Patient information generation system, patient information generation method, and patient information generation program

Abstract

To provide a patient information generation system that can generate accurate, detailed patient information, prediction information, and warning information by using attribute information and prescription information of a patient from at least one of patient's chart data in a medical institution and receipt data in a pharmacy.SOLUTION: A patient information generation system of the present invention includes a processor and a storage, and the processor generates display information for displaying patient information on map data. The processor comprises: a patient information acquisition unit that acquires attribute information and prescription information of a patient from at least one of patient's chart data in a medical institution and receipt data in a pharmacy; and a generation unit that, on the basis of at least one of the address and a residence of the patient, generates display data for displaying at least one of the attribute information and prescription information of the patient at a predetermined position.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a patient information generation system, a patient information generation method, and a patient information generation program, and particularly to a patient information generation system, a patient information generation method, and a patient information generation program for generating display information for displaying patient information on map data.

Background Art

[0002] Patients who receive medical treatment at hospitals or clinics bring the prescriptions issued by the hospitals or clinics to pharmacies and receive medicines based on the prescriptions. The information contained in the prescriptions is useful information for the operation and management of medical institutions and pharmacies, but it has not been fully utilized.

[0003] On the other hand, a method for providing medical-related information such as the number of regional patients within a predetermined medical treatment area, competing medical institution information, and the expected number of hospital visits to support business start-ups has been disclosed (Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the method disclosed in Patent Document 1, the number of patients by age within a predetermined area is calculated, and the information on the number of regional patients is calculated by comparing it with a preset medical treatment rate by age, and the information on the prescriptions is not utilized.

Means for Solving the Problems

[0006] The patient information generation system of the present invention includes a processor and a storage device, and is a patient information generation system in which the processor generates display information for displaying patient information on map data. The processor obtains at least one of the patient's age, address, residence, postal code, contact method, contact information, gender, height, weight, specific health check information, health diagnosis information, inquiry information, and diagnosis information as the patient's attribute information from at least one of the medical institution's medical record data and the pharmacy's receipt data, and obtains at least one of the patient's prescription drugs, number of prescriptions, efficacy, effect, usage, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information as prescription information. A patient information acquisition unit, and a generation unit that generates display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position on the map data based on at least one of the address and the residence.

Advantages of the Invention

[0007] According to the present invention, by using the patient's attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information.

Brief Description of the Drawings

[0008]

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Mode for Carrying Out the Invention

[0009] The patient information generation system according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing an example of the system configuration of the patient information generation system of the present embodiment. The patient information generation system 200 generates display information for displaying patient information on map data.

[0010] As shown in FIG. 1, the patient information generation system 200 is electrically connected via a network 201 and includes a computer (PC) 202 and a server 203.

[0011] FIG. 2 is a block diagram showing an example of the system configuration of the present embodiment. As shown in FIG. 2, the computer 202 includes a processor 20, a storage device (for example, ROM, RAM, HDD, etc.) 21, an input device 22, an output device 23, an interface 24, and a display device 25, and is electrically connected by a bus (not shown) and can communicate with each other.

[0012] The processor 20 is a control device such as a CPU, MPU, or GPU, and includes a map data acquisition unit 20a, a position designation unit 20b, a patient information designation unit 20c, a display data acquisition unit 20d, a distance information designation unit 20e, a prediction command unit 20f, and a notification command unit 20g.

[0013] FIG. 3 is a block diagram showing an example of the system configuration of the server 203 according to the present embodiment. As shown in FIG. 3, the server 203 includes a processor 11, a storage device (e.g., ROM, RAM, HDD, etc.) 12, an input device 13, an output device 14, and an interface 15, which are electrically connected by a bus (not shown) and can communicate with each other. The processor 11 is a control device such as a CPU, MPU, or GPU, and includes a map data transmission unit 11a, a position information acquisition unit 11b, a patient information acquisition unit 11c, a generation unit 11d, a machine learning unit 11e, an inference unit 11f, and a notification unit 11g. The storage device 12 includes an attribute information storage unit 12a, a prescription information storage unit 12b, a map data storage unit 12c, a position information storage unit 12d, a vital data / activity data storage unit 12e, and a learned model storage unit 12f.

[0014] FIG. 4 is a diagram showing an example of attribute information. As shown in FIG. 4, the attribute information storage unit 12a includes at least one piece of information of the patient's age, address, residence, postal code, contact method, contact destination, gender, height, weight, specific health examination information, health diagnosis information, inquiry information, and diagnosis information associated with a patient ID (name, insurance number, symbol / number of the insured person's certificate, any symbol / number, etc.) obtained from at least one of the medical institution's medical record data and the pharmacy's receipt data. At least one of the specific health examination information, health diagnosis information, inquiry information, and diagnosis information may include at least one piece of information of the patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, compliance / adherence, whether breastfeeding, presence or absence of pregnancy, past history, side effect history, allergy history, concomitant medications, lifestyle habits (exercise, diet, sleep, etc.), hobbies (drinking, smoking, etc.), and family past history. Also, although not shown, at least one of the specific health examination information, health diagnosis information, inquiry information, and diagnosis information may include at least one piece of information of blood test results, blood pressure, blood glucose level, lipid metabolism, vision, hearing, and heart function.

[0015] The medical record data is obtained from a hospital or clinic and includes the attribute information and prescription information of the patient who has been issued a prescription. The receipt data is obtained from a pharmacy and includes the attribute information and prescription information of the patient who has been issued a prescription.

[0016] FIG. 5 is a diagram showing an example of prescription information. As shown in FIG. 5, the prescription information storage unit 12b includes at least one of the patient's prescribed drugs, the number of prescriptions, efficacy, effect, usage, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information obtained from at least one of the medical institution's medical record data and the pharmacy's receipt data, and is associated with a patient ID (such as name, insurance number, symbol / number on the insured person's certificate, or any symbol / number), and these items are associated with each other.

[0017] Note that, as shown in FIG. 6(a), by classifying the patient's age into a plurality of groups (0 - 5 years old, 6 - 21 years old, 22 - 40 years old, 41 - 69 years old, 70 - 74 years old, 75 years old and above), the attribute information storage unit 12a is expanded. Also, height or weight may be classified into a plurality of groups. Also, lifestyle habits and hobbies (such as frequency of drinking and smoking and sleep time) may be classified into a plurality of groups. In this way, by classifying the attribute information into a plurality of groups, the attribute information storage unit 12a is expanded.

[0018] Also, as shown in FIG. 6(b), the medical treatment date or the prescription date is classified into a plurality of groups (season, quarter, month, and day of the week). By classifying the medical treatment date into at least one of season, quarter, month, and day of the week, the attribute information storage unit 12a or the prescription information storage unit 12b is expanded.

[0019] Also, for items where the attribute information and the prescription information overlap, either piece of information may be given priority, or either piece of information may be deleted after a certain period. For example, the patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, and pharmacy name in the prescription information may be stored as attribute information in the attribute information storage unit 12a from the prescription information storage unit 12b and deleted from the prescription information storage unit 12b after a certain period.

[0020] The map data storage unit 12c includes geographical information (such as terrain and distance), artificial structure information, administrative district information (such as the names, locations, and boundaries of municipalities), and place name information, and may also include display format information for displaying these in various formats such as aerial photographs, illustrations, computer graphics, two-dimensional, and three-dimensional.

[0021] The location information storage unit 12d includes at least one piece of information of at least one of the addresses and postal codes of medical institutions and pharmacies, and includes correspondence information for corresponding to the location of the map data storage unit 12c.

[0022] The vital data / activity data storage unit 12e stores at least one of the patient's vital data and activity data acquired from a wearable device or the like. The vital data and activity data include at least one piece of information of pulse, blood pressure, body temperature, oxygen saturation, number of steps, calories consumed, sleep time, amount of exercise, and exercise time.

[0023] The learned model storage unit 12f stores a learned model learned by machine learning based on at least one time series data of attribute information and prescription information for inferring prediction information.

[0024] FIG. 7 is a flowchart showing an example of the patient information generation method of the present embodiment. FIG. 8 is a sequence diagram showing an example of the operations executed by the patient information generation program of the present embodiment. FIG. 8 is a sequence diagram showing an example of the operation of generating display data. The patient information generation program may be mounted on a recording medium. FIG. 9 is a diagram showing an example of an input screen 90 displayed on the display device 25 of the computer 202.

[0025] As shown in FIGS. 7 and 8, the map data acquisition unit 20a of the computer 202 transmits a map data acquisition command to the server 203 via the network 201 (step S1,120). The map data acquisition unit 20a transmits a map data acquisition command by inputting an address or residence by the input device 22, designating a location on the map, or zooming in or out.

[0026] The map data transmission unit 11a of the server 203 receives a map data acquisition command, acquires map data corresponding to the map data acquisition command from the map data storage unit 12c, and transmits the map data to the computer 202 via the network 201 (step S2,121). Note that the map data transmission unit 11a may refer to the position information storage unit 12d and transmit position information indicating at least one position of a medical institution and a pharmacy having an address on the map data corresponding to the map data acquisition command together with the map data.

[0027] The map data acquisition unit 20a of the computer 202 acquires the map data transmitted from the server 203 and causes the display device 25 to display the map data. Further, when the map data acquisition unit 20a acquires position information indicating at least one position of a medical institution and a pharmacy transmitted from the server 203, the map data acquisition unit 20a causes the display device 25 to display the position information indicating at least one position of the medical institution and the pharmacy on the map data.

[0028] When specifying a position in step S3, the position specifying unit 20b of the computer 202 transmits a position specifying command to the server 203 via the network 201 when the position specifying icon 16a on the input screen 90 of the computer 202 is specified or selected (step S4,122). The position specifying unit 20b transmits a position specifying command when at least one position information (address) of a medical institution and a pharmacy is input by the input device 22, or when position information (a mark indicating a medical institution or a pharmacy or the position of a medical institution or a pharmacy) indicating at least one position of a medical institution and a pharmacy on the map data is specified, or when a favorite medical institution name, pharmacy name, or address is selected.

[0029] The position information acquisition unit 11b of the server 203 receives a position information acquisition command, and acquires at least one position information of a medical institution and a pharmacy corresponding to the position information acquisition command from the position information storage unit 12d (step S5,123).

[0030] The patient information specifying unit 20c of the computer 202 transmits a patient information specifying command to the server 203 via the network 201 (step S6,124). The patient information specifying unit 20c can specify or select at least one of the patient's age, address, place of residence, postal code, contact method, contact information, gender, height, weight, specific health check information, health diagnosis information, inquiry information, and diagnosis information as the patient's attribute information. In addition, the patient information specifying unit 20c can specify or select at least one of the patient's prescription drugs, number of prescriptions, efficacy, effect, usage method, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information as the prescription information.

[0031] The patient information specifying unit 20c transmits a patient information specifying command when the patient information specifying area 16b on the input screen 90 of the computer 202 is specified or selected by the input device 22. When the detailed search icon 16b-1 on the input screen 90 of the computer 202 is specified or selected, a detailed specification area for the attribute information and the prescription information is displayed on a pop-up screen or the like, and the patient information specifying unit 20c may transmit a patient information specifying command when detailed attribute information or prescription information is specified or selected by the input device 22.

[0032] The patient information acquisition unit 11c of the server 203 receives the patient information specifying command and acquires, in response to the patient information specifying command, the attribute information and the prescription information of the patient who has received a medical examination or a prescription from at least one of the medical institutions and pharmacies on the map data from the attribute information storage unit 12a and the prescription information storage unit 12b (step S7,125). When the position information acquisition unit 11b has received a position information acquisition command, the attribute information and the prescription information of the patient who has received a medical examination or a prescription from at least one of the medical institutions and pharmacies corresponding to the position information acquisition command are acquired from the attribute information storage unit 12a and the prescription information storage unit 12b.

[0033] If the location is not specified in step S3, in step S6, the patient information specifying unit 20c of the computer 202 transmits a patient information specifying command to the server 203. The patient information acquisition unit 11c of the server 203 receives the patient information specifying command, and in response to the patient information specifying command, acquires the attribute information and prescription information of the patient having an address or residence on the map data from the attribute information storage unit 12a and the prescription information storage unit 12b (steps S7, 125).

[0034] The generation unit 11d of the server 203 generates display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position on the map data based on at least one of the patient's address and residence (steps S8, 126). For example, the generation unit 11d generates icon data for displaying the patient information (attribute information and prescription information) of the patient acquired from the attribute information storage unit 12a and the prescription information storage unit 12b in response to the patient information specifying command at a predetermined position on the map data. In addition, the generation unit 11d generates display data for displaying the number of patients selected by at least one of the attribute information and the prescription information on the map data. The generation unit 11d transmits the generated display data to the server 203 via the network 201 (steps S8, 126).

[0035] The display data acquisition unit 20d of the computer 202 acquires the display data transmitted from the server 203 and causes the display device 25 to display the display data on the map data (steps S9, 127).

[0036] FIG. 10 is a diagram showing an example of a map screen 100 displayed on the display device 25 of the computer 202. In the map data 10 of the map screen 100, position information (mark 5) indicating at least one position of a medical institution and a pharmacy is displayed. Also, patient information is displayed in the map data 10 together with the number of patients (number information). Here, administrative districts 6a to 6c corresponding to the addresses and residences of the patients and the number of patients by age 1a to 1g for each administrative district 6a to 6c are displayed in the map data 10. The ages of the patients are distinguished by colors, and by referring to the age chart 3, the age group of the patients can be recognized. The number of patients (number information) is distinguished by the size of the bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Further, in the total patient number area 4, the total number of patients displayed in the map data 10 is displayed.

[0037] Note that before the display data acquisition unit 20d displays the display data on the map data of the display device 25, the display data acquisition unit 20d may display the total number of patients in the total patient number area 16d (FIG. 9) of the input screen 90. In this case, when the confirmation icon 16e on the input screen 90 is specified, map data 10 as shown in FIG. 10 may be displayed on the display device 25 together with the patient information.

[0038] When specifying the distance information in step S10, the distance information specifying unit 20e of the computer 202 transmits a distance information specifying command to the server 203 via the network 201 (steps S11, 140). The distance information specifying unit 20e transmits a distance information specifying command by having a specific distance input by the input device 22, having a preset distance input, having a predetermined point or range specified, enlarged, or reduced on the map data.

[0039] The patient information acquisition unit 11c of the server 203 receives the distance information specifying command and acquires attribute information and prescription information corresponding to the distance information from the attribute information storage unit 12a and the prescription information storage unit 12b (steps S12, 141).

[0040] The generation unit 11d of the server 203 generates display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position on the map data based on at least one of the patient's address and place of residence (steps S13, 142).

[0041] The generation unit 11d of the server 203 generates display data for displaying at least one of the location information of at least one of the medical institution and the pharmacy, the distance information from the medical institution, and the distance information from the pharmacy on the map data (steps S13, 142).

[0042] The generation unit 11d transmits the display data generated for the server 203 via the network 201 (steps S13, 142).

[0043] The display data acquisition unit 20d of the computer 202 acquires the display data transmitted from the server 203 and causes the display data to be displayed on the map data of the display device 25 (steps S14, 143).

[0044] In this way, by using the patient's attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, highly accurate and detailed patient information can be generated and displayed at a predetermined position on the map data.

[0045] FIG. 11 is a diagram showing an example of a map screen 100 displayed on the display device 25 of the computer 202. In the map data 10 of the map screen 100, position information (mark 5) indicating the position of at least one of a medical institution and a pharmacy is displayed. Further, distance information 2a, 2b from the medical institution is displayed in the map data 10. For example, the distance information 2a is a circle with a radius of 3 km from the position (mark 5) of the medical institution, and the distance information 2b is a circle with a radius of 1 km from the position (mark 5) of the medical institution. In FIG. 11, patient information corresponding to the distance information 2a is displayed together with the number of patients. Here, administrative districts 6a to c corresponding to the addresses / residences of patients within a radius of 3 km from the position (mark 5) of the medical institution and the number of patients by age 1a to e for each of the administrative districts 6a to c are displayed in the map data 10. Also, similar to FIG. 10, the ages of the patients are distinguished by colors, the number of patients (number information) is distinguished by the size of the bubbles, and the total number of patients displayed in the map data 10 is shown.

[0046] Also, when specifying a position in step S3, position information (addresses) of a plurality of medical institutions or pharmacies may be input, or position information (marks indicating medical institutions or pharmacies or the positions of medical institutions or pharmacies) indicating the position of at least one of a medical institution and a pharmacy on the map data may be specified, or a favorite medical institution name, pharmacy name, or address may be selected (step S4), so that position information of a plurality of medical institutions or pharmacies is transmitted from the server 203 to the computer 202, and position information (marks, etc.) for displaying the positions of a plurality of medical institutions or pharmacies may be displayed on the map data of the display device 25 of the computer 202. Further, even when not specifying a position in step S3, position information of a plurality of medical institutions or pharmacies having addresses on the map data may be transmitted from the server 203 to the computer 202, and position information (marks, etc.) for displaying the positions of a plurality of medical institutions or pharmacies may be displayed on the map data of the display device 25 of the computer 202.

[0047] In this case, in step S13, the generation unit 11d of the server 203 generates display data for displaying at least one of the patient attribute information and prescription information at a predetermined position on the map data based on at least one of the address and residence of the patients who have received examinations or prescriptions from multiple medical institutions or pharmacies. Further, the generation unit 11d of the server 203 generates display data for displaying at least one of the location information of multiple medical institutions or pharmacies, the distance information from the medical institutions, and the distance information from the pharmacies on the map data.

[0048] FIG. 12 is a diagram showing an example of a map screen 100 displayed on the display device 25 of the computer 202. In the map data 10 of the map screen 100, location information (marks 5a, 5b) for displaying at least one location of multiple medical institutions or pharmacies is displayed. Further, distance information 2a, 2b from the first medical institution (mark 5a) is displayed in the map data 10, and distance information 2c, 2d from the second medical institution (mark 5b) is displayed. For example, the distance information 2a is a circle with a radius of 3 km from the location of the first medical institution (mark 5a), and the distance information 2b is a circle with a radius of 1 km from the location of the first medical institution (mark 5a). The distance information 2c is a circle with a radius of 3 km from the location of the second medical institution (mark 5b), and the distance information 2d is a circle with a radius of 1 km from the location of the second medical institution (mark 5b). In FIG. 12, the patient information of the patients who have received examinations from the first medical institution according to the distance information 2a is displayed together with the number of patients (number information). The patient information of the patients who have received examinations from the second medical institution according to the distance information 2c is displayed together with the number of patients (number information). Also, similar to FIGS. 10 and 11, the ages of the patients are distinguished by colors, the number of patients (number information) is distinguished by the size of the bubbles, and the total number of patients displayed in the map data 10 is shown.

[0049] Patient information may be displayed in a bubble chart as shown in FIGS. 10 to 12, or may be displayed in a heat map together with a patient number chart 7 that refers to the number of patients as shown in FIGS. 13 and 14, or may be displayed illustratively or three-dimensionally together with major artificial structures as shown in FIG. 15. Also, although not shown, it may be displayed graphically on the map data 10.

[0050] For such a type of map data, in step S1, when the map data acquisition unit 20a transmits a map data acquisition command, it may specify or select a map data type. Also, when specifying a position in step S4 or specifying patient information in step S6, the map type specification icon 16c (FIG. 9) on the input screen 90 may be specified or selected, whereby the map data acquisition unit 20a may specify or select a map data type. Also, when specifying a position in step S4 or specifying patient information in step S6, the map type specification icon 16c (FIG. 9) on the input screen 90 may be specified or selected, whereby the map data acquisition unit 20a may specify or select a map data type.

[0051] FIG. 16 is a diagram showing an example of a map screen 100 displayed on the display device 25 of the computer 202. On the map data 10 of the map screen 100, human icons 1h to k are displayed together with the number information of patients and the increase / decrease information of the number information. On the map data 10 of the map screen 100, the number information of patients and the increase / decrease information of the number information are displayed. The gender is distinguished by human icons. The increase / decrease information of the number information is displayed by arrow icons. For example, in addition to the comparison with the previous month or the same month of the previous year, the increase / decrease information is displayed by comparing a predetermined period. In FIG. 16, the increase / decrease is indicated by the direction of the arrow, and the magnitude of the increase / decrease is indicated by the length of the arrow. The age of the patients is distinguished by color, and by referring to the age chart 3, the age group of the patients can be recognized. The number of patients (number information) is distinguished by the size of male and female icons, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Further, in the total patient number area 4, the total number of patients displayed in the map data 10 is displayed.

[0052] In this case, in addition to the above display data (including icon data), the generation unit 11d generates display data for displaying at least one of the number information of patients selected by at least one of the attribute information and the prescription information and the increase / decrease information of the number information on the map data, and transmits the generated display data to the server 203 via the network 201 (step S8,126).

[0053] Note that the human icon may be an icon indicating an age group, such as an infant icon, a child icon, an elderly icon, or the like.

[0054] In this way, by using the patient attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, highly accurate and detailed patient information can be generated, and information that is visually easy for the user to understand can be displayed at a predetermined position on the map data.

[0055] Next, an embodiment for generating at least one of prediction information and warning information will be described. FIG. 17 is a flowchart showing an example of a method for generating prediction information and warning information in this embodiment. FIG. 18 is a sequence diagram showing an example of an operation for generating prediction information and warning information. FIG. 19 is a diagram showing an example of an input screen 90 displayed on the display device 25 of the computer 202.

[0056] Steps S1 to S9 are the same as above. After step S6 and before steps S7 to S9, it may automatically proceed to the prediction sequence of step S22. Also, after step S9, when a command to proceed to the prediction sequence is input by the input device 22 of the computer 202, it may proceed to the prediction sequence of step S22.

[0057] In step S21, the prediction command unit 20f of the computer 202 transmits a prediction command to the server 203 via the network 201 (step S21,150).

[0058] In step S22, the patient information acquisition unit 11c of the server 203 acquires patient information of a patient who has received a diagnosis or a prescription from at least one of a medical institution and a pharmacy on the map data in response to a patient information designation command from the attribute information storage unit 12a and the prescription information storage unit 12b (step S22, 151). If the attribute information and the prescription information have already been acquired in step S7, the attribute information and the prescription information acquired in step S7 may be used.

[0059] In step S23, 152, the generation unit 11d generates at least one of prediction information indicating at least one of a disease or injury of a predetermined patient, side effects, interactions, overdose, underdose, non - adherence, and refill prescription of prescription drugs for the predetermined patient, and warning information indicating at least one of at least one of a disease or injury, side effects, interactions, overdose, underdose, non - adherence, and refill prescription based on at least one of the attribute information and the prescription information of the predetermined patient (step S23, 152).

[0060] For example, based on at least one of specific health check information, medical examination information, inquiry information, and diagnosis information, when the disease or injury of a predetermined patient deteriorates or a complication is suspected based on a predetermined threshold, the generation unit 11d generates prediction information or warning information about the disease or injury of the predetermined patient. Based on a predetermined criterion (for example, blood sugar level, type of diabetes medicine, etc.), when diabetes is deteriorating or the risks of kidney damage, retinopathy, neuropathy, etc. associated with the deterioration of diabetes are increasing, these prediction information or warning information are generated. Based on a predetermined criterion (for example, blood pressure, type of hypertension medicine, etc.), when hypertension is deteriorating or the risks of stroke, myocardial infarction, kidney damage, etc. associated with the deterioration of hypertension are increasing, these prediction information or warning information are generated. Based on a predetermined criterion, when suffering from influenza or the risks of bacterial pneumonia, influenza encephalitis, myocarditis, etc. associated with influenza are increasing, these prediction information or warning information are generated.

[0061] The generation unit 11d may generate at least one of prediction information indicating a prediction regarding a patient's lifestyle and warning information indicating a warning regarding the lifestyle, based on an injury related to the patient's lifestyle or a prescription drug related to the injury.

[0062] The generation unit 11d may generate prediction information or warning information regarding side effects and interactions of prescription drugs by referring to a correspondence table created in advance. The generation unit 11d may generate prediction information or warning information regarding overdose, underdose, non - adherence, and refill prescriptions based on the number of prescriptions, prescription frequency, and prescription interval.

[0063] In this way, the generation unit 11d can generate at least one of prediction information or warning information regarding side effects, interactions, overdose, underdose, non - adherence, and refill prescriptions of a prescription drug for a predetermined patient, based on information regarding, for example, the injury name, drug name, compliance / adherence, medical history, side effect history, allergy history, concomitant medications, lifestyle, preferences, family medical history, blood test results, blood pressure, blood glucose level, number of prescriptions of prescription drugs, prescription days, and prescription dates of the predetermined patient.

[0064] In addition, the generation unit 11d generates at least one of prediction information indicating a prediction of the geographical spread of an injury (e.g., an infectious disease) in a predetermined area and warning information indicating a warning of the geographical spread, based on at least one of the attribute information and prescription information of patients in the predetermined area of the map data (step S23,152). Also, since there are seasonal injuries (e.g., influenza prevails from January to February) and the patient's behavior characteristics may be affected depending on the injury, the generation unit 11d uses data classifying the medical treatment date or the prescription date of the prescription drug into at least one of season, quarter, month, and day of the week to generate at least one of prediction information and warning information.

[0065] For example, the generation unit 11d generates at least one of prediction information indicating an increase or decrease prediction of injuries and illnesses in a region and warning information indicating a warning of injuries and illnesses based on at least one of the number of patients with a predetermined injury or illness, the types of prescription drugs related to the injury or illness, the number of prescriptions for the prescription drugs related to the injury or illness, and the number of prescription forms for the prescription drugs related to the injury or illness in a predetermined region of the map data (for example, season, 7-day average value of the number of patients, population density, effective reproduction number, etc.).

[0066] Also, the generation unit 11d generates at least one of prediction information indicating a prediction related to at least one of an antibacterial drug and a resistant bacterium of the antibacterial drug and warning information indicating a warning related to at least one of the antibacterial drug and the resistant bacterium of the antibacterial drug based on at least one of the attribute information of patients and the prescription form information in a predetermined region of the map data (step S23,152).

[0067] For example, the generation unit 11d generates at least one of prediction information indicating an increase or decrease prediction of injuries and illnesses in a region and warning information indicating a warning of injuries and illnesses based on at least one of the number of patients with a predetermined infectious disease, the types of antibacterial drugs, the number of prescriptions for the antibacterial drugs, and the number of prescription forms for the antibacterial drugs in a predetermined region of the map data (for example, 7-day average value of the number of patients, population density, effective reproduction number, etc.).

[0068] Also, the generation unit 11d generates at least one of prediction information indicating a prediction related to at least one of the consumption and inventory of medical supplies in a medical institution or pharmacy and warning information indicating a warning related to at least one of the consumption and inventory of medical supplies based on at least one of the attribute information of patients and the prescription form information in a predetermined region of the map data.

[0069] For example, according to the inventory status and procurement status of predetermined medical supplies in a predetermined medical institution or pharmacy of the map data, based on a predetermined standard related to at least one of the number of patients with a predetermined injury or illness, the types of prescription drugs related to the injury or illness, the number of prescriptions for the prescription drugs related to the injury or illness, and the number of prescription forms for the prescription drugs related to the injury or illness, at least one of prediction information indicating a prediction related to at least one of the consumption and inventory of medical supplies in the predetermined medical institution or pharmacy and warning information indicating a warning of injuries and illnesses is generated.

[0070] The generation unit 11d transmits at least one of the prediction information generated by the server 203 via the network 201 and the warning information indicating the injury and illness warning (prediction warning information) (steps S24, 153). The display data acquisition unit 20d of the computer 202 acquires the prediction warning information transmitted from the server 203 and displays the display data on the map data of the display device 25 (steps S25, 154).

[0071] In step S26, the notification command unit 20g of the computer 202 transmits a notification command to the server 203 via the network 201 (step S26, 155).

[0072] In step S27, the notification unit 11g of the server 203 notifies at least one of the medical institution, pharmacy, and patient of at least one of the prediction information and the warning information in response to the notification command (step S27, 156). When notifying the patient, the notification unit 11g reads out the contact information of the patient from the attribute information storage unit 12a and notifies the patient.

[0073] FIG. 19 is a diagram showing an example of the input screen 160 displayed on the display device 25 of the computer 202. By designating or selecting the injury and illness prediction 16f-1 in the prediction warning information designation area 16f, the generation unit 11d of the server 203 determines the injury and illness of the patient specified by the patient information (attribute information and prescription information), as well as the side effects, interactions, overdose, underdose, non-adherence, and refill prescription of the prescribed drugs for a predetermined patient. At least one of the prediction information indicating at least one prediction and the warning information indicating at least one warning of injury and illness, side effects, interactions, overdose, underdose, non-adherence, and refill prescription is generated.

[0074] Note that the patient information specifying unit 20c may transmit a patient information specifying command to the server 203 via the network 201 when the patient information specifying area 16g of the input screen 160 is specified or selected by the input device 22. In this case, when the prediction warning information specifying area 16f of the input screen 160 is specified or selected, a detailed specification area for attribute information and prescription information is displayed on a pop-up screen or the like, and the patient information specifying unit 20c may transmit a patient information specifying command when detailed attribute information or prescription information is specified or selected by the input device 22. Also, in the patient information specifying area 16g, the patient information specifying unit 20c may transmit a patient information specifying command when a disease or injury is specified by disease or injury search or a favorite disease or injury is selected.

[0075] When the map type specifying icon 16h is specified or selected, the map data acquisition unit 20a may specify or select a map data type. The total number of patients may be displayed in the total number of patients area 16i. When the confirmation icon 16j is specified, the map data 10 may be displayed on the display device 25 together with the patient information.

[0076] FIG. 20 is a diagram showing an example of the input screen 160 displayed on the display device 25 of the computer 202. When the infectious disease prediction 16f-2 is specified or selected in the prediction warning information specifying area 16f, the generation unit 11d of the server 203 generates at least one of prediction information indicating a prediction of the geographical spread of a disease or injury (e.g., an infectious disease) in a predetermined area of the map data and warning information indicating a warning of the geographical spread based on at least one of the attribute information and prescription information of the patients in the predetermined area of the map data. In the patient information specifying area 16g, the patient information specifying unit 20c may transmit a patient information specifying command when an infectious disease name or the symptoms of an infectious disease are specified by infectious disease / symptom search or a favorite infectious disease name is selected.

[0077] FIG. 21 is a diagram showing an example of a map screen 100 on which prediction warning information is displayed on the display device 25 of the computer 202. The conditions specified or selected in the prediction warning information designation area 16f are displayed in the display condition area 110. The patient information specified or selected in the patient information designation area 16g is displayed in the patient information display area 111. The display format of the map data 10 is displayed in the display format area 112. By designating or selecting the display format area 112, the display format of the map data 10 may be changed. When the data output area 113 is designated or selected, patient information, prediction warning information, medical institution information, pharmacy information, etc. are output together with a graph. When the return area 114 is designated or selected, the process transitions to the input screen 160 of FIG. 20.

[0078] In the map data 10, patient information (patient information regarding patients suffering from injuries or infectious diseases, patient information regarding medical supplies for injuries or infectious diseases, etc.) specified or selected in the patient information designation area 16g is displayed together with the number of patients (number information). Here, administrative districts 6a and 6b corresponding to the current (for example, the current month) addresses / residences of the patients and the number of patients by age group 1a to 1d for each of the administrative districts 6a and 6b are displayed in the map data 10. Also, the ages of the patients are distinguished by color, and by referring to the age chart 3, the age group of the patients can be recognized. The number of patients (number information) is distinguished by the size of the bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Furthermore, in the total patient number area 4, the total number of patients displayed in the map data 10 is displayed.

[0079] In the map data 10, the prediction information and the warning information generated by the generation unit 11d are displayed together with the number of patients (number information). Here, the administrative districts 6a, 6b, 6d corresponding to the addresses / residences of the patients in the prediction period (for example, next month) and the predicted number of patients 8a - g by age for each of the administrative districts 6a, 6b, 6d are displayed near or at new locations corresponding to the number of patients 1a - d by age. Also, the ages of the patients are distinguished by colors, and by referring to the age chart 3, the age groups of the patients can be recognized. The predicted number of patients (number information) is distinguished by the size of the dashed - line bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Warning information is generated and displayed such that the thickness and color of the dashed line of the dashed - line bubbles change according to the importance level of the prediction information (for example, the growth rate of the number of patients).

[0080] The medicine icon 9a is information regarding at least one of the consumption and inventory of medical supplies in the medical institution 5, and is generated by the generation unit 11d based on prescription information (for example, prescription drugs, number of prescriptions, and medical institution name, etc.). Here, the consumption number of Tamiflu for the current month (for example, the current month) is generated and displayed for the medicine icon 9a. The medicine icon (dashed - line) 9b is prediction information and warning information indicating a prediction regarding at least one of the consumption and inventory of medical supplies in the medical institution 5. Here, the consumption number of Tamiflu for the prediction period (for example, next month) is generated and displayed for the medicine icon 9b. The sizes of the medicine icons 9a and 9b change according to the consumption number or the inventory number. Warning information is generated and displayed such that the thickness and color of the dashed line of the medicine icon 9a change according to the importance level of the patient information (for example, the consumption number of prescription drugs for the current month). Warning information is generated and displayed such that the thickness and color of the dashed line of the medicine icon 9b change according to the importance level of the prediction information (for example, the predicted number of consumption numbers).

[0081] In the total prediction area 115, the total predicted number of patients displayed in the map data 10 is displayed based on the prediction information. When the notification area 116 is specified or selected, the notification command unit 20g transmits a notification command to the server 203 via the network 201.

[0082] In this way, by using the patient's attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information regarding injuries or infectious diseases, and to display information that is visually easy for the user to understand at a predetermined position on the map data, and to notify the patient, medical institution, or pharmacy.

[0083] FIG. 22 is a diagram showing an example of an input screen 160 displayed on the display device 25 of the computer 202. When the resistant bacteria prediction 16f-3 is designated or selected in the prediction warning information designation area 16f, the generation unit 11d of the server 203 is based on at least one of the patient's attribute information and prescription information in a predetermined area of the map data. At least one of prediction information indicating a prediction regarding at least one of an antibacterial drug and a resistant bacteria of the antibacterial drug and warning information indicating a warning regarding at least one of an antibacterial drug and a resistant bacteria of the antibacterial drug is generated. In the patient information designation area 16g, the patient information designation unit 20c may transmit a patient information designation command when an antibacterial drug name or a resistant bacteria name is designated or a favorite antibacterial drug name or a resistant bacteria name is selected in the antibacterial drug / resistant bacteria search.

[0084] FIG. 23 is a diagram showing an example of a map screen 100 on which prediction warning information is displayed on the display device 25 of the computer 202. The conditions designated or selected in the prediction warning information designation area 16f are displayed in the display condition area 110. The patient information designated or selected in the patient information designation area 16g is displayed in the patient information display area 111. The display format of the map data 10 is displayed in the display format area 112. By designating or selecting the display format area 112, the display format of the map data 10 may be made changeable. When the data output area 113 is designated or selected, patient information, prediction warning information, medical institution information, pharmacy information, etc. are output together with a graph. When the return area 114 is designated or selected, the process transitions to the input screen 160 of FIG. 22.

[0085] In the map data 10, the patient information (patient information regarding antibacterial drugs, patient information regarding patients infected with drug-resistant bacteria of antibacterial drugs, etc.) specified or selected in the patient information specifying area 16g is displayed together with the number of patients (number information). Here, based on the patient information (attribute information and prescription information) regarding oxacillin and MRSA, the administrative districts 6a and 6b corresponding to the addresses and residences of the current patients (for example, in the current month) and the number of patients by age group 1a to 1d for each of the administrative districts 6a and 6b are displayed in the map data 10. Also, the ages of the patients are distinguished by colors, and by referring to the age chart 3, the age groups of the patients can be recognized. The number of patients (number information) is distinguished by the size of the bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Furthermore, in the total patient number area 4, the total number of patients displayed in the map data 10 is shown.

[0086] In the map data 10, the prediction information and warning information generated by the generation unit 11d are displayed together with the number of patients (number information). Here, the administrative districts 6a and 6b corresponding to the addresses and residences of the patients during the prediction period (for example, next month) and the predicted number of patients by age group 18a to 1d for each of the administrative districts 6a and 6b are displayed in the vicinity of the corresponding number of patients by age group 1a to 1d. Also, the ages of the patients are distinguished by colors, and by referring to the age chart 3, the age groups of the patients can be recognized. The predicted number of patients (number information) is distinguished by the size of the dashed-line bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Warning information is generated and displayed such that the thickness and color of the dashed line of the dashed-line bubbles change according to the importance level (for example, the increase rate of the number of patients) of the prediction information.

[0087] The drug icon 19a is information regarding at least one of the consumption and inventory of medical supplies in the medical institution 5, and is generated by the generation unit 11d based on prescription information (for example, prescription drugs, number of prescriptions, and medical institution name, etc.). Here, the consumption number of vancomycin for the current month (for example, the current month) is generated and displayed for the drug icon 19a. The drug icon (dashed line) 19b is prediction information and warning information indicating a prediction regarding at least one of the consumption and inventory of medical supplies in the medical institution 5. Here, the consumption number of vancomycin for the prediction period is generated and displayed for the drug icon 19b. The sizes of the drug icons 19a and 19b change according to the consumption number or inventory number. Warning information is generated and displayed such that the thickness and color of the dashed line of the drug icon 19a change according to the importance of the patient information (for example, the consumption number of prescription drugs for the current month). Warning information is generated and displayed such that the thickness and color of the dashed line of the drug icon 19b change according to the importance of the prediction information (for example, the predicted number of consumption numbers).

[0088] In the prediction total number area 115, based on the prediction information, the predicted total number of patients displayed on the map data 10 is displayed. When the notification area 116 is designated or selected, the notification command unit 20g transmits a notification command to the server 203 via the network 201.

[0089] In this way, by using the patient's attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information regarding antibacterial drugs or resistant bacteria, and to display information that is visually easy for the user to understand at a predetermined position on the map data, and to notify patients, medical institutions, or pharmacies.

[0090] FIG. 24 is a diagram showing an example of an input screen 160 displayed on the display device 25 of the computer 202. When the consumption / inventory prediction 16f-4 is specified or selected in the prediction warning information specification area 16f, the generation unit 11d of the server 203 is based on at least one of the attribute information and prescription information of patients in a predetermined area of the map data. Generate at least one of prediction information indicating a prediction regarding at least one of the consumption and inventory of medical supplies in a medical institution or pharmacy and warning information indicating a warning regarding at least one of the consumption and inventory of medical supplies. In the patient information specification area 16g, the patient information specification unit 20c may send a patient information specification command by specifying a medical supply name in the medical supply search or selecting a favorite medical supply name.

[0091] FIG. 25 is a diagram showing an example of a map screen 100 on which prediction warning information is displayed on the display device 25 of the computer 202. The conditions specified or selected in the prediction warning information specification area 16f are displayed in the display condition area 110. The patient information specified or selected in the patient information specification area 16g is displayed in the patient information display area 111. The display format of the map data 10 is displayed in the display format area 112. By specifying or selecting the display format area 112, the display format of the map data 10 may be changed. When the data output area 113 is specified or selected, patient information, prediction warning information, medical institution information, pharmacy information, etc. are output together with a graph. When the return area 114 is specified or selected, the process transitions to the input screen 160 of FIG. 24.

[0092] In the map data 10, the patient information (such as patient information regarding the patient for whom the medical supplies were prescribed) specified or selected in the patient information specifying area 16g is displayed together with the number of patients (number of persons information). Here, based on the patient information (attribute information and prescription information) for which ibuprofen was prescribed, the administrative districts 6a, 6b corresponding to the addresses / residences of the patients currently (for example, in the current month) and the number of patients by age for each of the administrative districts 6a, 6b, 1a~d, are displayed in the map data 10. Also, the age of the patients is distinguished by color, and by referring to the age chart 3, the age group of the patients can be recognized. The number of patients (number of persons information) is distinguished by the size of the bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Further, in the total number of patients area 4, the total number of patients displayed in the map data 10 is shown.

[0093] In the map data 10, the prediction information and warning information generated by the generation unit 11d are displayed together with the number of patients (number of persons information). Here, the administrative districts 6a, 6b corresponding to the addresses / residences of the patients during the prediction period (for example, next month) and the predicted number of patients by age 28a~d for each of the administrative districts 6a, 6b are displayed in the vicinity of the corresponding number of patients by age 1a~d. Also, the age of the patients is distinguished by color, and by referring to the age chart 3, the age group of the patients can be recognized. The predicted number of patients (number of persons information) is distinguished by the size of the dashed bubbles, and by referring to the patient number chart 7, the range of the number of patients can be recognized. Warning information is generated and displayed such that the thickness and color of the dashed line of the dashed bubbles change according to the importance level of the prediction information (for example, the increase rate of the number of patients).

[0094] The medicine icon 29a is information regarding at least one of the consumption and inventory of medical supplies in the medical institution 5, and is generated by the generation unit 11d based on prescription information (for example, prescription drugs, number of prescriptions, and medical institution name, etc.). Here, the medicine icon 29a shows the consumption number of ibuprofen currently (for example, in the current month) being generated and displayed. The medicine icon (dashed line) 29b is prediction information and warning information indicating a prediction regarding at least one of the consumption and inventory of medical supplies in the medical institution 5. Here, the medicine icon 29b shows the predicted consumption number of ibuprofen during the prediction period being generated and displayed. The medicine icons 29a and 29b change in size according to the consumption number or inventory number. The medicine icon 29a generates and displays warning information such that the thickness and color of the dashed line change according to the importance level of the patient information (for example, the consumption number of prescription drugs in the current month). The medicine icon 29b generates and displays warning information such that the thickness and color of the dashed line change according to the importance level of the prediction information (for example, the predicted number of consumption numbers).

[0095] In the prediction total number area 115, based on the prediction information, the predicted total number of patients displayed on the map data 10 is displayed. When the notification area 116 is specified or selected, the notification command unit 20g sends a notification command to the server 203 via the network 201.

[0096] In this way, by using the patient's attribute information and prescription information from at least one of the medical institution's medical record data and the pharmacy's receipt data, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information regarding the consumption or inventory of medical supplies, and display information that is visually easy for the user to understand at a predetermined position on the map data, and it is also possible to notify patients, medical institutions, or pharmacies.

[0097] As described above, the embodiments according to the present invention have been explained, but the present invention is not limited to these, and it is possible to make changes and modifications within the scope described in the claims.

[0098] The generation unit 11d may generate prediction information and warning information by statistical analysis based on at least one of the attribute information and the prescription information. For example, ARIMA (AutoRegressive Integrated Moving Average), Seasonal ARIMA (SARIMA), exponential smoothing method, regression analysis, polynomial regression, Bayesian model, SIR model, agent-based modeling, and gray system theory, etc. may be used to generate prediction information and warning information.

[0099] Alternatively, the inference unit 11f may infer a prediction from a learned model learned by machine learning based on time series data of at least one of the attribute information and the prescription information, and the generation unit 11d may generate prediction information or warning information using the inference result of the inference unit 11f. In this case, by executing an inference instruction, the inference unit 11f inputs at least one of the attribute information and the prescription information to the learned model stored in the learned model storage unit 12f and infers prediction information. Based on this, the generation unit 11d generates prediction information and warning information. As machine learning, for example, Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and hybrid models (e.g., a combination of CNN and RNN), etc. are used.

[0100] For example, when generating a learned model using a neural network based on learning data (attribute information and prescription information), a machine learning unit (not shown) inputs patient information into an input layer and generates a learned model using a neural network that inputs attribute information or prescription information corresponding to prediction information as a correct value in an output layer. An Affine layer or a Convolution layer is provided in an intermediate layer of the neural network. Appropriately, downsampling processing or the like may be performed. Also, the number of layers, the number of neurons, and the activation function of the intermediate layer are selected to be optimal so that the inference result has high accuracy. As the neural network, a recurrent neural network (RNN) or the like is used. The machine learning unit calculates a loss function representing the divergence between the value output to the output layer when attribute information or prescription information is input to the input layer and the correct value of the output layer (attribute information or prescription information corresponding to the prediction information) using parameters (weights) initialized with predetermined values such as random numbers, and changes the parameters (weights) so that the divergence between the value output to the output layer and the correct value of the output layer becomes small, using the differential value of the loss function as a gradient, to generate a learned model. The machine learning unit may generate a learned model by machine learning based on learning data extended by using data obtained by classifying the medical treatment date or the prescription date of the prescription drug into at least one of season, quarter, month, and day of the week.

[0101] In this way, by using machine learning, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information.

[0102] Further, the generation unit 11d may acquire at least one of the patient's vital data and activity data, and generate at least one of prediction information and warning information by using at least one of the vital data and activity data.

[0103] In this way, by using vital data or activity data in addition to attribute information and prescription information, it is possible to generate highly accurate and detailed patient information, prediction information, and warning information.

Industrial Applicability

[0104] The present invention is useful as a disease name inference system that can accurately infer disease names in order to comprehensively infer disease names from prescription data by machine learning. In particular, it is useful as a disease name inference system that can accurately infer disease names by comprehensively considering a plurality of drugs.

Explanation of Signs

[0105] 10 Map data 11a Map data transmission unit 11b Position information acquisition unit 11c Patient information acquisition unit 11d Generation unit 11e Machine learning unit 11f Inference unit 11g Notification unit 12 Storage device 12a Attribute information storage unit 12b Prescription information storage unit 12c Map data storage unit 12d Position information storage unit 12e Vital activity data storage unit 12f Trained model storage unit 20a Map data acquisition unit 20b Position designation unit 20c Patient information designation unit 20d Display data acquisition unit 20e Distance information designation unit 20f Prediction command unit 20g Notification command unit 200 Patient information generation system 201 Network 202 Computer 203 Server

Claims

1. A patient information generation system including a processor and a storage device, wherein the processor generates display information for displaying patient information on map data, the processor is configured to: obtain at least one of the patient's age, address, residence, postal code, contact method, contact information, gender, height, weight, specific health examination information, health diagnosis information, interview information, and diagnosis information as the patient's attribute information from at least one of the medical institution's medical record data and the pharmacy's receipt data, and obtain at least one of the patient's prescribed medications, number of prescriptions, efficacy, effect, usage, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information as prescription information; a patient information acquisition unit; a generation unit that generates display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position on the map data based on at least one of the address and the residence; A patient information generation system, characterized by comprising the above.

2. The processor is configured to: include a location information acquisition unit that acquires location information of at least one of the medical institution and the pharmacy, The generation unit generates the display data for displaying at least one of the location information, distance information from the medical institution, and distance information from the pharmacy on the map data. The patient information generation system according to claim 1.

3. The generation unit generates the display data for displaying at least one of the number of patients selected by at least one of the attribute information and the prescription information and the increase or decrease information of the number of patients on the map data. The patient information generation system according to claim 1.

4. The generation unit generates at least one of prediction information indicating at least one of the diseases of the predetermined patient and predictions of at least one of side effects, interactions, overdose, underdose, non - adherence, and refill prescriptions of the prescribed medications for the predetermined patient, and warning information indicating at least one of warnings of the diseases, side effects, interactions, overdose, underdose, non - adherence, and refill prescriptions, based on at least one of the attribute information and the prescription information of the predetermined patient. The patient information generation system according to claim 1.

5. The generation unit generates at least one of prediction information indicating a prediction of geographical spread of injuries and illnesses in the predetermined area and warning information indicating a warning of the geographical spread, based on at least one of the attribute information and the prescription information of patients in the predetermined area of the map data. The patient information generation system according to claim 1, characterized in that.

6. The generation unit generates at least one of prediction information indicating a prediction regarding at least one of the antibacterial agent and the resistant bacteria of the antibacterial agent and warning information indicating a warning regarding at least one of the antibacterial agent and the resistant bacteria of the antibacterial agent, based on at least one of the attribute information and the prescription information of patients in the predetermined area of the map data. The patient information generation system according to claim 1, characterized in that.

7. The generation unit generates at least one of prediction information indicating a prediction regarding at least one of the consumption and inventory of medical supplies in a medical institution or pharmacy and warning information indicating a warning regarding at least one of the consumption and inventory of the medical supplies, based on at least one of the attribute information and the prescription information of patients in the predetermined area of the map data. The patient information generation system according to claim 1, characterized in that.

8. The generation unit acquires at least one of the vital data and activity data of the patient, and generates at least one of the prediction information and the warning information by using at least one of the vital data and the activity data. The patient information generation system according to any one of claims 4 to 7, characterized in that.

9. The generation unit generates at least one of the prediction information and the warning information by using data obtained by classifying the medical treatment date or the prescription date of the prescription drug into at least one of season, quarter, month, and day of the week. The patient information generation system according to any one of claims 4 to 7, characterized in that.

10. The processor The patient information generation system according to any one of claims 4 to 7, characterized in that it includes a notification unit that notifies at least one of the medical institution, the pharmacy, and the patient of at least one of the prediction information and the warning information.

11. The processor The patient information generation system according to claim 3, further comprising an inference unit that infers an increase or decrease in the number of persons based on at least one time-series data of the attribute information and the prescription information and a learned model learned by machine learning.

12. The processor The patient information generation system according to any one of claims 4 to 7, further comprising an inference unit that infers the prediction from a learned model learned by machine learning based on at least one time-series data of the attribute information and the prescription information.

13. A patient information generation method in which a processor generates display information for displaying patient information on map data, acquiring, as the attribute information of the patient, at least one of the patient's age, address, residence, postal code, contact method, contact destination, gender, height, weight, specific health examination information, health diagnosis information, interview information, and diagnosis information from at least one of the medical institution's medical record data and the pharmacy's receipt data, and acquiring, as prescription information, at least one of the patient's prescribed medicine, number of prescriptions of the prescribed medicine, efficacy, effect, usage, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information; generating display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position on the map data based on at least one of the address and the residence; A patient information generation method characterized by comprising:

14. A patient information generation program executed by a computer in which a processor generates display information for displaying patient information on map data, a patient information acquisition function for acquiring, as the attribute information of the patient, at least one of the patient's age, address, residence, postal code, contact method, contact destination, gender, height, weight, specific health examination information, health diagnosis information, interview information, and diagnosis information from at least one of the medical institution's medical record data and the pharmacy's receipt data, and acquiring, as prescription information, at least one of the patient's prescribed medicine, number of prescriptions of the prescribed medicine, efficacy, effect, usage, dosage, number of prescription days, number of prescription times, prescription type, prescription date, patient's symptoms, patient's disease name, medical treatment date, medical institution name, medical department name, medical department classification, pharmacy name, and medication guidance information; A generation function that generates display data for displaying at least one of the patient's attribute information and prescription information at a predetermined position in the map data based on at least one of the address and the place of residence; A patient information generation program, characterized by realizing the above.

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