correspondence data stored in the correspondence data storage unit 15 (hereinafter referred to as correspondence data of the person to be processed) (step S23). . In this case, the display data generation unit 16 acquires correspondence data including a target person ID (and a target person name) for identifying the processing target person. In the case where correspondence data for a plurality of years is accumulated in the correspondence data storage unit 15, all the correspondence data for the plurality of years are acquired.

  Next, the display data generation unit 16 acquires the number of times the processing target person has refused to receive a consultation recommendation in the past (hereinafter referred to as the number of rejections) based on the acquired correspondence data of the processing target person ( Step S24). The number of rejections is acquired by counting the number of corresponding data including the reaction data “rejected” among the acquired correspondence data of the processing target person.

  Here, although it has been described that the corresponding data of the processing target person is stored in the corresponding data storage unit 15, as described above, no corresponding data is stored for the target person extracted as a high-risk person for the first time. In such a case, it is assumed that 0 is acquired as the number of rejections described above.

  When the process of step S24 is executed, it is determined whether or not the processes of steps S23 and S24 described above have been executed for all the subjects extracted in step S21 (step S25). When it is determined that the process has not been executed for all the target persons (NO in step S25), the process returns to step S23 described above and the process is repeated. In this case, the processes in steps S23 and S24 are executed with the target person who has not been processed as the target person. That is, the processes in steps S23 and S24 are executed in a loop for each of all subjects. On the other hand, when it is determined that the process has been executed for all the target persons (YES in step S25), the display data generation unit 16 is acquired in the target person data stored in the target person data storage unit 13 and in step S24. Target person display data for displaying the number of rejections is generated (step S26).

  The output unit 17 outputs the target person display data generated by the display data generation unit 16 (step S27). In this case, the target person display data is displayed on the display device by being output to the display device, for example.

  Here, FIG. 8 shows an example of a display screen when the above-described subject display data is displayed. On the display screen 100 shown in FIG. 8, a target person list indicating the target person data associated with each target person and the number of rejections in a list format is displayed as the target person display data.

  The above-mentioned person in charge can easily grasp the target person who has refused to receive a consultation recommendation in the past by checking the target person list displayed in this way. When consulting on whether or not to make a recommendation, it is possible to take measures (such as initiatives and measures different from the past) that agree to receive the recommendation. Specifically, the person in charge can take measures such as intensive follow-up for a subject who has a large number of refusals, or requesting a person in charge of personnel at a company to respond.

  Note that the number of rejections displayed on the display screen 100 shown in FIG. 8 is 1 or 0 because it is a value calculated based on the corresponding data shown in FIG. Returning to FIG. 6 again, processing in steps S28 and S29 corresponding to the processing in steps S5 and S6 shown in FIG. 2 is executed. According to the processing in steps S28 and S29, correspondence data indicating each correspondence of the subject to the consultation recommendation for the second year is input, and the correspondence data is accumulated in the correspondence data storage unit 15.

  Specifically, if the correspondence data (including reaction data) shown in FIG. 9 is input in step S28, the correspondence data storage unit 15 stores the correspondence data for each year as shown in FIG. The

  Here, the case where the consultation recommendation for the second year is implemented has been described, but for example, the same processing is executed when the consultation recommendation for the third year is implemented, and the corresponding data storage unit 15 shown in FIG. In addition, correspondence data of each subject indicating the correspondence to the consultation recommendation in the third fiscal year is accumulated. The same applies to the fourth and subsequent years.

  According to the target person display process after the above-mentioned 2nd year, in addition to the target person data regarding the target person who is the subject of the consultation recommendation, the number of times that the target person refused to receive the recommendation in the past (number of rejections) is presented. In addition, correspondence data indicating the correspondence of the subject to the consultation recommendation can be further accumulated in the correspondence data accumulation unit 15.

  Here, it has been described that the number of refusals is acquired based on all the correspondence data stored in the correspondence data storage unit 15, but in the case where correspondence data for a larger number of years is accumulated. May be configured such that the number of refusals is acquired based on correspondence data for the most recent years.

  Further, in the present embodiment, it has been described that the consultation recommendation is performed every fiscal year. However, for example, when the consultation recommendation is performed a plurality of times within the fiscal year, the above-described FIG. The process shown in FIG. 6 is executed, and the above-described process shown in FIG.

  Hereinafter, with reference to FIG. 11, the effect of the consultation recommendation when the information processing apparatus 10 according to the present embodiment is used will be specifically described. Here, for example, in Company W having about 4,000 employees, as described above, subjects who are at high risk of getting a serious medical condition (high-risk subjects) are extracted, and consultations are encouraged for those subjects. The case will be described.

  In FIG. 11, the number of subjects, the number of rejected previous year, the number of recommended consenters, the number of recommended rejections, the consent rate, and the number of consent converted persons are shown for each year. The number of target persons is the number of target persons extracted as high-risk persons among employees of the company W. The number of rejections in the previous year is the number of subjects who rejected the consultation recommendation in the previous year. The number of recommended consenters is the number of target persons who have agreed to receive a consultation recommendation among the target persons. In addition, the number of recommended refusal persons is the number of target persons who have refused to receive a consultation recommendation among the target persons. The number of recommended refusals is calculated by “the number of target persons—the number of recommended consenters”. The consent rate is a value calculated by “the number of recommended consensus / number of subjects”. In addition, the number of persons who consented to change is the number of eligible persons who refused to receive a recommendation in the previous year but agreed to receive the recommendation in this year. On the other hand, FIG. 12 shows the number of target persons, the number of rejected persons in the previous year, the number of recommended consenters, the number of recommended rejections, the consent rate, and the number of consent-converted persons when the company X does not use the information processing apparatus 10 according to the present embodiment. It shows the transition of. In FIG. 12, it can be seen that the number of rejected persons and the number of recommended refusing persons in the previous year tend to increase even though the number of target persons has increased or decreased within a predetermined range. This is because, in many cases, the previous year's refusals in each year do not agree to receive a consultation recommendation in the same way as in the previous year. (I.e., the refusals of the previous year accumulate as the number of recommended refusals). In connection with this, in FIG. 12, the consent rate is drastically decreasing year by year. That is, as shown in FIG. 12, in Company X that does not use the information processing apparatus 10 according to the present embodiment, the number of refusals in the previous year has increased year by year and the consent rate has also decreased, so it is recommended to receive a consultation. It can be said that the effect of the consultation recommendation is decreasing year by year.

  In contrast, in FIG. 11, compared with FIG. 12, a decrease in the consent rate for the consultation recommendation (receiving) after the second year is suppressed. This is because the person in charge of the consultation recommendation recognizes the number of refusals to be included in the above-mentioned target list, for example, as a consultation recommendation in the category of consultation recommendation resources (for example, human resources) that can be allocated at his discretion. It can be said that this is a result of intensive follow-up to the subject who frequently changed the number of refusals. Although omitted in FIG. 11, a decrease in the consent rate is suppressed, thereby suppressing a decrease in the number of patients who have started the examination (the number of subjects who have started the examination). As a result, the recommended success rate ( It is possible to suppress a decrease in the number of medical examination starters / number of subjects).

  Each value shown in FIG. 11 can be calculated based on the subject data stored in the subject data storage unit 13 and the correspondence data stored in the correspondence data storage unit 15. Therefore, although FIG. 11 is used here to explain the effect of the consultation recommendation, it is also possible to display a list as shown in FIG. 11 for the purpose of verifying the effect of the consultation recommendation by the person in charge, for example. It is.

  As described above, in this embodiment, the data indicating the correspondence of the subject to the consultation recommendation is input, the data is accumulated in the correspondence data accumulation unit 15, and the subject data stored in the subject data storage unit 13 is stored. And the target person display data for displaying the correspondence data stored in the correspondence data storage unit 15 is output (displayed). In addition, as correspondence data in this case, the number of refusals (first number of times) in which the subject has refused to receive a consultation recommendation in the past is displayed.

  In the present embodiment, such a configuration allows the person in charge to grasp the number of rejections of each target person. For this reason, by taking new measures, etc., for subjects who are frequently rejected, the decline in the consent rate for the consultation recommendation is suppressed (that is, the probability of receiving the consultation recommendation itself is reduced and the consultation is reduced). The probability of leading to the start can be increased), and as a result, it is possible to suppress a decrease in the effect of the consultation recommendation performed on the subject. In other words, it is possible to minimize the accumulation of the previous year's refusals that are inevitably caused by the encouragement of consultations in the second year and beyond, and to carry out efficient consultation recommendations with limited resources. In this way, by suppressing the decline in the effectiveness of consultation recommendations, it is possible to prevent the medical condition of the subject from becoming serious and to improve the efficiency of medical services. Become.

  In the present embodiment, for example, the target person display data is displayed on the information processing apparatus 10 (display apparatus) by outputting the target person display data to a display device provided in the information processing apparatus 10. Although mainly described, the configuration may be such that the target person display data is printed on a print medium (for example, a paper medium) by being output to a printing apparatus such as a printer. Further, the target person display data may be transmitted to an external apparatus other than the information processing apparatus 10 and displayed on the external apparatus.

  In the present embodiment, the processing illustrated in FIGS. 2 and 6 has been described as being executed in the information processing apparatus 10, but the processing is executed in the external apparatus, and the information processing apparatus 10 is output by the external apparatus. It may operate as a display device that displays the displayed target person display data.

  Furthermore, in the present embodiment, the information processing apparatus 10 has been described as having the health data storage unit 11, the target person data storage unit 13, and the corresponding data storage unit 15. However, the health data storage unit 11, the target person data storage For example, at least one of the unit 13 and the corresponding data storage unit 15 may be provided in, for example, a server (hereinafter referred to as a cloud server) that executes an online storage service or other various cloud computing services. I do not care. In this case, the information processing apparatus 10 can upload necessary data to the cloud server or download it from the cloud server by executing communication with the cloud server.

  In the present embodiment, each functional unit illustrated in FIG. 1 is described as being included in one device (information processing device 10). However, these functional units are distributed and arranged in a plurality of devices. It may be a configuration. In this case, the effect of the present embodiment can be realized by cooperation of a plurality of devices.

  Since the processing of the present embodiment can be realized by a computer program, the computer program is installed and executed on a computer through a computer-readable storage medium that stores the computer program, as in the present embodiment. The effect of can be easily realized.

(Second Embodiment)
Next, a second embodiment will be described. The functional configuration of the information processing apparatus 10 according to the present embodiment is the same as that of the first embodiment described above, and will be described with reference to FIG. 1 as appropriate. Further, the description of the parts common to the first embodiment described above will be omitted, and the parts different from the first embodiment will be mainly described.

  In the present embodiment, the input unit 14 inputs correspondence data including reaction data and action data, and the point that the input correspondence data is accumulated in the correspondence data accumulation unit 15 is the same as the first embodiment described above. Is different. The behavior data is data indicating whether or not the subject has started consultation. In the present embodiment, the display data generation unit 16 generates target person display data for displaying the number of non-consultation after consent, which will be described later, in addition to the target person data and the number of rejections described in the first embodiment. To do.

  Hereinafter, the target person display process for the first year and the target person display process for the second and subsequent years executed by the information processing apparatus 10 according to the present embodiment will be described.

  First, the processing procedure of the subject display process for the first year will be described with reference to the flowchart of FIG. 2 described above. First, as described in the first embodiment, steps S1 to S4 shown in FIG. 2 are executed.

  Next, the input unit 14 inputs data indicating the correspondence of the subject to the consultation recommendation based on, for example, the input by the person in charge of the consultation recommendation for the subject (step S5). The correspondence data input here includes reaction data and action data. The reaction data is as described in the first embodiment. The behavior data is data indicating whether or not the subject has started consultation. The person in charge can confirm whether or not the subject has started a medical examination by referring to, for example, health data (receipt data) stored in the health data storage unit 11.

  FIG. 13 is a diagram illustrating an example of a data structure of the correspondence data storage unit 15 in which correspondence data of the first year is accumulated. As shown in FIG. 13, in the correspondence data storage unit 15, data indicating the correspondence of the subject to the consultation recommendation is accumulated for each subject.

  The correspondence data includes a subject ID, a subject name, reaction data, and action data. The behavior data includes “starting consultation” indicating that the consultation has started and “no consultation” indicating that the consultation has not started. The subject ID, subject name, and reaction data are as described in FIG. 4 and the like described above.

  In the example illustrated in FIG. 13, correspondence data including, for example, a subject ID “1”, a subject name “subject A”, reaction data “agreement”, and behavior data “start consultation” is accumulated. According to this correspondence data, it is shown that the subject A agrees to receive a consultation recommendation and has started the consultation. Corresponding data including, for example, a subject ID “2”, a subject name “subject B”, reaction data “reject”, and behavior data “no medical examination” are accumulated. According to this correspondence data, it is indicated that the subject B refuses to receive the consultation recommendation and does not start the consultation.

  Further, the correspondence data storage unit 15 stores correspondence data including, for example, a subject ID “10”, a subject name “subject J”, reaction data “consent”, and behavior data “no medical examination”. According to the correspondence data, it is shown that the subject J has agreed to receive a consultation recommendation but has not started the consultation. As described above, among the subjects, there is a person who agrees to receive a consultation recommendation (that is, despite receiving a consultation recommendation) and does not start the consultation.

  Here, the correspondence data indicating the correspondence between the target persons A, B, and J has been described, but the same applies to the correspondence data indicating the correspondence between other target persons.

  As described above, according to the target person display process in the first year, data related to the target person who is the subject of the consultation recommendation can be presented to the person in charge, and the data indicating the correspondence of the target person to the recommendation (reaction data) And action data) can be stored in the corresponding data storage unit 15.

  Next, with reference to the flowchart of FIG. 14, the process procedure of the subject display process after the second year will be described. Here, the case where a consultation recommendation for the second year is implemented will be described. That is, it is assumed that the correspondence data storage unit 15 stores the correspondence data shown in FIG.

  In this case, the processes of steps S31 and S32 corresponding to the processes of steps S1 and S2 shown in FIG. 2 are executed. That is, the target person data regarding the target person extracted in step S31 is stored in the target person data storage unit 13 in step S32. Here, it is assumed that the target person data shown in FIG. 7 is stored in the target person data storage unit 13.

  As described above, when the target person data is stored in the target person data storage unit 13, the person in charge operates the information processing apparatus 10 in order to encourage the target person to receive the consultation. Display can be instructed.

  In this case, the following steps S33 to S36 are executed for each of the subjects extracted in step S31. In the description of steps S33 to S35, the target person to be processed in steps S33 to S36 is referred to as a process target person.

  The display data generation unit 16 acquires correspondence data (corresponding data of the processing target person) associated with the processing target person from the corresponding data stored in the corresponding data storage part 15 (step S33). In this case, the display data generation unit 16 acquires correspondence data including a target person ID (and a target person name) for identifying the processing target person.

  Next, the display data generation unit 16 acquires the number of times that the processing subject has refused to receive a consultation recommendation in the past (rejection count) based on the acquired correspondence data of the processing subject (step S34). The number of rejections is acquired by counting the number of corresponding data including the reaction data “rejected” among the acquired correspondence data of the processing target person.

  In addition, the display data generation unit 16 has agreed that the processing subject has received a consultation recommendation in the past based on the acquired correspondence data of the processing subject, but the number of times the consultation has not started (the number of non-consultation after consent). ) Is acquired (step S35). The number of non-consultation after consent is obtained by counting the number of correspondence data including the response data “consent” and the action data “no medical examination” among the correspondence data of the obtained processing target person.

  Here, it has been described that the corresponding data of the processing target person is stored in the corresponding data storage unit 15, but no corresponding data is stored for the target person extracted as a high-risk person for the first time. In such a case, 0 is acquired as the above-mentioned number of refusals and the number of non-consultation after consent.

  Next, based on the number of rejections acquired in step S34 and the number of non-consents after consent acquired in step S35, the display data generation unit 16 is an index (hereinafter referred to as a delegation of the person to be processed regarding consultation recommendation or consultation). , Expressed as a negative index) (step S36). This negative index gives weight to negative events such as refusal to receive a medical examination recommendation or consent to receive a medical examination recommendation but did not actually start the medical examination. It is a value integrated.

  Here, the negative index calculation process in step S36 will be specifically described. Here, it is assumed that, for example, 7 (points) is assigned for one rejection, and 3 (points) is assigned for one non-consultation after consent. In this case, the negative index is calculated by “rejection count * 7 + no-consultation count after consent * 3”. Specifically, when the number of refusals of the person to be processed is one and the number of non-consultation after consent is zero, the negative index of the person to be processed is 7.

  When the process of step S36 is executed, it is determined whether or not the processes of steps S33 to S36 described above have been executed for all subjects extracted in step S31 (step S37). If it is determined that the process has not been executed for all the target persons (NO in step S37), the process returns to step S33 described above and the process is repeated. In this case, the processes in steps S33 to S36 are executed with the target person who has not been processed as the target person. That is, the processing of steps S33 to S36 is executed in a loop for each of all the prevalent persons.

  On the other hand, when it is determined that the processing has been executed for all the target persons (YES in step S37), the display data generation unit 16 stores the target person extracted in step S31 in the target person data storage unit 13. Subject display data for displaying the subject data, the number of rejections acquired in step S34, the number of non-consent visits after consent acquired in step S35, and the negative index calculated in step S36 (step S38). ).

  Thereafter, the processes of steps S39 to S41 corresponding to the processes of steps S27 to S29 shown in FIG. 6 are executed.

  Here, FIG. 15 shows an example of a display screen when the above-described subject display data is displayed. In the display screen 200 shown in FIG. 15, as a subject display data, a subject list showing the subject data associated with each subject, the number of refusals, the number of non-consultation after consent, and the negative index in a list format is displayed. ing. In addition, on the display screen 200, subject data and the like regarding each subject are displayed in descending order of the negative index.

  By checking the list of subjects displayed in this way, the person in charge can easily grasp the subjects who have refused the consultation recommendation in the past and who have agreed to receive the consultation recommendation but have not started the consultation. can do.

  Assuming that the correspondence data (including reaction data and action data) shown in FIG. 16 is input in step S40, the correspondence data storage unit 15 stores the correspondence data for each year as shown in FIG. The

  Here, the case where the consultation recommendation for the second year is implemented has been described, but the same processing is executed even when the consultation recommendation for the third and subsequent years is implemented, for example.

  According to the target person display process after the above-mentioned 2nd year, in addition to the target person data related to the target person who is the subject of the consultation recommendation, the above-mentioned number of refusals, the post-consent unvisited function and the negative index can be presented. Correspondence data indicating the correspondence of the subject to the consultation recommendation can be further accumulated in the correspondence data accumulation unit 15.

  Hereinafter, with reference to FIG. 18, the effect of the consultation recommendation when the information processing apparatus 10 according to the present embodiment is used will be specifically described. Here, for example, in Company Y having about 4,000 employees, as described above, subjects who are at high risk of getting sick (high-risk subjects) are extracted, and consultation recommendations are conducted for those subjects. The case will be described.

  In FIG. 18, for each fiscal year, the number of subjects, the number of rejected persons in the previous year, the number of recommended consenters, the number of rejected recommendations, the consent rate, the number of unexamined persons after the previous year's consent, the number of persons who started the examination, the number of unexamined persons after the consent The success rate of consultation, the recommended success rate, and the number of consent convertors are shown. The number of non-visited patients after consenting to the previous year is the number of target persons who have agreed to receive a medical examination recommendation in the previous fiscal year but have not started the medical examination (that is, those who have not been examined after the recommendation). The number of examinee starters is the number of subjects who have started a consultation among the subjects. The number of non-consulted patients after consenting is the number of subjects who have agreed to receive a medical examination recommendation this year but have not started the medical examination. The consultation success rate is a value calculated by “number of consultation start / number of recommended consenters”. The recommended success rate is a value calculated by “the number of examinees started / the number of subjects”. Note that the number of target persons, the number of rejected persons in the previous year, the number of recommended consenters, the number of recommended rejections, the consent rate, and the number of consent convertors are as described in FIG.

  On the other hand, FIG. 19 shows the number of target persons, the number of rejected persons in the previous year, the number of recommended consenters, the number of rejected recommendations, the consent rate, and the previous year's consent when the company X does not use the information processing apparatus 10 according to the present embodiment. It shows changes in the number of people who have not yet been examined, the number of people who have started the examination, the number of persons who have not yet received the consent, the success rate of the consultation, the recommended success rate, and the number of consent-converters.

  Comparing FIG. 18 and FIG. 19, in Company Y, the decrease in the consent rate for the consultation recommendation after the second year is suppressed, and the decrease in the consultation success rate and the recommendation success rate is also suppressed. This is because the person in charge of the consultation encouraged the patient list in the above list to receive the consultation recommendation in the past, but clearly understands the existence of the subject who did not start the consultation (the person who did not consult after the consent). This is because guidance (measures) conscious of the unexamined person after the consent becomes possible, and as a result, it can be said that it has led to the start of the unexamined person after the previous year's consent.

  Each value shown in FIG. 18 is calculated based on the subject data stored in the subject data storage unit 13 and the corresponding data (reaction data and action data) stored in the corresponding data storage unit 15. Is possible. Therefore, for example, a list as shown in FIG. 18 can be displayed in order to verify the effect of consultation recommendation by the person in charge.

  As described above, in the present embodiment, correspondence data including reaction data and action data is accumulated in the correspondence data accumulation unit 15, and subject data stored in the subject data storage unit 13, the number of rejections (first Number of times) and the number of non-consultation after consent (second number) are displayed. In the present embodiment, unlike the first embodiment described above, the person in charge can grasp the presence of an undiagnosed person after consent according to such a configuration. For this reason, by taking new measures (approaches and measures different from the past) not only for those who are frequently rejected, but also for those who have not yet received consent, the consent rate for consultation recommendation, the rate of successful consultation, and It is possible to suppress a decrease in the recommendation success rate and the like, and as a result, it is possible to suppress a decrease in the effect of the consultation recommendation performed on the subject.

  Further, in the present embodiment, a negative index (first index) that represents a consultation recommendation or a negative polarity related to a consultation is calculated, and the negative index is further displayed, so that the subject data, the number of refusals, and unconfirmed Compared with the case where the number of medical examinations is displayed, the person in charge can intuitively grasp the target person who should follow up in a focused manner.

  Furthermore, in the present embodiment, by displaying the target person data regarding the target person in the order of the negative index, it is possible to display the target person to be followed in a focused manner in a manner that makes it easier to grasp. In other words, in this embodiment, the accumulation of the previous year's refusal and the unconfirmed after the previous year's inevitability that is inevitably caused by the second year's or later consultation recommendation is suppressed to a minimum, and the effective examination recommendation is limited with limited resources. Can be implemented.

  In the present embodiment, in addition to the negative index described above, for example, a risk index (second index) may be calculated for each target person, and the risk index may be displayed. . Here, the risk index is an index representing the degree of risk that a medical condition calculated based on, for example, medical examination data of a subject becomes serious. For example, the risk index is calculated according to the difference between the index value related to the target disease described in the first embodiment and the recommended consultation value. Specifically, if the recommended consultation value is 7.0%, for example, if the value of hemoglobin A1C, which is an index value related to diabetes of the subject, is 8.0%, the risk index is set to 1. When the value of the hemoglobin A1C is 9.0%, the risk index can be set to 2. By displaying such a risk index together, the person in charge can make a consultation recommendation considering the negative index and the risk index comprehensively.

(Third embodiment)
Next, a third embodiment will be described. The functional configuration of the information processing apparatus 10 according to the present embodiment is the same as that of the first and second embodiments described above, and will be described with reference to FIG. 1 as appropriate. Further, the description of the parts common to the second embodiment described above will be omitted, and the parts different from the second embodiment will be mainly described.

  The present embodiment is different from the second embodiment described above in that the subject data related to subjects belonging to a range in which the negative index falls within a predetermined range is displayed so as to be distinguishable from other subject data. In addition, when the consultation recommendation for the second year is carried out, only the correspondence data for the first year is accumulated in the correspondence data storage unit 15, so that a significant negative index may not be calculated between the subjects. For this reason, such a display shall be performed when the consultation recommendation after the 3rd year is implemented (that is, in the target person display process after the 3rd year).

  Hereinafter, with reference to the flowchart of FIG. 14 mentioned above, the process sequence of the subject display process after the 3rd year performed by the information processing apparatus 10 according to the present embodiment will be described. In addition, since the target person display process of the first year and the target person display process of the second year in this embodiment are as described in the second embodiment described above, detailed description thereof is omitted.

  Here, it is assumed that the correspondence data accumulating unit 15 accumulates data indicating past correspondences of the subject with respect to consultation recommendations in the first year and the second year.

  First, the processes of steps S31 to S37 shown in FIG. 14 described above are executed.

  If it is determined in step S37 that the process has not been executed for all subjects (NO in step S37), the process returns to step S33 described above and the process is repeated.

  On the other hand, when it is determined that the processing has been executed for all the target persons (YES in step S37), the display data generation unit 16 stores the target person extracted in step S31 in the target person data storage unit 13. Subject display data for displaying the subject data, the number of rejections acquired in step S34, the number of non-consent visits after consent acquired in step S35, and the negative index calculated in step S36 (step S38). ). In this case, the display data generating unit 16 displays the target person display data for displaying the target person data related to the target person whose negative index falls within a predetermined range as described above so as to be distinguishable from other target person data. Generate.

  Specifically, for example, the display data generation unit 16 has subject data regarding subjects whose negative index is the top 20%, subject data regarding subjects whose negative index is the bottom 50%, and other subjects ( The target person display data is generated so that the target person data related to the target person with the negative index of medium 30% is displayed in a distinguishable manner.

  Thereafter, the processing of steps S39 to S41 shown in FIG. 14 described above is executed.

  Here, FIG. 20 shows an example of a display screen when the above-described subject display data is displayed. On the display screen 300 shown in FIG. 20, as subject display data, a subject list showing the subject data associated with each other, the number of rejections, the number of non-consultation after consent, and the negative index in a list format is displayed.

  In addition, the target person display data (target person list) displayed on the display screen 300 includes the data regarding the target person extracted when the examination recommendation for the third year is implemented, and the past for the examination recommendations for the first year and the second year. 15 is generated based on the data indicating the correspondence between the target person and the target person display data (target person list) shown in FIG. 15 described above, the target person (data) and each value (rejection count, unchecked) The number of times and the negative index etc. are different.

  In the display screen 300 shown in FIG. 20, the subject data, the number of refusals, the number of non-consultations, and the negative index regarding the ten subjects are displayed in the order of the negative index. (Hereinafter referred to as “High Difficulty Target Person”), Target Person Data related to the target person whose negative index is the lower 50% (hereinafter referred to as “Medium Difficulty Target Person”), and other target persons ( Hereinafter, the target person data regarding the low difficulty target person is displayed in a distinguishable manner.

  Specifically, the target person data regarding the target persons B and F, the target person data regarding the target persons H, K, and J, and the target person data regarding the target persons O, P, S, T, and U are different, for example. It shall be displayed in the state where the color was given. According to this, the persons in charge who refer to the target person list displayed on the display screen 300 are the target persons B and F who are the high difficulty target persons related to the examination recommendation, and the target persons H, K and J are the medium difficulty levels. It is a target person, and it can be easily grasped that the target persons O, P, S, T, and U are low difficulty target persons.

  Further, in the lower part of the display screen 300 shown in FIG. 20, for example, information on measures to be taken when the above-mentioned high-difficulty target person, medium-difficulty target person, and low-difficulty target person are to be referred to ( For example, an appropriate human resource arrangement or the like is displayed. Specifically, when a consultation recommendation is implemented, for example, for high-difficulty target persons (subjects B and F), for example, occupational physicians and corporate personnel personnel are recommended and the medium-difficulty target For those who are subject (subjects H, K and J), for example, an industrial physician is recommended, and for those who are subject to low difficulty (subjects O, P, S, T and U), for example, medical health staff A message stating that the person is in charge of recommendation is displayed. According to this, an appropriate person in charge can be assigned to each subject person. It should be noted that other information may be displayed on the display screen 300 as long as it is significant information regarding the implementation of the consultation recommendation.

  According to the above-mentioned target person display processing after the third fiscal year, it is possible to distinguish target data regarding target persons whose negative index is within a predetermined range (for example, the top 20%, the middle 30%, and the bottom 50%). Can be displayed in various ways. In addition, by setting the degree of difficulty related to the recommendation of at least two or more in this way, it is possible to recommend measures to the person in charge according to each degree of difficulty.

  Here, subjects with a negative index of the top 20% are subject to high difficulty, subjects with a negative index of 30% are subject to medium difficulty, and subjects with a negative index of lower 50% are subject to low difficulty. However, for example, subjects with a negative index greater than or equal to the first threshold are subject to high difficulty, and subjects with a negative index less than the first threshold and greater than or equal to the second threshold are subject to medium difficulty. The target person whose negative index is less than the second threshold may be the low difficulty target person. The first and second threshold values can be set freely, but the first threshold value is larger than the second threshold value.

  In addition, the above-described high difficulty level target person, medium difficulty level target person, and low difficulty level target person have been described as being set based on the negative index, but based on the risk index described in the second embodiment described above. It may be set based on both the negative index and the risk index.

  Hereinafter, with reference to FIG. 21, the effect of consultation recommendation when the information processing apparatus 10 according to the present embodiment is used will be specifically described. Here, for example, in the company Z having about 4000 employees, a case where a high-risk person is extracted and a medical examination recommendation is performed on the target person will be described. In addition, FIG. 19 mentioned above is used as a comparative example for explaining the effect of consultation recommendation when the information processing apparatus 10 according to the present embodiment is used.

  Note that the items such as the number of subjects shown for each fiscal year in FIG. 21 are the same as those described in FIG. 11 and FIG.

  In the present embodiment, the target person display process in the first year and the target person display process in the second year are the same as those in the second embodiment described above. Therefore, comparing FIG. 21 and FIG. 19, as with the company Y (that is, FIG. 18) described above, in the company Z, the decrease in the consent rate for the consultation recommendation in the second year is suppressed and the consultation has been successful. The decline in rates and recommended success rates is also suppressed. Furthermore, it can be seen that in Company Z, the consent rate and the success rate after the third year have greatly recovered. This is the result of properly grasping the high difficulty level target person, medium difficulty level target person and low difficulty level target person in the target person list mentioned above after 3rd fiscal year, and taking appropriate measures for each target person You can say that. Further, as shown in FIG. 21, in the company Z, the recommended success rate is greatly improved as compared with FIG.

  As described in the second embodiment described above, a list as shown in FIG. 21 may be displayed in this embodiment as well.

  As described above, in the present embodiment, data related to a subject whose negative index falls within a predetermined range is displayed so as to be distinguishable from other subject data. In the present embodiment, with such a configuration, the person in charge obtains a more intuitive target person (that is, consent to receive a medical examination recommendation) compared to the second embodiment described above. It is possible to easily grasp subjects who are difficult to start or are difficult to start medical examinations).

  In the present embodiment, the recommendation is performed by further displaying information on the measures to be taken when the recommendation index is applied to a subject whose negative index falls within a predetermined range. Therefore, it is possible to improve the consent rate, the consultation success rate, the recommendation success rate, etc. with respect to the consultation recommendation, and to suppress the decrease in the effectiveness of the consultation recommendation.

  In other words, in the present embodiment, the accumulation of the previous year's refusal and the unconfirmed previous year's inevitability that is inevitably caused by the recommendation for the second and subsequent years is minimized, and the efficiency and success rate is limited with limited resources. Can be highly recommended.

  According to at least one embodiment described above, it is possible to provide an information processing device, a display device, a display method, and a program that are useful for suppressing a decrease in the effect of a consultation recommendation performed on a subject. .

  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 embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 10 ... Information processing apparatus, 11 ... Health data storage part, 12 ... Target person extraction part, 13 ... Target person data storage part, 14 ... Input part, 15 ... Corresponding data storage part, 16 ... Display data generation part, 17 ... Output Part

Claims (12)

Storage means for storing subject data regarding the subject of the consultation recommendation;
Input means for inputting correspondence data indicating the correspondence of the subject to the consultation recommendation;
Storage means for storing the input correspondence data;
An information processing apparatus comprising: output means for outputting target person display data for displaying target person data stored in the storage means and corresponding data stored in the storage means. The correspondence data includes response data indicating whether or not they have agreed to receive the consultation recommendation,
The output means sets, as the correspondence data, the first number of times that the subject acquired based on the response data included in the correspondence data stored in the storage means has not agreed to receive the consultation recommendation. The information processing apparatus according to claim 1, wherein target display data for display is output. The correspondence data includes behavior data indicating whether or not a consultation has started,
The output means agrees that the subject obtained based on the response data and behavior data included in the correspondence data stored in the storage means as the correspondence data has received the recommendation, but starts receiving The information processing apparatus according to claim 2, wherein target information display data for further displaying the second number of times of not performing is output.   The information processing apparatus according to claim 1, further comprising display means for displaying the output subject display data.   The information processing apparatus according to claim 1, wherein the output unit outputs the target person display data to an external apparatus capable of displaying the target person display data.   The information processing apparatus according to claim 1, wherein the output unit outputs the target person display data to a printing apparatus capable of printing.   The information processing apparatus according to claim 1, wherein the subject data includes identification information for identifying the subject and a result of a health check on the subject. Further comprising extraction means for extracting the subject based on a result of a health checkup on the subject,
The information processing apparatus according to claim 1, wherein the storage unit stores target person data related to the extracted target person. In an information processing apparatus that is communicably connected to a server device having storage means and storage means for storing subject data relating to a subject who is recommended for consultation,
Input means for inputting correspondence data indicating the correspondence of the subject to the consultation recommendation;
Storage processing means for storing the input correspondence data in the storage means;
An information processing apparatus comprising: output means for outputting target person display data for displaying target person data stored in the storage means and corresponding data stored in the storage means. Acquisition means for acquiring target person display data for displaying target data regarding the target person of the consultation recommendation and correspondence data indicating the past correspondence of the target person to the consultation recommendation;
A display device comprising: display means for displaying the acquired subject display data. A display method executed by an information processing apparatus having storage means and storage means for storing subject data relating to a subject who is recommended for examination,
Inputting correspondence data indicating the correspondence of the subject to the consultation recommendation;
Storing the input correspondence data in the storage means;
Outputting the subject person data stored in the storage means and the subject person display data for displaying the corresponding data stored in the storage means. A program executed by a computer of an information processing apparatus having storage means and storage means for storing subject data relating to a subject who is recommended for examination,
In the computer,
Inputting correspondence data indicating the correspondence of the subject to the consultation recommendation;
Storing the input correspondence data in the storage means;
A program for executing the step of outputting the target person data stored in the storage means and the target person display data for displaying the corresponding data stored in the storage means.

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