JP7081103B2 - Computer program, medical receipt analyzer and telemedicine guidance method - Google Patents

Description

The present invention relates to a computer program, a medical receipt analyzer, and a telemedicine guidance method.

National medical expenses are increasing year by year, which is a heavy burden not only on corporate health insurance associations but also on patients, and there is an urgent need to curb medical expenses. Under such circumstances, a service is provided in which medical receipts (medical remuneration details) are analyzed to calculate medical expenses for union members.

For example, in Patent Document 1, the medical expense decomposition is conventionally performed by reading the injury / illness name code associated with the same classification code as the medical practice code of the medical receipt and associating the medical expenses associated with the medical practice code with the injury / illness name code. A medical expense decomposition analysis device that can be performed more accurately is disclosed.

Japanese Patent No. 4312757

Accurate calculation of medical expenses is important in order to curb medical expenses, but it is necessary to take concrete measures. For example, if a lifestyle-related disease, which is a non-infectious disease, becomes severe, medical expenses will be incurred for a long period of time, so it is desired to avoid the aggravation of lifestyle-related disease patients. In addition, in the case of patients with mild lifestyle-related diseases, it is possible to perform not only outpatient treatment but also telemedicine.

The present invention has been made in view of such circumstances, and an object of the present invention is to provide a computer program, a medical receipt analyzer, and a telemedicine guidance method capable of extracting patients capable of telemedicine.

The computer program according to the embodiment of the present invention is a computer program for causing a computer to analyze a receipt, and is a process of acquiring the receipt data for each of a plurality of patients over a first period and the acquisition. Based on the name of injury or illness or medical practice related to any patient included in the receipt data, the process of determining the patient's classification every second period shorter than the first period, and the patient's classification for each second period. Is to execute a process of determining whether or not the patient is a candidate for remote medical care, depending on whether or not the predetermined condition is satisfied over the first period.

The receipt analyzer according to the embodiment of the present invention has an acquisition unit that acquires receipt data over a first period of each of a plurality of patients, and an injury / illness name relating to any patient included in the receipt data acquired by the acquisition unit. Alternatively, the classification determination unit that determines the classification of the patient for each second period shorter than the first period based on the medical practice, and the classification for each second period of the patient are predetermined conditions over the first period. It is provided with a candidate patient determination unit for determining whether or not the patient is a candidate patient for remote medical treatment, depending on whether or not the above is satisfied.

The terminal device according to the embodiment of the present invention is a terminal device provided with a display screen and used by a telemedicine candidate patient, and receives a reception unit for receiving a guidance for a telemedicine service and a guidance received by the reception unit. A display control unit for displaying on the display screen is provided.

The remote medical care guidance method according to the embodiment of the present invention is a remote medical care guidance method using a medical receipt analyzer, and the medical receipt analyzer acquires and acquires medical receipt data for each of a plurality of patients over a first period. Based on the name of injury or illness or medical practice related to any patient included in the medical receipt data, the classification of the patient is determined every second period shorter than the first period, and the classification of the patient for each second period is determined. It is determined whether or not the patient is a candidate for remote medical treatment according to whether or not the predetermined conditions are satisfied over the first period, and the determined remote medical care candidate patient is informed of the remote medical care service. Is output.

According to the present invention, it is possible to extract patients who can perform telemedicine.

It is a block diagram which shows an example of the structure of the receipt analysis system of this embodiment. It is explanatory drawing which shows an example of the main record structure of the medical receipt. It is explanatory drawing which shows an example of the subject extraction condition. It is explanatory drawing which shows the 1st example of the report generated by the receipt analyzer of this embodiment. It is explanatory drawing which shows the 2nd example of the report generated by the receipt analyzer of this embodiment. It is explanatory drawing which shows an example of the patient selection file of this embodiment. It is a schematic diagram which shows an example of the classification determination on a monthly basis by the classification determination unit of this embodiment. It is a schematic diagram which shows the 1st example of the candidate patient determination by the candidate patient determination unit of this embodiment. It is a schematic diagram which shows the 2nd example of the candidate patient determination by the candidate patient determination unit of this embodiment. It is explanatory drawing which shows an example of the list of the telemedicine candidate patients generated by the receipt analyzer of this embodiment. It is a schematic diagram which shows the 1st example of the guidance document of the telemedicine service generated by the receipt analyzer of this embodiment. It is a schematic diagram which shows the 2nd example of the guidance document of the telemedicine service generated by the receipt analyzer of this embodiment. It is a schematic diagram which shows the 3rd example of the guidance document of the telemedicine service generated by the receipt analyzer of this embodiment. It is a schematic diagram which shows the 4th example of the guidance document of the telemedicine service generated by the receipt analyzer of this embodiment. It is a flowchart which shows an example of the receipt analysis processing procedure by the receipt analysis apparatus of this embodiment. It is a flowchart which shows an example of the extraction processing procedure of the telemedicine candidate patient by the receipt analyzer of this embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing an example of the configuration of the receipt analysis system of the present embodiment. The receipt analysis system of the present embodiment includes a receipt analysis device 50, a terminal device 10 used by a patient, and the like. The receipt analyzer 50 and the terminal device 10 are connected to each other via the communication network 1. Although only two terminal devices 10 are shown in FIG. 1 for convenience, there are as many terminal devices 10 as there are patients.

The terminal device 10 is composed of, for example, a smartphone, a tablet, a notebook personal computer, or the like. The terminal device 10 includes a control unit 11, a communication unit 12, a display control unit 13, a display screen 14, an operation unit 15, and the like that control the entire device.

The receipt analysis device 50 includes a control unit 51, an acquisition unit 52, a classification determination unit 53, a candidate patient determination unit 54, a storage unit 55, a communication unit 56, a guidance information generation unit 57, a calculation unit 58, and a target person who control the entire device. It is provided with an extraction unit 59 and the like.

The receipt analyzer 50 provides, for example, a function for promoting a telemedicine service specialized for lifestyle-related diseases. Further, the receipt analyzer 50 provides a function of extracting candidate patients suitable for telemedicine by analyzing the receipts of the patients suffering from lifestyle-related diseases, for example, and for the extracted candidate patients. Provides a function to output information on telemedicine services.

First, the receipt analysis function for promoting the telemedicine service will be described.

The acquisition unit 52 acquires the receipt data of each of the plurality of patients over the required period. The required period can be the period required to calculate the statistical information, and may be appropriately determined. The receipt data is the data described in the electronic receipt 5 (medical fee schedule). The electronic receipt 5 is a receipt electronically recorded in CSV format text, and includes, for example, a medical receipt, a dispensing receipt, a dental receipt, an inpatient receipt, and the like. Here, the acquisition unit 52 acquires, for example, the receipt data of the medical receipt and the dispensing receipt for 6 months.

FIG. 2 is an explanatory diagram showing an example of a main record structure of a medical receipt. As shown in FIG. 2, the record structure is composed of columns such as a receipt type and main record contents. The record types include, for example, receipt management, medical institution information, receipt common, insurer, injury / illness name, medical practice, medicine, and the like. For the injury / illness name, the injury / illness name code is defined for each injury / illness name, and for the medical practice, the medical practice code is defined for each medical practice.

The subject extraction unit 59 extracts patients with a predetermined disease based on the name of injury or illness or medical practice related to any patient included in the acquired receipt data. Specifically, the patient's name, insurer number, injury / illness name (injury / illness name code), medical practice (medical practice code), medical fee, dispensing fee, etc. are read from the receipt data, and the medical receipt and the dispensing receipt are identified. .. Then, each record condition for extracting the target patient (target person) is set, and the target person is narrowed down. The patient with a predetermined disease can be, for example, a patient with a lifestyle-related disease.

FIG. 3 is an explanatory diagram showing an example of the subject extraction conditions. It is an item for extracting a target patient (for example, a patient with a lifestyle-related disease) from a plurality of patients, and examples thereof include an injury / illness name, a medical practice, and an age. In addition, as the conditions to be set, for example, the name of injury or illness may be diabetes, hypertension, dyslipidemia, etc., the medical practice may be a blood test, urine test, etc., and the age may be 40s or older. can. The target person extraction conditions are not limited to those illustrated in FIG. 3, and the settings can be changed as appropriate.

The target person extraction unit 59 analyzes the attributes of the narrowed down target person. For example, medical expenses, dispensing expenses, classification according to the area where the patient is visiting can be performed.

The calculation unit 58 calculates the statistical information of the subject. The statistical information may not include personal information such as the name of the target person. Specifically, the calculation unit 58 can calculate the ratio of the number of patients with a predetermined disease to the number of patients with a predetermined disease (subject) or the number of a plurality of patients. In addition, the calculation unit 58 can calculate the reduction amount of medical expenses based on the number of patients with a predetermined disease.

The target person extraction unit 59 can create a report based on the statistical information calculated by the calculation unit 58. The report may be output on a paper medium or displayed on a display screen.

FIG. 4 is an explanatory diagram showing a first example of a report generated by the receipt analyzer 50 of the present embodiment. In the example of FIG. 4, the absolute value and the relative ratio of the number of patients (other than lifestyle-related diseases) and the number of patients with lifestyle-related diseases are shown. The numerical value and percentage (%) shown in FIG. 4 are examples and are not limited thereto. In this way, statistical information of patients with lifestyle-related diseases can be obtained, and the number of patients with lifestyle-related diseases can be grasped.

FIG. 5 is an explanatory diagram showing a second example of the report generated by the receipt analyzer 50 of the present embodiment. In the example of FIG. 5, when the number of patients narrowed down as patients (subjects) of a predetermined disease is 1500, the current average medical care per person when the participation rate of the subjects in the telemedicine service is changed. The cost (for example, in the case of going to the hospital) and the expected reduction in medical costs when switching to telemedicine are shown in comparison. In this way, by comparing the medical expenses of lifestyle-related diseases between the case of going to the hospital and the case of telemedicine, it is possible to grasp the effect of reducing medical expenses by introducing the telemedicine service.

Next, the function of extracting telemedicine candidate patients suitable for telemedicine will be described. First, the monthly classification determination will be described.

The acquisition unit 52 acquires the receipt data for each of the plurality of patients over a plurality of months (first period). Here, the acquisition unit 52 acquires the receipt data of the medical receipt, the dispensing receipt, and the dental receipt. The plurality of months as the first period can be, for example, 6 months, 12 months, and the like. Further, the first period may be a plurality of days such as 50 days, 100 days, and 150 days. In the present embodiment, it will be described as 6 months.

The classification determination unit 53 determines the patient classification in units of the second period (every second period) based on the injury / illness name or medical practice related to any patient included in the acquired medical receipt data. Hereinafter, the second period will be described as January, but the second period can be appropriately determined, for example, one week, two weeks, or ten days. The classification is, for example, for stratifying whether patients are targeted or excluded from telemedicine candidates. The target of the telemedicine candidate is, for example, a patient with a relatively mild disease suitable for telemedicine, and it is highly likely that the symptoms will be improved by diet therapy or exercise therapy, and it is highly likely that the telemedicine service will be used. .. On the other hand, the non-target patients are patients with relatively severe illnesses that are not suitable for telemedicine, and they have already received the guidance of a doctor, or have another illness and require the guidance of a doctor. It can be said that the symptom is unlikely to improve and the telemedicine service is unlikely to be used. Information for patient selection is used to determine whether a patient is a target patient or a non-target patient.

When the classification determination unit 53 determines the classification of any patient, the arbitrary patient can be extracted in advance. For example, a patient whose injury / illness name is "diabetes", a patient who has undergone a blood test under the item "HbA1c" in medical practice, a patient who is in his 40s or older, and a patient who goes to the dentistry by the subject extraction unit 59. , Patients taking antihypertensive agents and the like may be extracted, and classification determination may be made for the extracted patients.

FIG. 6 is an explanatory diagram showing an example of a patient selection file of the present embodiment. The patient selection file contains information that serves as a reference for selecting whether a patient is a target patient or a non-target patient. As shown in FIG. 6, the patient selection file is composed of item names, codes, determinations, and names. In the judgment column, "corresponding" means that the patient is the target patient, and "exclusion" means that the patient is excluded from the target patients (excluded from the telemedicine candidates).

As shown in FIG. 6, type 2 diabetes and hypertension are relatively mild diseases, so the judgment is "corresponding", and they are the names of injuries and diseases that are the targets of telemedicine candidates. On the other hand, the names of injuries and illnesses such as gastric cancer and thyroid cancer are serious diseases, and the judgment is "excluded". In addition, the medical practice is a lifestyle-related disease management fee, and since the guidance for lifestyle-related diseases has already been received, the judgment is "excluded". The contents of each column of code, judgment, and name are not limited to the example of FIG. 6, and in reality, the code, judgment, name, and the like can include more contents.

The classification determination unit 53 determines that if the injury / illness name or medical practice related to an arbitrary patient is a predetermined injury / illness name or medical practice, the monthly classification of the patient is excluded from the telemedicine candidate patients ( The judgment is "exclusion").

As shown in FIG. 6, the predetermined injury / illness name or medical practice is, for example, one that requires the guidance of a doctor such as cancer, one that is not suitable for telemedicine such as a serious disease, an infectious disease or a mental illness, and already has a life. Including those who have received habitual guidance. This makes it possible to appropriately extract patients who can undergo telemedicine.

FIG. 7 is a schematic diagram showing an example of classification determination on a monthly basis by the classification determination unit 53 of the present embodiment. In the receipt data of patient A, there is a record of injury / illness name code 1 and injury / illness name code 2 in April, and it can be seen that, for example, he / she visits two medical institutions. Also, in May, since there is no record, it can be seen that he did not visit a medical institution. Also, in June, there are records of injury / illness name code 3 and medical practice code 4. The codes 1 to 4 are numerical values for convenience, and the actual code is a multi-digit numerical value. Further, regarding the codes 1 to 4, in the patient selection file, the judgment of the codes 2 and 4 is "corresponding", the judgment of the code 3 is "exclusion", and the judgment of the code 1 is blank. Indicates for convenience that code 1 does not exist in the patient selection file.

The classification determination unit 53 performs the following processing on a monthly basis. (1) Set the "patient judgment" variable to the initial value "none". (2) Read the injury / illness name code, and if there is no code in the patient selection file, do not change the "patient judgment" variable. (3) Read the injury / illness name code, and if the code corresponds to "corresponding" in the patient selection file and the "patient judgment" variable is not "excluded", change the "patient judgment" variable to "corresponding". .. (4) Read the injury / illness name code, and if the code corresponds to "corresponding" in the patient selection file and the "patient judgment" variable is "excluded", leave the "patient judgment" variable as "excluded". do. (5) Read the injury / illness name code, and if it is a code that corresponds to "exclusion" in the patient selection file, change the "patient judgment" variable to "exclusion". The same treatment is performed for all the names of injuries and illnesses. When the processing of all the injury / illness names is completed, the medical practice code is read and the same processing as in the case of the injury / illness name code is performed.

For example, as shown in FIG. 7, when the classification determination for April is performed, the "patient determination" variable is first set to "none". Since the injury / illness name code 1 is read and the injury / illness name code 1 is not in the patient selection file (neither "corresponding" nor "excluded"), the "patient determination" variable remains "none". Next, read the injury / illness name code 2, and since the injury / illness name code 2 is the code corresponding to "corresponding" and the "patient judgment" variable is "none" (because it is not "excluded"), the "patient judgment" variable is selected. Change to "Applicable". Since there is no other injury / illness name code or medical practice code for April, the classification for April is "corresponding".

When making a classification judgment for the May classification, first set the "patient judgment" variable to "none". Since there is no injury / illness name code or medical practice code for April, the classification for May is "None".

When making a classification judgment for the June classification, first set the "patient judgment" variable to "none". The injury / illness name code 3 is read, and since the injury / illness name code 3 is a code corresponding to "exclusion", the "patient determination" variable is changed to "exclusion". The medical practice code 4 is read and the code corresponds to "corresponding" in the patient selection file, but since the "patient judgment" variable is "excluded", the "patient judgment" variable is left as "excluded". Since there is no other injury / illness name code or medical practice code for June, the category for June is "excluded".

As described above, the classification determination unit 53 includes a plurality of injury / illness names or medical practices related to any patient in the receipt data for one month, and the injury / illness name or one of the plurality of injury / illness names or medical practices. When the medical practice is a predetermined injury / illness name or medical practice, it is determined that the monthly classification of the patient is an exclusion classification (“exclusion”).

Depending on the patient, he / she may visit multiple medical institutions in a month, one medical institution receives medical treatment for mild lifestyle-related diseases, and the other medical institution receives relatively serious illnesses. May be receiving medical treatment. Omission of patients who can perform telemedicine by determining whether the name of an injury or illness or a medical practice of one of a plurality of names of injury or illness or medical practice per month is a predetermined name of injury or illness or medical practice. It is possible to prevent erroneous extraction of patients who are not suitable for telemedicine.

Once the results of the monthly classification determination of each patient are obtained, the next step is to perform a determination over a plurality of months based on the monthly classification determination results and extract telemedicine candidate patients (final candidates). Hereinafter, a method for extracting telemedicine candidate patients will be described.

The candidate patient determination unit 54 determines whether the patient is a telemedicine candidate patient depending on whether the monthly classification of the patient (such as "applicable" or "excluded") satisfies the predetermined condition for a plurality of months. Judge whether or not.

Patients do not always go to a medical institution such as a hospital every month. For example, patients with lifestyle-related diseases often visit a medical institution once every two to three months. Therefore, by determining the monthly classification of patients over a plurality of months, it is possible to appropriately extract patients who can perform telemedicine.

Specifically, the candidate patient determination unit 54 performs the following processing. (1) Read the result of the classification judgment of the first month of a certain patient and set it in the "comprehensive judgment" variable. For example, if the classification judgment is "applicable", the "comprehensive judgment" variable is set to "corresponding", and if the classification judgment is "none", the "comprehensive judgment" variable is set to "other". If the classification determination is "exclusion", the "comprehensive determination" variable is set to "exclusion" and the next patient is processed. (2) Read the result of the classification judgment of the next month, and if the classification judgment is "applicable" and the "comprehensive judgment" variable up to last month is "corresponding" or "other", the "comprehensive judgment" variable is selected. Set to "Applicable". If the classification judgment is "None" and the "Comprehensive judgment" variable up to last month is "Applicable", the "Comprehensive judgment" variable is set to "Applicable". If the classification judgment is "corresponding" and the "comprehensive judgment" variable up to last month is "other", the "comprehensive judgment" variable is set to "other". If the classification judgment is "exclusion", the "comprehensive judgment" variable is set to "exclusion" and the next patient is processed. Do the same for the last month. When the treatment of one patient is completed, the same treatment is performed for the next patient, and the treatment is performed for all patients.

FIG. 8 is a schematic diagram showing a first example of candidate patient determination by the candidate patient determination unit 54 of the present embodiment. As shown in FIG. 8, it is assumed that the classification determination result from April to September is obtained for patient B. That is, it is assumed that the classification determination of patient B is "applicable", "none", "applicable", "none", "applicable", and "applicable" in order from April to September.

As shown in FIG. 8, since the classification judgment in April is "corresponding", the comprehensive judgment up to April is "corresponding". Next, since the classification judgment in May is "None" and the comprehensive judgment up to April is "Applicable", the comprehensive judgment up to May is "Applicable". Next, since the classification judgment in June is "corresponding", the comprehensive judgment up to June is "corresponding". Next, since the classification judgment in July is "None" and the comprehensive judgment up to June is "Applicable", the comprehensive judgment up to July is "Applicable". Next, since the classification judgment in August is "corresponding", the comprehensive judgment up to August is "corresponding". Finally, since the classification judgment in September is "corresponding", the final comprehensive judgment is "corresponding".

FIG. 9 is a schematic diagram showing a second example of candidate patient determination by the candidate patient determination unit 54 of the present embodiment. As shown in FIG. 9, it is assumed that the classification determination result from April to September is obtained for patient C. That is, it is assumed that the classification determination of patient C is "applicable", "applicable", "excluded", "none", "applicable", and "applicable" in order from April to September.

As shown in FIG. 9, since the classification judgment in April is "corresponding", the comprehensive judgment up to April is "corresponding". Next, since the classification judgment in May is "corresponding", the comprehensive judgment up to May is "corresponding". Next, since the classification judgment in June is "exclusion", the comprehensive judgment up to June is "exclusion", and the processing is completed for patient C, so that the final comprehensive judgment is "exclusion".

As described above, the candidate patient determination unit 54 determines that the patient is not a telemedicine candidate patient when the monthly category of the patient is an exclusion category in one of a plurality of months.

Patients do not always go to a medical institution such as a hospital every month. In addition, they may visit different medical institutions for multiple months. Therefore, by excluding patients who have undergone a disease that is not suitable for telemedicine even in one of multiple months from the candidates for telemedicine, it is possible to prevent erroneous extraction of patients who are not suitable for telemedicine. be able to.

Further, the candidate patient determination unit 54 determines that the patient is a telemedicine candidate patient when the exclusion category does not exist for a plurality of months. This makes it possible to appropriately extract patients who can undergo telemedicine.

Next, the function of outputting the guidance of the telemedicine service to the candidate patients suitable for the telemedicine will be described.

The guidance information generation unit 57 generates a list of telemedicine candidate patients.

FIG. 10 is an explanatory diagram showing an example of a list of telemedicine candidate patients generated by the receipt analyzer 50 of the present embodiment. As shown in FIG. 10, the list of remote medical treatment candidate patients based on the receipt analysis includes, for example, an insured person's certificate symbol, a non-insured person's certificate number, a name, a date of birth, a gender, an injury / illness name, a medical fee fee, and a dispensing fee. Information such as can be included.

The guidance information generation unit 57 generates data for notification to the telemedicine candidate patient. As shown in FIG. 10, data for notification to telemedicine candidate patients is generated by performing name identification with a list of telemedicine candidate patients and an employee list containing contact information such as an address or an e-mail address. Can be done. The notification data can include, for example, information such as an insured person's card symbol, a non-insured person's card number, a name, an email address, an address, a gender, an injury / illness name, a medical fee, and a dispensing fee. This makes it possible to ascertain the number of patients suitable for telemedicine. In addition, it is possible to notify a patient who is suitable for telemedicine about the telemedicine service.

In addition, the guidance information generation unit 57 can generate guidance data for the telemedicine service for the telemedicine candidate patient. The guidance data is, for example, data for generating a guidance document for a telemedicine service. When printing the guidance document, print data can be generated by a separate printing program based on the guidance data generated by the guidance information generation unit 57, and can be printed on a paper medium. In this case, the guidance information generation unit 57 does not have to have a function of generating print data. Further, when the guidance document is displayed on the display screen, the display data can be generated by a separate display program based on the guidance data generated by the guidance information generation unit 57 and displayed on the display screen. In this case, the guidance information generation unit 57 does not have to have a function of generating display data.

FIG. 11 is a schematic diagram showing a first example of a guidance document for a telemedicine service generated by the receipt analyzer 50 of the present embodiment. The guidance document may be recorded on a paper medium or displayed on a display screen. In the case of paper media, the guide document is printed on both sides and can be folded along the dotted line in the center. The guide document may be enclosed in a sealed letter or may be a crimped postcard. As shown in FIG. 11, it can be composed of two pages on the front surface and two pages on the back surface (inside), for a total of four pages. It is desirable to write personal information on the back side. The guidance document can also be sent by e-mail. In that case, each page shown in FIG. 11 is displayed on the display screen of the destination device.

On the first page of the surface, the address, the guide text, etc. are described. At the address, the address and name will be written in a position that can be seen from the transparent window frame of the envelope. In the place of the guide text, for example, words such as "a guide to a convenient telemedicine service that can be received by a smartphone for people with lifestyle-related diseases" and "now in a point gift campaign" can be described.

The second page on the front page describes the terms of use for applying for telemedicine services, the application form by fax or mail, the URL of the homepage when applying via the Internet, or the QR code (registered trademark).

On the first page on the back side, explanations of telemedicine, explanations of life improvement support, etc. are described. In the explanation section of telemedicine, for example, explanations about insurance coverage, first-time face-to-face medical treatment with a doctor, use of a smartphone with an application downloaded, complete reservation system, credit card payment possible, blood test by self-collection, etc. are described. In addition, in the explanation part of life improvement support, the doctor in charge, the public health nurse supervised, the nutrition manager is in charge, the program introduction such as remote support once a week, exercise support and meal support is described by chat, and the notification destination The recommended program for the patient's medical condition is clearly described with an underline.

On the second page on the back side, the current medical expenses, medical expenses after application, etc. are described. In the section on current medical expenses, medical expenses analyzed by medical receipts, dispensing expenses, out-of-pocket expenses, etc. are described. In addition, in the section of medical expenses after application, the estimated medical expenses self-pay amount when switching to telemedicine, the expenses when self-collecting blood, etc. are described.

With the above configuration, the guidance can be notified via a sealed letter or e-mail. The personal information may be encrypted using the insured person's card as a password, or may be disclosed on a homepage that requires personal ID and password authentication.

FIG. 12 is a schematic diagram showing a second example of a guidance document for a telemedicine service generated by the receipt analyzer 50 of the present embodiment. The guide document is divided into an upper column, a middle column, and a lower column, and the description points are arranged.

In the upper column, common guidance texts for telemedicine candidate patients are described. For example, words such as "<Telemedicine information>", "You can see a doctor by video call", and "It is convenient because you can make a reservation in advance" are described.

In the middle column, individual guidance texts for each telemedicine candidate patient are described. For example, words such as "Your injury and illness name is diabetes" and "You may be able to participate in telemedicine (eventually, a doctor's face-to-face judgment is required)" are described.

Individual menus are listed in the lower column. For example, "This is a lifestyle improvement menu recommended for you", "Doctors and public health nurses support", "Exercise program-walking course", "Exercise program-exercise course", "Diet improvement program-calorie restriction", etc. The wording of is described, and it is shown that the double circle is a required menu and the circle is a recommended menu. In addition, a QR code (registered trademark) for accessing the homepage is described.

FIG. 13 is a schematic diagram showing a third example of a guidance document for a telemedicine service generated by the receipt analyzer 50 of the present embodiment. The structure of the guidance document is the same as that of the example of FIG. In Fig. 13, the individual guidance texts in the middle column include words such as "Your lipid abnormality" and "You may be able to participate in telemedicine (finally, a doctor's face-to-face judgment is required)". Is described. In addition, the individual menus in the lower column state that the dietary improvement program is an essential menu and the exercise program is a recommended menu.

As described above, the guidance information generation unit 57 can generate an individual guidance text (guidance information) or an individual life improvement menu (life improvement measure information) for the telemedicine candidate patient, which is included in the guidance data. This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

FIG. 14 is a schematic diagram showing a fourth example of a guidance document for a telemedicine service generated by the receipt analyzer 50 of the present embodiment. FIG. 14 is an example of an individual guidance sentence for a telemedicine candidate patient, for example, "Your medical expenses for XX months are XX yen, of which dispensing expenses are XX yen", "For telemedicine services. Words such as "It will be a circle" and "Details will be decided after consultation with a doctor" are described.

As described above, the guidance information generation unit 57 generates medical expense information before and after the use of telemedicine regarding the telemedicine candidate patient included in the guidance data. For example, medical expenses before using telemedicine may include medical expenses for a specific month, monthly average medical expenses, dispensing expenses, and the like. In addition, the cost of the telemedicine service can be included as the medical cost information after using the telemedicine. This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

The communication unit 56 has a communication function with the terminal device 10 via the communication network 1. The communication unit 56 can output (transmit) the guidance data generated by the guidance information generation unit 57 to the terminal device 10.

The storage unit 55 includes a patient selection file, an employee list, receipt data acquired by the acquisition unit 52, guidance data generated by the guidance information generation unit 57, and information obtained by each processing in the receipt analyzer 50 (for example, remote). Store a list of medical treatment candidate patients, notification data, etc.).

Next, the terminal device 10 will be described.

The communication unit 12 has a communication function with the receipt analyzer 50 via the communication network 1. The communication unit 12 can receive (acquire) the guidance data of the telemedicine service from the receipt analysis device 50.

The display control unit 13 displays the guidance document on the display screen 14 based on the guidance data received by the communication unit 12. For example, the guidance documents illustrated in FIGS. 11, 12, 13, and 14 can be displayed.

More specifically, the display control unit 13 can display an individual guidance text (guidance information) or an individual life improvement menu (life improvement measure information) for the telemedicine candidate patient included in the guidance data. This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

In addition, the display control unit 13 generates medical expense information before and after the use of telemedicine regarding the candidate patient for telemedicine, which is included in the guidance data. For example, medical expenses before using telemedicine may include medical expenses for a specific month, monthly average medical expenses, dispensing expenses, and the like. In addition, the cost of the telemedicine service can be included as the medical cost information after using the telemedicine. This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

The operation unit 15 is composed of a touch panel or the like, and can input characters and perform operations on icons, buttons, menu bars, windows, cursors, and the like on the display screen 14.

FIG. 15 is a flowchart showing an example of a receipt analysis processing procedure by the receipt analysis device 50 of the present embodiment. The receipt analyzer 50 reads the receipts (for example, medical receipts and dispensing receipts) (S11), and acquires the receipt data for each patient (S12).

The receipt analyzer 50 sets the conditions for a person subject to lifestyle-related diseases (S13). The setting of the conditions for persons subject to lifestyle-related diseases is, for example, the setting of the conditions as shown in FIG. The receipt analyzer 50 extracts the subject (S14) and analyzes the attributes of the extracted subject (for example, medical expenses, dispensing expenses, classification according to the area where the patient is visiting, etc.) (S15).

The receipt analyzer 50 generates statistical information (S16) and ends the process. The statistical information is, for example, the information shown in FIGS. 4 and 5.

FIG. 16 is a flowchart showing an example of a procedure for extracting a telemedicine candidate patient by the receipt analyzer 50 of the present embodiment. The receipt analyzer 50 reads the receipts (for example, medical receipts, dispensing receipts, and dental receipts) (S21), and acquires receipt data for each patient (S22).

The medical receipt analyzer 50 reads the patient selection file (S23) and performs monthly classification determination for each patient (S24). The medical receipt analyzer 50 extracts telemedicine candidate patients (S25) and generates a list of telemedicine candidate patients based on the classification determination over a plurality of months (S26).

The receipt analysis device 50 generates guidance data for the telemedicine service for the telemedicine candidate patient (S27), outputs (transmits) the generated guidance data to the terminal device 10 (S28), and ends the process.

The receipt analyzer 50 of the present embodiment can also be realized by using a computer including a CPU (processor), RAM, and the like. That is, as shown in FIGS. 15 and 16, a computer program defining the procedure of each process is loaded into a RAM provided in the computer, and the computer program is executed by the CPU (processor) to perform receipt analysis on the computer. The device 50 can be realized.

As described above, according to the present embodiment, it is possible to grasp statistical information (for example, the number of patients) of mild patients suitable for a telemedicine service specialized for lifestyle-related diseases, and medical expenses by telemedicine. It is possible to grasp the reduction effect of. In addition, it is possible to appropriately extract patients with relatively mild lifestyle-related diseases (patients who are candidates for telemedicine) from among the patients who are undergoing outpatient treatment. In addition, candidates for telemedicine can be encouraged to participate in lifestyle-related improvement programs such as dietary guidance or exercise guidance, and the aggravation of lifestyle-related diseases can be prevented.

In the above-described embodiment, the number of patients who are candidates for remote medical care, patients who have already received lifestyle guidance, and severely ill patients who require the guidance of a doctor such as cancer should be grasped, and the receipt of each patient should be analyzed. As statistical information, the medical expenses of each candidate for remote medical care, patients who have already received lifestyle guidance, patients who need the guidance of a doctor such as cancer, or severely ill patients (for example, patients who are receiving insulin injection) are used as statistical information. It may be calculated.

The computer program according to the present embodiment is a computer program for causing a computer to analyze a receipt, and is a process of acquiring the receipt data for each of a plurality of patients over the first period and the acquired receipt data. Based on the name of injury or illness or medical practice related to any patient included in the above, the process of determining the classification of the patient for each second period shorter than the first period and the classification of the patient for each second period are described above. A process of determining whether or not the patient is a candidate for remote medical care is executed depending on whether or not the predetermined condition is satisfied over the first period.

The receipt analyzer according to the present embodiment has an acquisition unit for acquiring receipt data for each of a plurality of patients over the first period, and an injury / illness name or medical treatment for any patient included in the receipt data acquired by the acquisition unit. Based on the act, the classification determination unit that determines the classification of the patient for each second period shorter than the first period, and the classification for each second period of the patient satisfy the predetermined conditions over the first period. It is provided with a candidate patient determination unit for determining whether or not the patient is a candidate patient for remote medical treatment, depending on whether or not the patient is to be treated.

The remote medical treatment guidance method according to the present embodiment is a remote medical treatment guidance method using a medical receipt analyzer, and the medical receipt analyzer acquires the medical receipt data for each of a plurality of patients over the first period, and the acquired medical receipt. Based on the name of injury or illness or medical practice related to any patient included in the data, the classification of the patient is determined for each second period shorter than the first period, and the classification of the patient for each second period is the first. It is determined whether or not the patient is a candidate for remote medical treatment according to whether or not the predetermined conditions are satisfied over one period, and the guidance of the remote medical treatment service is output to the determined remote medical treatment candidate patient. do.

The computer program (acquisition unit) acquires the receipt data for each of the plurality of patients over the first period. The first period may be, for example, a plurality of months such as 6 months and 12 months, or a plurality of days such as 50 days, 100 days and 150 days. The medical receipt data is the data described in the medical receipt (medical fee schedule), and includes the medical treatment date, the patient's name, the injury / illness name (injury / illness name code), the medical treatment practice (medical treatment practice code), and the like.

The computer program (classification determination unit) determines the patient classification every second period, which is shorter than the first period, based on the injury / illness name or medical practice related to any patient included in the acquired receipt data. The classification is, for example, for stratifying whether patients are targeted or excluded from telemedicine candidates. The second period can be appropriately determined, for example, monthly, one week, two weeks, ten days, and the like. The target of the telemedicine candidate can be, for example, a patient with a relatively mild disease suitable for telemedicine, and the non-target can be a patient with a relatively severe disease unsuitable for telemedicine.

The computer program (candidate patient determination unit) determines whether or not the patient is a telemedicine candidate patient, depending on whether or not the classification of each second period of the patient satisfies the predetermined conditions over the first period. do.

Patients do not always go to a medical institution such as a hospital every second period. For example, patients with lifestyle-related diseases often visit a medical institution once every two to three months. Therefore, by determining the classification of patients for each second period over the first period, it is possible to appropriately extract patients who can perform telemedicine.

In the computer program according to the present embodiment, when the injury / illness name or medical practice related to the arbitrary patient is a predetermined injury / illness name or medical practice, the computer program divides the patient into the categories for each second period as a telemedicine candidate. The process of determining that the category is excluded from the patient is executed.

If the injury / illness name or medical practice pertaining to any patient is a predetermined injury / illness name or medical practice, the computer program determines that the category for each second period of the patient is an exclusion category that excludes the patient from the telemedicine candidate patients.

Prescribed injury / illness names or medical practices are, for example, those that require the guidance of a doctor such as cancer, those that are not suitable for telemedicine such as serious diseases, infectious diseases or mental illnesses, those that have already received lifestyle-related guidance, etc. including. This makes it possible to appropriately extract patients who can undergo telemedicine.

In the computer program according to the present embodiment, the computer includes a plurality of injury / illness names or medical practices related to the arbitrary patient in the receipt data for the second period, and the computer program includes the plurality of injury / illness names or medical practices. When one injury / illness name or medical practice is a predetermined injury / illness name or medical practice, a process of determining that the category of the patient for each second period is the exclusion category is executed.

In the computer program, the receipt data for the second period includes a plurality of injury / illness names or medical treatments relating to any patient, and the injury / illness name or medical treatment line of one of the multiple injury / illness names or medical treatments is a predetermined injury / illness name. Or, in the case of medical practice, it is determined that the category for each second period of the patient is an exclusion category.

Some patients may visit multiple medical institutions during the second period, one medical institution for mild lifestyle-related illnesses, and the other medical institutions for relatively serious illnesses. May be receiving medical treatment. By determining whether one of the multiple injury / illness names or medical treatments per second period is the prescribed injury / illness name or medical treatment, omission of extraction of patients capable of telemedicine It is possible to prevent erroneous extraction of patients who are not suitable for telemedicine.

In the computer program according to the present embodiment, when the computer is in the second period of one of the first periods and the category of the patient for each second period is the exclusion category, the patient is a candidate for telemedicine. The process of determining that the patient is not a patient is executed.

The computer program determines that the patient is not a telemedicine candidate patient if the patient's second period category is an exclusion category in the second period of one of the first periods.

Patients do not always go to a medical institution such as a hospital every second period. In addition, they may visit different medical institutions during the first period. Therefore, by excluding patients who have undergone a disease that is not suitable for telemedicine even in the second period of one of the first periods from the candidates for telemedicine, patients who are not suitable for telemedicine are erroneously extracted. Can be prevented.

The computer program according to the present embodiment causes the computer to execute a process of determining that the patient is a telemedicine candidate patient when the exclusion category does not exist for the first period.

The computer program determines that the patient is a telemedicine candidate patient if there is no exclusion category for the first period. This makes it possible to appropriately extract patients who can undergo telemedicine.

The computer program according to the present embodiment causes a computer to execute a process of generating a list of the telemedicine candidate patients.

The computer program produces a list of telemedicine candidate patients. The list of telemedicine candidate patients includes, for example, the patient's name, contact information (address, email address, etc.). This makes it possible to ascertain the number of patients suitable for telemedicine. In addition, it is possible to notify a patient who is suitable for telemedicine about the telemedicine service.

The computer program according to the present embodiment causes the computer to execute a process of outputting the guidance data of the telemedicine service to the telemedicine candidate patient.

The terminal device according to the present embodiment is a terminal device provided with a display screen and used by a telemedicine candidate patient, and has a receiving unit for receiving guidance data of a telemedicine service and a guidance data received by the receiving unit. It is provided with a display control unit that displays a guide document based on the guide document on the display screen.

The computer program outputs guidance data for the telemedicine service to the telemedicine candidate patient. The guidance data is, for example, data for generating a guidance document for a telemedicine service. The guidance document may be recorded on a paper medium or displayed on a display screen. This allows the guidance to be notified via a sealed letter or e-mail.

The computer program according to the present embodiment causes the computer to execute a process of generating guidance information or life improvement measure information unique to the telemedicine candidate patient included in the guidance data.

In the terminal device according to the present embodiment, the display control unit displays guidance information or life improvement measure information unique to the telemedicine candidate patient.

The computer program generates guidance information or life improvement measure information unique to the telemedicine candidate patient, which is included in the guidance data. The unique guidance information includes, for example, the name of the injury or illness peculiar to the telemedicine candidate patient, information indicating the possibility of participating in the telemedicine, and the like. The unique life improvement measure information includes, for example, an optimal life improvement program for a telemedicine candidate patient (for example, an essential or recommended exercise program, a diet improvement program, etc.). This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

The computer program according to the present embodiment causes the computer to execute a process of generating medical expense information before and after the use of telemedicine regarding the telemedicine candidate patient included in the guidance data.

In the terminal device according to the present embodiment, the display control unit displays medical expense information before and after the use of telemedicine for the telemedicine candidate patient.

The computer program generates medical expense information before and after the use of telemedicine regarding the candidate patients for telemedicine, which is included in the guidance data. For example, medical expenses before using telemedicine may include medical expenses for a specific month, monthly average medical expenses, dispensing expenses, and the like. In addition, the cost of the telemedicine service can be included as the medical cost information after using the telemedicine. This makes it possible to encourage telemedicine candidate patients to participate in the telemedicine service.

The computer program according to the present embodiment has a process of extracting patients with a predetermined disease based on the name of injury or illness or medical practice related to any patient included in the acquired receipt data, and the number of patients with the predetermined disease. Alternatively, the process of calculating statistical information including the ratio of the number of patients with the predetermined disease to the number of the plurality of patients is executed.

The computer program extracts patients with a predetermined disease based on the name of injury or illness or medical practice related to any patient included in the acquired receipt data. The patient with a predetermined disease can be, for example, a patient with a lifestyle-related disease.

The computer program calculates statistical information including the ratio of the number of patients with a predetermined disease to the number of patients with a predetermined disease or the number of multiple patients. As a result, statistical information on patients with lifestyle-related diseases can be obtained, and the number of patients with lifestyle-related diseases can be grasped.

The computer program according to the present embodiment causes the computer to execute a process of calculating the reduction amount of medical expenses based on the number of patients with the predetermined disease.

The computer program calculates the amount of reduction in medical expenses based on the number of patients with a given disease. For example, by comparing the medical expenses of lifestyle-related diseases between the case of outpatient treatment and the case of telemedicine, it is possible to grasp the effect of reducing medical expenses by introducing the telemedicine service.

1 Communication network 5 Receipt 10 Terminal equipment 11 Control unit 12 Communication unit 13 Display control unit 14 Display screen 15 Operation unit 50 Receipt analyzer 51 Control unit 52 Acquisition unit 53 Classification judgment unit 54 Candidate patient judgment unit 55 Storage unit 56 Communication unit 57 Guidance information generation unit 58 Calculation unit 59 Target person extraction unit

Claims (13)

A computer program that lets a computer analyze a receipt,
On the computer
The process of acquiring receipt data for each of multiple patients over the first period,
Based on the name of injury or illness or medical practice related to any patient included in the acquired medical receipt data, the process of determining the patient's classification for each second period shorter than the first period, and
A process of determining whether or not the patient is a candidate for telemedicine is executed according to whether or not the classification of the patient for each second period satisfies a predetermined condition over the first period. Computer program. On the computer
When the injury / illness name or medical practice pertaining to the arbitrary patient is a predetermined injury / illness name or medical practice, the process of determining that the category of the patient for each second period is an exclusion category to be excluded from the telemedicine candidate patients. The computer program according to claim 1 to be executed. On the computer
The receipt data for the second period includes a plurality of injury / illness names or medical practices related to the arbitrary patient, and the injury / illness name or medical practice of one of the plurality of injury / illness names or medical practices is a predetermined injury / illness name or medical practice. The computer program according to claim 2, wherein in the case of a medical practice, a process of determining that the category of the patient for each second period is the exclusion category is executed. On the computer
Claim 2 or claim 2 for executing a process of determining that the patient is not a telemedicine candidate patient when the category of the patient for each second period is the exclusion category in the second period of one of the first periods. The computer program according to claim 3. On the computer
The computer program according to claim 4, wherein when the exclusion category does not exist during the first period, the process of determining that the patient is a telemedicine candidate patient is executed. On the computer
The computer program according to any one of claims 1 to 5, which executes a process of generating a list of telemedicine candidate patients. On the computer
The computer program according to any one of claims 1 to 6, wherein the process of outputting the guidance data of the telemedicine service to the telemedicine candidate patient is executed. On the computer
The computer program according to claim 7, wherein a process of generating guidance information or life improvement measure information unique to the telemedicine candidate patient included in the guidance data is executed. On the computer
The computer program according to claim 7 or 8, wherein a process of generating medical expense information before and after the use of telemedicine regarding the telemedicine candidate patient included in the guidance data is executed. On the computer
Processing to extract patients with a predetermined disease based on the name of injury or illness or medical practice related to any patient included in the acquired medical receipt data.
The computer according to any one of claims 1 to 9, wherein the process of calculating statistical information including the ratio of the number of patients with the predetermined disease to the number of patients with the predetermined disease or the number of the plurality of patients is executed. program. On the computer
The computer program according to claim 10, wherein the process of calculating the reduction amount of medical expenses is executed based on the number of patients with the predetermined disease. An acquisition unit that acquires receipt data for the first period of each of multiple patients,
Based on the name of injury or illness or medical practice related to any patient included in the receipt data acquired by the acquisition unit, the classification determination unit that determines the classification of the patient every second period shorter than the first period, and the classification determination unit.
With a candidate patient determination unit that determines whether or not the patient is a telemedicine candidate patient, depending on whether or not the classification of the patient for each second period satisfies a predetermined condition over the first period. Receipt analyzer. It is a telemedicine guidance method using a medical receipt analyzer.
The receipt analyzer is
Obtained receipt data for each of multiple patients over the first period,
Based on the name of injury or illness or medical practice related to any patient included in the acquired medical receipt data, the classification of the patient is determined for each second period shorter than the first period.
It is determined whether or not the patient is a telemedicine candidate patient according to whether or not the classification of the patient for each second period satisfies the predetermined condition over the first period.
A telemedicine guidance method that outputs information on telemedicine services to determined telemedicine candidate patients.

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