Computer program, information processing device, information processing method, and medical assistance system

Abstract

Provided is a computer program, an information processing device, an information processing method, and a medical assistance system that assist patient pain management. The computer program causes a computer to execute processing for: acquiring medical examination data corresponding to the content of a medical examination for a patient and Activities of Daily Living (ADL) data relating to the daily life activities of the patient; and outputting state data indicating the state of the patient after the medical examination to the patient on the basis of the acquired medical examination data and ADL data.

Description

Computer program, information processing device, information processing method, and medical support system

[0001] The present invention relates to a computer program, an information processing device, an information processing method, and a medical support system for supporting pain management in patients.

[0002] Pain caused by diseases such as cancer directly affects a patient's quality of life (QOL), and therefore requires appropriate pain management. Even if the disease cannot be cured, maintaining the patient's QOL is essential. However, it is difficult for patients to accurately report pain to their doctor, and for the doctor to select an appropriate pain relief method and administer supporting medication, due to the time and skill requirements of both parties.

[0003] The need for patient pain management even during remote medical care is disclosed in, for example, Patent Document 1. Conventional systems require patients to input information accurately, but as with the above-mentioned self-reporting of pain to doctors, accurate input of facts is not always possible. Furthermore, to make an objective and accurate pain diagnosis, evaluations based on tests and other procedures covering a large number of items are required, and there is a shortage of specialists who can perform such evaluations.

[0004] Special table number 2004-502995

[0005] To improve or maintain a patient's QOL, medical professionals must not only listen carefully to what the doctor says during the medical interview, but also identify changes or abnormalities from information obtained about the patient's Activities of Daily Living (ADL) and determine whether medication has improved ADL.

[0006] The present invention has been made in view of the above circumstances, and has an object to provide a computer program, an information processing device, an information processing method, and a medical support system that support pain management for patients.

[0007] (1) The computer program of the present invention causes a computer to execute a process of acquiring medical data corresponding to the content of medical treatment for a patient and ADL data related to the patient's activities of daily living, and outputting condition data indicating the condition of the patient after the medical treatment for the patient based on the acquired medical data and ADL data.

[0008] (2) In the computer program of (1) above, when medical data corresponding to the contents of past medical treatment and ADL data after the treatment are input to the computer, the computer may be caused to execute a process of using a learning model that has been trained using medical treatment records for multiple patients, inputting the acquired medical data and ADL data into the learning model, and outputting the condition data obtained from the learning model, so as to output the condition data.

[0009] (3) In the computer program of (2) above, the condition data is data indicating whether treatment is required for the patient and the content of the treatment if treatment is required, and the learning model may be trained so that a reward is given the closer the evaluation value of the patient's condition is to a number indicating good after the treatment indicated by the condition data output from the learning model is performed or after no treatment is performed.

[0010] (4) In the computer program of any one of (1) to (3) above, the computer may be caused to execute a process of deriving an evaluation value of the patient's activities of daily living based on the acquired medical data and ADL data, and outputting the condition data based on the derived evaluation value.

[0011] (5) In the computer program of any one of (1) to (4) above, the ADL data may include at least one of answers to questions output on the patient's patient terminal device, detection results of the patient's daily living activities, and vital data of the patient.

[0012] (6) In the computer program of any one of (1) to (5) above, the computer may be caused to execute a process of transmitting screen data including text or images showing the condition of the patient after the medical treatment to a medical provider's medical equipment terminal device or the patient's patient terminal device.

[0013] (7) In the computer program of (6) above, the screen data may include a graph showing a time series change in the evaluation value of the patient's activities of daily living after the medical treatment.

[0014] (8) In the computer program of (6) or (7) above, the screen data may include at least one of medication information, examination information, subjective pain, and medical history of the patient related to medical treatment for the patient.

[0015] (9) In the computer program of any one of (1) to (8) above, the computer may be caused to execute a process of outputting control data for a medication device or prescription information based on condition data indicating the condition of the patient after the medical treatment for the patient.

[0016] (10) The information processing device of the present invention includes a processing unit that acquires medical data corresponding to the content of medical treatment for a patient and ADL data related to the patient's activities of daily living, and outputs status data indicating the patient's condition after the medical treatment for the patient based on the acquired medical data and ADL data.

[0017] (11) The information processing method according to the present invention includes a process of acquiring medical data corresponding to the content of medical treatment for a patient and ADL data relating to the patient's activities of daily living, and outputting condition data indicating the condition of the patient after the medical treatment for the patient based on the acquired medical data and ADL data.

[0018] (12) The medical support system of the present invention is a medical support system including a patient terminal device, a medical professional terminal device, and an information processing device capable of communicating with the patient terminal device and the medical professional terminal device, wherein the information processing device acquires medical data corresponding to the content of medical treatment for a patient from the medical professional terminal device, acquires ADL data related to the patient's activities of daily living from the patient terminal device, derives an evaluation value for evaluating the patient's activities of daily living based on the acquired medical data and ADL data, judges the patient's condition after the medical treatment based on the derived evaluation value, and outputs the judgment result to the patient terminal device or the medical professional terminal device.

[0019] According to the present disclosure, regarding the patient's pain after treatment, the condition of the patient after the treatment derived from the medical data and ADL data related to activities of daily living is output in a manner that can be understood by the patient and medical professional. This allows both the patient and medical professional to confirm the effectiveness of the treatment and prescription, and is expected to improve the satisfaction of both the patient and their family, and the medical professional.

[0020] 1 is a schematic diagram of a medical support system according to a first embodiment. FIG. 2 is a block diagram showing the configuration of a patient terminal device. FIG. 3 is a block diagram showing the configuration of an information processing device. FIG. 4 is a block diagram showing the configuration of a medical professional terminal device. FIG. 5 is a flowchart showing an example of a processing procedure by an information processing device. FIG. 6 shows an example of a correspondence relationship between evaluation items and scores for a patient's ADL. FIG. 7 is a diagram showing an example of an input screen based on an application program. FIG. 8 is a diagram showing an example of an output screen based on an application program. FIG. 9 is a diagram showing an example of an output screen based on an application program. FIG. 10 is a block diagram showing an example of an output screen based on a medical professional application program. FIG. 11 is a block diagram showing the configuration of an information processing device according to a second embodiment. FIG. 12 is a schematic diagram of a first learning model according to the second embodiment. FIG. 13 is a block diagram showing the configuration of an information processing device according to a third embodiment. FIG. 14 is a schematic diagram of a second learning model. FIG. 15 is a flowchart showing an example of a procedure for deriving an ADL evaluation value.

[0021] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present disclosure will be specifically described with reference to the accompanying drawings illustrating embodiments thereof. In the following embodiments, a medical support system to which the computer program, information processing device, and information processing method of the present disclosure are applied will be described.

[0022] 1 is a schematic diagram of a medical support system 300 according to a first embodiment. The medical support system 300 includes a patient terminal device 1 used by a patient, their caregiver, or their family member, a device 2 that measures data related to the patient's vital signs or ADL, an information processing device 3 that collects data from each patient's patient terminal device 1 and device 2, and a medical professional terminal device 4 used by a medical business operator. The medical support system 300 includes a network N that enables communication between the devices.

[0023] The patient terminal device 1 is installed at the patient's residence or carried by the patient. The patient terminal device 1 is not limited to being used by the patient himself / herself, but may also be used by the patient, his / her caregiver, or family member, as described above. The patient terminal device 1 is a smartphone, tablet terminal, personal computer, etc. The patient terminal device 1 is capable of communication via a network N. The device 2 is worn by the patient or installed at the patient's residence. The device 2 is a heart rate monitor, blood pressure monitor, camera, etc. The device 2 may be a wearable device or a stationary testing device equipped with a measuring device such as a heart rate monitor or camera and a communication unit. The device 2 is capable of communication via the network N via the patient terminal device 1 or directly.

[0024] The medical professional terminal device 4 is used by medical professionals such as doctors, nurses, pharmacists, and clinical laboratory technicians at medical institutions. The medical professional terminal device 4 is a personal computer, a tablet terminal, or the like. The medical professional terminal device 4 is connected to the network N via the local network of the medical institution. The medical institution may also have other testing devices connected to the local network.

[0025] The information processing device 3 is a device managed by a medical provider or a medical device manufacturer. The information processing device 3 can send and receive data between the patient terminal device 1 and the medical provider terminal device 4 via the network N. The information processing device 3 acquires data input into the patient terminal device 1 and data obtained from the device 2 via the network N, associating them with a patient ID that identifies the patient, and stores the data in a database 310 constructed in an internal or external storage device. The information processing device 3 acquires patient interview results, prescription information, etc. input into the medical provider terminal device 4 via the network N, associating them with the patient ID and medical provider ID via the network N, and stores the data in the database 310. The information processing device 3 acquires test results obtained from testing devices at medical institutions via the medical provider terminal device 4 or directly via the network N, associating them with the patient ID and medical provider ID of the subject, and stores the results in the database 310.

[0026] The network N includes a public communication network (Internet), a carrier network, and local networks in various locations. The network N may also include a dedicated line or an intranet between the patient terminal device 1, the information processing device 3, and the medical professional terminal device 4.

[0027] In the medical support system 300 of the first embodiment, the information processing device 3 derives status data indicating the patient's condition after consultation with a doctor at a medical institution, etc., where the patient has been prescribed medication or received treatment, using the information stored in the database 310 as described above. This data may include not only consultation but also interviews. The status data indicating the patient's condition after consultation may indicate whether the patient's progress is good or bad, or may indicate the treatment to be performed. The information processing device 3 outputs the status data indicating the patient's condition until the next consultation or interview to the patient terminal device 1 or the medical provider terminal device 4. The patient can determine whether to receive a medical interview from the doctor in charge by referring to the information indicated in the status data output from the patient terminal device 1. Based on the output from the medical provider terminal device 4, the medical provider can consider whether to continue the consultation, change the type or amount of medication, or change the administration method. Both patients and doctors can evaluate the results of medical treatment and prescriptions based on the condition of patients between visits to a medical institution, which can improve the accuracy of treatment, improve or maintain the patient's quality of life, and increase satisfaction.

[0028] The configuration and processing procedures of each device that realizes the medical support system 300 that feeds back to the medical provider and the patient status data indicating the patient's status until the next medical examination after medical examination or interview as described above will be described below.

[0029] 2 is a block diagram showing the configuration of the patient terminal device 1. The patient terminal device 1 includes a processing unit 10, a storage unit 11, a first communication unit 12, a second communication unit 13, a display unit 14, an operation unit 15, and a voice input / output unit 16.

[0030] The processing unit 10 is a processor such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), etc. The processing unit 10 executes input and output of information related to medical treatment for patients and data related to ADL based on an application program P1 stored in the storage unit 11.

[0031] The storage unit 11 uses a non-volatile memory such as a flash memory or an SSD (Solid State Drive). The storage unit 11 stores an application program P1 and data referenced by the processing unit 10. The application program P1 may be an application program P8 stored in the storage medium 8 that is read by the processing unit 10 and copied to the storage unit 11. The application program P1 may be downloaded from the information processing device 3, which is a server, or from another program server device via the first communication unit 12 and stored in an executable state.

[0032] The first communication unit 12 realizes communication with the information processing device 3 via the network N. Specifically, the first communication unit 12 is a wired network card, a carrier communication device, or a Wi-Fi wireless communication device. The processing unit 10 can transmit and receive data to and from the information processing device 3 via the first communication unit 12.

[0033] The second communication unit 13 establishes a communication connection with the device 2. Specifically, the second communication unit 13 is a USB interface, a wireless communication device for Wi-Fi, or a short-range wireless communication device such as Bluetooth (registered trademark). The processing unit 10 can acquire data measured by the device 2 via the second communication unit 13.

[0034] The display unit 14 uses a display device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display unit 14 is an interface that accepts operations. The operation unit 15 uses physical buttons, a keyboard, a pointing device, a touch panel device built into the display unit 14, etc. The operation unit 15 may accept operations on a screen displayed on the display unit 14 using physical buttons or a touch panel.

[0035] The audio input / output unit 16 uses a speaker and a microphone. The processing unit 10 can output audio from the speaker and accept audio input from the microphone. The processing unit 10 may use the audio input / output unit 16 as part of the operation by the operation unit 15. In other words, the processing unit 10 may recognize the operation content from the input audio via the microphone and accept the operation in an interactive format using the audio output from the speaker.

[0036] The patient terminal device 1 accepts input of vital data or data related to ADL via the operation unit 15 on the application screen displayed based on the application program P1. When the patient terminal device 1 starts the application program P1, it may accept the vital data or data related to ADL measured by the device 2 via the second communication unit 13. The patient terminal device 1 itself may include a measurement unit, and the patient terminal device 1 itself may be able to acquire the vital data or data related to ADL.

[0037] The vital data obtained from the device 2 or the patient terminal device 1 itself may be any of body temperature, heart rate, brain waves, EDA (electrodermal activity), etc. The vital data output from the device 2 or the patient terminal device 1 itself is not limited to these, and may include any vital data that is assumed to be related to pain caused by the patient's illness. The vital data obtained from the device 2 or the patient terminal device 1 itself may include, for example, blood pressure, pulse pressure, pulse wave, blood glucose level, weight, number of steps, activity level, etc.

[0038] Data related to ADL obtained from the device 2 or the patient terminal device 1 itself includes data input to an application screen based on the application program P1. The application screen displays questions about pain and acquires responses, allowing the processing unit 10 of the patient terminal device 1 to calculate a pain assessment index based on the application program P1. Pain assessment indexes include a visual analog scale (VAS), a numeric rating scale (NRS), a verbal rating scale (VRS), a face rating scale (FRS), subjective assessments by the patient themselves, such as the nature of pain and the degree of interference with daily life, or objective assessments by medical professionals. The VAS, NRS, VRS, and FRS are all indices that represent the intensity of pain. The VAS is a visual scale that indicates the current level of pain, allowing patients to check and respond to the scale. The NRS is a graded scale that divides pain into 11 levels from 0 to 10 and indicates the current level of pain. The VRS is a four-level pain scale. The FRS is a facial expression scale that indicates the intensity of pain based on facial expressions. The facial expression may be determined by the processing unit 10 based on image data obtained from a camera provided in the patient terminal device 1. The nature of the pain can be determined by identifying the pain and symptoms felt, and the patient is asked to answer the degree of pain for each type of pain, such as "throbbing pain," "sharp pain," "pain when touched," "tingling," and "itchy." The pain evaluation evaluates the extent to which the pain affects daily life, and the patient may be asked to answer, for example, whether it hurts even when still, whether it hurts when moving, whether it is possible to go out, whether the pain keeps the patient from sleeping, etc.

[0039] The processing unit 10 of the patient terminal device 1 also outputs several questions on the application screen, such as whether the patient was able to go out on their own, whether they were able to go out with assistance, whether they were able to bathe on their own, whether they were able to bathe with assistance, and whether they were able to fall asleep, and acquires answers. The data related to ADLs, in this case, the detection results of the patient's movements, may include still image data and video data that can confirm the patient's ADLs. Therefore, some of the devices 2 may be cameras. The processing unit 10 may install multiple devices 2 at the patient's residence, and based on images obtained from the devices 2, determine whether the patient has gotten up from bed or moved from bed through processing based on the application program P1 of the processing unit 10 of the patient terminal device 1. The ADL data may include activity records, such as the number of steps, obtained from location information obtained by a GPS receiver included in the patient terminal device 1 or an acceleration sensor included in the patient terminal device 1. The ADL data may also be detection results using radar using radio waves such as millimeter waves or an optical sensor such as infrared. It may also be the result of detecting whether or not the patient has risen from the bed using a radar or sensor provided on the bed.

[0040] The processing unit 10 may accept input from the patient or a caregiver on the application screen regarding the physical condition including the amount of activity, sleep time, drowsiness, fatigue, delirium, constipation, voice, facial expression, and the like.

[0041] The processing unit 10 of the patient terminal device 1 configured in this manner transmits data obtained from the device 2 or the patient terminal device 1 itself, and data input by operating the operation unit 15, to the information processing device 3. The processing unit 10 may process the data obtained from the device 2 or the patient terminal device 1 itself (such as deriving binary data by image recognition indicating whether the patient has woken up and moved from the bed, or whether they have been able to go out, etc.) before transmitting the data. When transmitting data to the information processing device 3, the processing unit 10 links the data to the patient ID stored in the storage unit 11 and transmits it. The device ID, type, product number, etc. of each device 2 may also be transmitted. The vital data or ADL-related data transmitted from the patient terminal device 1 is sequentially added to the database 310 of the information processing device 3.

[0042] 3 is a block diagram showing the configuration of the information processing device 3. The information processing device 3 includes a processing unit 30, a storage unit 31, a communication unit 32, and an input / output unit 33.

[0043] The processing unit 30 is a processor using a CPU, MPU, GPU, etc. The processing unit 30 controls each component using built-in RAM and ROM. The processing unit 30 reads out the information processing program P3 stored in the storage unit 31, executes processing on data related to ADL, etc. as described below, and transmits the processing results to the patient terminal device 1 or the medical professional terminal device 4.

[0044] The storage unit 31 uses a flash memory, an SSD, or a hard disk. The storage unit 31 stores the information processing program P3 and also stores data referenced by the processing unit 30. The information processing device 3 has a database 310 built in a recording device provided inside or outside the storage unit 31. The information processing program P3 stored in the storage unit 31 may be an information processing program P9 stored in a computer-readable storage medium 9 that has been read by the processing unit 30 and stored in the storage unit 31, or may be an information processing program P3 downloaded from a program distribution server.

[0045] The storage unit 31 stores the relationship between the contents of data used in the processing described below and scores for calculating the ADL evaluation value. For example, the storage unit 31 stores the relationship between vital data values ​​and scores, specifically, a relationship in which if the patient's weight is within a predetermined range corresponding to the patient's age, the score is set to +10. For example, the storage unit 31 may store the relationship between answers entered in the application program P1 of the patient terminal device 1 and scores. As a specific example, the storage unit 31 stores the relationship in which if the answer is that the patient was able to go out, the score for that day is set to +10. The storage unit 31 stores the relationship in which if the image data obtained from the device 2 indicates that the patient has gotten up from bed and been able to get out of bed, the score for that day is set to +2 to +8 (the longer the time spent out of bed, the larger the added value).

[0046] The communication unit 32 realizes communication with the patient terminal device 1 or the medical professional terminal device 4 via the network N. The processing unit 30 can transmit and receive data between a plurality of patient terminal devices 1 and medical professional terminal devices 4 via the network N using the communication unit 32.

[0047] The input / output unit 33 is an interface for connecting to an external storage device that stores the database 310. If the database 310 is provided in the storage unit 31, this is not necessary.

[0048] The database 310 includes an ADL table that stores data input by the application program P1 of the patient terminal device 1 in association with a patient ID that identifies the patient to be processed. Each piece of data is associated with time information when it was input. The database 310 also stores data directly obtained from the device 2 in the ADL table in association with a device ID (patient ID) and time information. The database 310 also includes a medical examination table that stores the results of medical examinations (interview results) that patients receive at medical institutions. The medical examination table includes, in association with the patient ID, the date and time of the examination and identification data that identifies the medical institution. The medical examination table may also include diagnosis results and prescription information.

[0049] The information processing device 3 configured in this way outputs condition data indicating the progress of a patient who has received medical treatment and been prescribed medicine, or whether further treatment is required, as will be described later, based on the data stored in the database 310. The processing procedure will be described later.

[0050] 4 is a block diagram showing the configuration of the medical professional terminal device 4. The medical professional terminal device 4 includes a processing unit 40, a storage unit 41, a communication unit 42, a display unit 43, an operation unit 44, and a voice input / output unit 45.

[0051] The processing unit 40 is a processor using a CPU, MPU, GPU, etc. The processing unit 40 controls each component using built-in RAM and ROM. The processing unit 40 reads out the medical professional application program P4 stored in the storage unit 41 and transmits data related to the doctor's medical treatment details and test results to the information processing device 3, as described below.

[0052] The storage unit 41 uses a flash memory, SSD, or hard disk. The storage unit 41 stores the medical professional application program P4 and also stores data referenced by the processing unit 40. The medical professional application program P4 stored in the storage unit 41 may be a medical professional application program P4 stored in a computer-readable storage medium 7 that is read by the processing unit 40 and stored in the storage unit 41, or may be a program downloaded from a program distribution server. The medical professional application program P4 may be a web application program that can execute the processing described below when a medical professional logs in to a web page of the medical support system 300 provided by the information processing device 3 using a medical professional account.

[0053] The communication unit 42 realizes communication with the information processing device 3 via the network N. Specifically, the communication unit 42 is a wired network card, a carrier communication device, or a Wi-Fi wireless communication device. The processing unit 40 can send and receive data to and from the information processing device 3 via the communication unit 42.

[0054] The display unit 43 uses a display device such as a liquid crystal display or an organic EL display. The display unit 43 is an interface that accepts operations. The operation unit 44 uses physical buttons, a keyboard, a pointing device, a touch panel device built into the display unit 43, etc. The operation unit 44 may accept operations on a screen displayed on the display unit 43 using physical buttons or a touch panel.

[0055] The audio input / output unit 45 uses a speaker and a microphone. The processing unit 40 can output audio from the speaker and receive audio input from the microphone. The processing unit 40 may use the audio input / output unit 45 as part of the operation by the operation unit 44. In other words, the processing unit 40 may recognize the operation content from the input audio via the microphone and receive the operation in an interactive format using the audio output from the speaker.

[0056] When the processing unit 40 of the medical professional terminal device 4 accepts input of medical treatment details from a medical provider such as a doctor via the medical professional application program P4, it transmits a notification of the medical treatment itself, associated with the patient ID of the patient being treated, along with the medical institution's identification data and the date of the treatment, to the information processing device 3. The processing unit 40 may transmit medical treatment data indicating the input medical treatment details to the information processing device 3. The medical treatment data indicating the medical treatment details may include pain index values ​​obtained by a doctor or nurse through a medical interview, as well as findings obtained through interviews, visual examination, palpation, percussion, etc. When data from an examination device is acquired, the processing unit 40 transmits the data to the information processing device 3 in association with the patient ID of the subject. The data obtained by the examination device may be the results of a blood test, a urine test, or an imaging test. Because the data related to the medical treatment transmitted to the information processing device 3 is personal information, it may be encrypted or data that could identify the medical treatment details itself may not be transmitted.

[0057] The processing unit 40 can use the medical professional application program P4 to specify a patient ID, send a request to output the progress after medical treatment to the information processing device 3, and view a screen for checking the progress. Processing regarding the progress after medical treatment will be described later with reference to the screen.

[0058] The processing performed by the medical support system 300 configured as described above will be described. As described above, the information processing device 3 updates the ADL table with the transmitted ADL-related data (answers based on the application program P1, image recognition results, vital data) each time an input operation is performed on the patient terminal device 1 or a measurement is performed on the device 2 and data is transmitted to the information processing device 3. The information processing device 3 updates the medical examination table with the interview results each time input is performed on the medical professional terminal device 4 and data is transmitted to the information processing device 3.

[0059] On the premise that a patient will visit the medical institution multiple times after receiving medical treatment, the information processing device 3 derives evaluation data based on the ADL table and medical treatment table, which are updated periodically regarding the effectiveness of medical treatment or successively whenever the database 310 is updated. Specific processing procedures will be described below.

[0060] 5 is a flowchart showing an example of a processing procedure by the information processing device 3. The information processing device 3 continuously executes the following processing based on the information processing program P3. After medical treatment is performed, the information processing device 3 executes the following processing for each patient.

[0061] The processing unit 30 of the information processing device 3 receives a notification from the medical professional terminal device 4 that a medical treatment has been performed (step S101). The processing unit 30 identifies the identification data of the medical institution where the medical professional terminal device 4 that sent the notification is installed, the date and time of the medical treatment, and the patient ID of the patient being treated (step S102). In step S102, the processing unit 30 may identify the diagnosis and prescription from data indicating the details of the medical treatment (diagnosis and prescription).

[0062] The processing unit 30 executes the following process for the patient ID identified in step S102. The processing unit 30 determines whether it is time to ask the patient a question (step S103). In step S103, the processing unit 30 may determine whether it is time to ask a question based on whether a predetermined time (a specific time every day, a specific time every week) has arrived. In step S103, the processing unit 30 may determine that it is time to ask a question when the application program P1 is launched and logged in based on the patient ID for the first time every predetermined period (one day, one week).

[0063] When the processing unit 30 determines that it is time to ask a question (S103: YES), it outputs the question associated with the data indicating the medical treatment content identified in step S102 to the patient terminal device 1 (step S104), and proceeds to step S105. When an answer to the output question is input in the patient terminal device 1, the processing unit 30 of the information processing device 3 adds the answer to the database 310 one by one.

[0064] If the processing unit 30 determines that it is not the timing to ask a question (S103: NO), the processing unit 30 proceeds to step S105.

[0065] The processing unit 30 determines whether the output timing has arrived (step S105). In step S105, the processing unit 30 may determine whether the output timing has arrived based on whether a predetermined time (one day, three days, etc.) has passed since the patient received medical treatment in step S101. In step S105, the processing unit 30 may determine that the output timing has arrived when an output request specifying the patient ID identified in step S102 is received from the medical professional terminal device 4. In step S105, the processing unit 30 may determine that the output timing has arrived when an output request specifying the patient ID is received from the patient terminal device 1. The processing unit 30 may determine whether the output timing has arrived once a day, once every three days, or a cycle set according to the patient's condition has arrived.

[0066] If the processing unit 30 determines that the output timing has not arrived (S105: NO), the processing unit 30 returns to step S103 and repeats the processing of steps S103-S105 until it determines that the output timing has arrived for the target patient.

[0067] When the processing unit 30 determines that the output timing has arrived (S105: YES), it uses the patient ID to read the patient's medical history along with the date and time of the medical treatment from the medical treatment table (step S106). In step S106, the processing unit 30 reads, for example, the contents of medications previously prescribed. If data indicating the medical treatment details is stored in step S106, the processing unit 30 reads this data. After each medical treatment, the processing unit 30 sequentially reads the vital signs and / or ADL data that are associated with the patient ID and transmitted and stored in the database 310 (step S107).

[0068] The processing unit 30 derives an ADL evaluation value based on the read medical history, vital data, and / or ADL-related data (step S108). Step S108 makes it possible to determine whether the patient's QOL has improved or is being maintained, as will be described later. The processing of step S108 may be performed by another device. The processing unit 30 may obtain an evaluation value derived by an external device.

[0069] In step S108, the processing unit 30 calculates a preset score for each answer for a predetermined period, such as daily or weekly, based on the correspondence stored in the storage unit 31, based on the answers to the questions entered via the patient terminal device 1. FIG. 6 shows an example of the correspondence between evaluation items and scores for the patient's ADL. In the correspondence shown in FIG. 6, the evaluation item "sitting up" results in a score of +1, "getting out of bed (within 5 minutes)" results in a score of +2, "getting out of bed (more than 5 minutes but less than 30 minutes)" results in a score of +4, "getting out of bed (more than 30 minutes)" results in a score of +8, and "going out" results in a score of +10. The correspondence shown in FIG. 6 also shows relationships for other vital data, such as a score of -2 for the evaluation item "blood pressure (change of 5% or more from the average for the previous week)," a score of -4 for "heart rate (change of 5% or more from the average)," and a score of -8 for "irregular pulse."

[0070] According to the correspondence relationship shown in FIG. 6 , in the process of step S108, if the application program P1 answers "YES" to the question "Were you able to sit up?", the processing unit 30 adds +1 to the score. In addition to the answer, if the processing unit 30 determines, based on image recognition of image data obtained from the device 2, that the patient's time out of bed exceeds 30 minutes, the processing unit 30 can add +8 to the score. Image recognition may be performed by either the patient terminal device 1 or the information processing device 3. In step S108, the processing unit 30 can also add -10 to the score if there is a record of a pacemaker being used as part of the medical history and if it is possible to recognize "arrhythmia" based on the heart rate data obtained from the device 2.

[0071] To calculate a value related to a specialized ADL evaluation such as the conventional Parcel Index, various items must all be input. In contrast, in this embodiment, the processing unit 30 narrows down the answers received by the application program P1 from items such as the Parcel Index and derives an ADL (activity level) evaluation value using vital data automatically obtained from the device 2, etc. For example, the Parcel Index includes many items, such as whether the subject was able to move their upper body, move their lower body, input data, urinate independently, defecate independently, and move independently, in addition to eating. Of these, the application program P1 narrows down the questions to questions such as "Were you able to bathe independently today?" and "Were you able to go out today?" as described below (see, e.g., FIG. 7 ). The processing unit 30 calculates a score using data obtained from the device 2 and can derive an evaluation value by combining the scores with the answers obtained by the application program P1.

[0072] Returning to the flowchart of FIG. 5 , the explanation will be continued. The processing unit 30 determines whether the magnitude of the evaluation value derived in step S108 or the change in the evaluation value satisfies the condition stored in the storage unit 31 (step S109). In step S109, the processing unit 30 determines whether the predetermined condition is satisfied based on the range in which the evaluation value (score) falls for each medical treatment content, based on the data indicating the medical treatment content identified in step S102. In step S109, the processing unit 30 may determine whether the condition is satisfied based on the magnitude of the evaluation value in multiple stages. In step S109, the processing unit 30 may determine whether the change in the evaluation value is equal to or greater than a predetermined rate over a predetermined period.

[0073] If the processing unit 30 determines that the condition is satisfied in step S109 (YES in step S109), it determines that the condition requires treatment (worsening) (step S110). In step S110, the processing unit 30 may simply determine whether treatment is necessary, or may also specify the type of treatment. In step S110, the processing unit 30 may determine that a medical examination (revisit) should be performed, or that the drug prescription should be changed, or may determine that the control data (speed, dosage, etc.) of the medication pump should be changed. When specifying the type of treatment, the processing unit 30 stores the determination result for each setting of the predetermined condition determined to be satisfied in step S109, and reads out the satisfied determination result in step S110.

[0074] The processing unit 30 notifies the patient terminal device 1 and the medical professional terminal device 4 of a message indicating the determination result of step S110 (step S111). For each setting of a predetermined condition determined to be satisfied in step S109, the processing unit 30 stores the contents of a message indicating the determination result according to the medical treatment content in the storage unit 31, and in step S111, reads out and notifies the message according to the satisfied condition.

[0075] The processing unit 30 stores the evaluation value derived in step S108 and the determination result in step S110 in the database 310 in association with the patient ID (step S112), and ends the process.

[0076] If the processing unit 30 determines in step S109 that the conditions are not met (S109: NO), it determines that no treatment is required (progress is good) (step S113). In step S113, the processing unit 10 determines, for example, to maintain the medication prescription and determine that hospital visits are not required until the regular checkup. The processing unit 30 notifies the patient terminal device 1 and the medical professional terminal device 4 of a message indicating the determination result of step S113 (S111). The processing unit 30 stores the determination result of step S113 in the database 310 in association with the patient ID (S112), and ends the process.

[0077] By executing the flowchart in Figure 5 according to the output timing, data showing the progress after treatment can be fed back to the medical provider or the patient based on an inquiry (output request) from the medical provider or based on an inquiry (output request) from the patient, even if no abnormalities are found.

[0078] FIG. 7 is a diagram illustrating an example of an input screen 150 based on the application program P1. The input screen 150 illustrated in FIG. 7 displays a question. For example, the question may be a question about whether or not the patient's daily life is being affected. In the example of the input screen 150 illustrated in FIG. 7, a question message 151 asking, "Were you able to bathe by yourself today?" and an input field 152 are displayed. The question message 151 displayed on the input screen 150 is limited to questions pre-associated with medical treatment details from the information processing device 3. The database 310 of the information processing device 3 stores correspondences between data identifying medical treatment details and medical conditions and questions, and the processing unit 30 may refer to these correspondences. Different questions may be stored for different numbers of hospital visits and medical history. Instead of the correspondences in the database 310, a learning model may be used that is trained to output questions to be asked to the patient when medical treatment details, medical conditions, medical history, etc. are input.

[0079] 7, the input field 152 of the input screen 150 may be a radio button, a text box that allows input in natural language, a pull-down list of options, or a check box. The input screen 150 includes a "Send" button 153. When the patient or caregiver selects the "Send" button 153 on the input screen 150, the processing unit 10 of the patient terminal device 1 accepts this, associates the patient ID with the date and time, and transmits them to the information processing device 3.

[0080] By simply answering the narrowed-down questions on the patient terminal device 1 as shown in FIG. 7, an evaluation value regarding ADL is derived in the information processing device 3, and an appropriate judgment is made accordingly.

[0081] 8 and 9 are diagrams showing examples of output screen 154 based on application program P1. Output screen 154 shown in Fig. 8 shows an example of content displayed in response to the notification in step S111 when it is determined in step S109 that the predetermined conditions are met and that action is required (S110) in the processing procedure shown in the flowchart of Fig. 5. Output screen 154 shown in Fig. 9 shows an example of content displayed in response to the notification in step S111 when it is determined in step S109 that the predetermined conditions are not met and that action is not required (S113).

[0082] A message 155 indicating the result of the determination is displayed on the output screen 154. In the output screen 154 of FIG. 8, a message 155 is displayed indicating that the pain has not been alleviated, that the prescription should be changed, and that a follow-up visit is recommended. The output screen 154 also includes a "Confirm" button 156. When the "Confirm" button 156 is selected, the output screen 154 is closed. In the case of the message 155 that recommends a follow-up visit, when the "Confirm" button 156 is selected, the process may proceed to the appointment procedure with the corresponding medical institution based on the application program P1 of the patient terminal device 1.

[0083] A message 155 indicating the result of the judgment is also displayed on the output screen 154 in Fig. 9. The output screen 154 in Fig. 9 displays a message 155 indicating that the progress is favorable due to the prescription based on the medical examination and instructions from the doctor, and that the treatment should be continued.

[0084] 10 is a diagram showing an example of an output screen 430 based on the medical professional application program P4. The output screen 430 includes information identifying the patient (e.g., the patient's name) and a graph 431 showing the progress of the evaluation score based on data related to ADL since the previous medical examination. The output screen 430 displays a message 432 indicating that the medication prescription should be changed. The output screen 430 also includes a "Confirm" button 433. When the "Confirm" button 433 is selected, the output screen 430 is closed. When the "Confirm" button 433 is selected, the user may be able to check the details of the corresponding patient's medical history or the patient's appointment status.

[0085] In this way, the medical support system 300 of this embodiment judges the progress after medical treatment from the perspective of ADL, and allows the doctor to recognize the patient's condition between medical treatments or interviews, rather than simply diagnosing the patient's condition at the time of the doctor's interview or examination. Furthermore, the medical support system 300 is not limited to detecting and notifying a patient when a follow-up visit is necessary, but can also output an evaluation based on vital data and ADL data, even if the progress is good, and use this information in subsequent medical treatments. Both patients and medical providers can confirm the effectiveness of medical treatment and prescriptions, which is expected to improve the satisfaction of both patients, their families, and medical providers.

[0086] Second Embodiment In the first embodiment, the information processing device 3 judged the condition of the patient after medical treatment based on whether the evaluation value related to ADL satisfied a predetermined condition (S109, S110, S113). In the second embodiment, the processing unit 30 of the information processing device 3 uses a learning model that is trained to output a result specifying what to do next when the evaluation value, conditions, etc. are input. In this case, the evaluation value is handled as not only a specific index value but also multidimensional index values ​​such as "symptom," "prescription information," "ADL index," and "pain index value," enabling more detailed output.

[0087] The configuration of the medical support system 300 of the second embodiment is the same as that of the medical support system 300 of the first embodiment, except that a learning model is used to determine the state of a patient after medical treatment. Therefore, among the configurations of the medical support system 300 of the second embodiment, the configurations common to the medical support system 300 of the first embodiment are denoted by the same reference numerals and detailed descriptions thereof will be omitted.

[0088] FIG. 11 is a block diagram showing the configuration of an information processing device 3 according to the second embodiment. The information processing device 3 according to the second embodiment stores a first learning model M31 in the storage unit 31. The first learning model M31 is trained in advance based on training data in which a condition assessment result determined by a medical provider, such as a doctor or a specialized laboratory technician, based on past vital signs and / or ADL-related values, evaluation values, and medical treatment details is taken as the correct answer. Like the information processing program P3, the first learning model M31 may be a first learning model M91 stored in the storage medium 9, which the processing unit 30 copies and stores in the storage unit 31. The first learning model M31 may be continuously re-learned by the processing unit 30 of the information processing device 3 through reinforcement learning, which sequentially provides a reward when the condition improves and does not provide a reward when the condition worsens, based on the ADL evaluation value.

[0089] 12 is a schematic diagram of a first learning model M31 according to the second embodiment. The first learning model M31 is trained to output data indicating a judgment result regarding the patient's progress (whether to change the prescription or provide medical care) when data indicating the details of medical care for the patient and an ADL evaluation value derived from vital data and / or ADL-related values ​​are input. The first learning model M31 is trained using, for example, a support vector machine (SVM), a decision tree, a random forest, Adaboost, a neural network, or the like.

[0090] The first learning model M31 includes an input layer 311 that receives data indicating the details of medical treatment for a patient and an ADL evaluation value derived from vital data and / or ADL-related values. The data indicating the details of medical treatment is, for example, a data string of medical conditions in which data corresponding to the diagnosed medical condition is assigned a value of 1 and other data is assigned a value of 0 (zero). The ADL evaluation value may be a single value indicating a score, or it may be vector data such as the good or bad status of the "medical condition," the product number of the drug in the "prescription information," whether or not the patient has taken the medication, the score of the "ADL index," and a "pain index value" obtained from the patient's subjective responses to questions. For example, the "pain index value" may be scored high if the pain is so severe that the patient cannot move, or may be scored based on the patient's responses such as whether the pain is so severe that the patient cannot sleep. The "pain index value" may be a score determined by facial expressions determined from images obtained by the device 2. The "pain index value" may also be a score obtained from other testing devices.

[0091] The first learning model M31 includes an output layer 312 that outputs a condition assessment result indicating the patient's condition after medical treatment. The output layer 312 is a data sequence corresponding to the need for treatment and the content of treatment if treatment is required, such as {"good progress," "reexamination required (no improvement)," "reexamination required (no improvement) and medication prescription change," "prescription change required (no improvement)," ...}. When the data sequence is set as described above, if the output layer 312 outputs {1, 0, 0, 0, ...}, the processing unit 30 determines that the patient is "good progress," and if the processing unit 30 outputs {0, 1, 0, 0, ...}, the processing unit 30 determines that the patient is "reexamination required" (output indicated by the bold line in FIG. 12 ). The condition assessment result may include the product number and quantity of the medication to be prescribed, or a specific example of a pump-based administration method.

[0092] The first learning model M31 includes an intermediate layer 313 having parameters trained so that the condition assessment result output from the output layer 312 matches the assessment result by the medical provider. When relearning, reinforcement learning may be performed so that the closer the ADL evaluation value when the condition assessment result is good and no treatment is performed, or the ADL evaluation value when the condition assessment result is that a re-examination is required and treatment (medical care) is performed, the more likely the output result of the need for previous treatment and the content of the treatment is judged to be correct. The intermediate layer 313 preferably includes multiple nodes and multiple layers.

[0093] In the second embodiment, in the determination of step S110 and step S111 or S109 of the processing procedure shown in the flowchart of Fig. 5, the processing unit 30 inputs data indicating the medical treatment content identified in step S102 and the ADL evaluation value derived in step S108 to the first learning model M31. The processing unit 30 determines the result from the condition determination result output from the first learning model M31, and notifies a message (S111) or stores the result (S112).

[0094] (Third Embodiment) In the first embodiment, the information processing device 3 derives the evaluation value related to ADL based on the read medical history and vital data and / or data related to ADL by adding the score from the correspondence between the data and the score (S108). In the third embodiment, the processing unit 30 of the information processing device 3 uses the second learning model M32 that has been trained to output an evaluation value when the read medical history and vital data and / or data related to ADL are input.

[0095] 13 is a block diagram showing the configuration of an information processing device 3 according to the third embodiment. The information processing device 3 according to the third embodiment stores a second learning model M32 in the storage unit 31. The second learning model M32 is trained in advance based on training data in which an ADL evaluation value derived from parameters including past vital data and / or ADL-related data is used as an answer. The second learning model M32 may be a second learning model M92 stored in the storage medium 9 that the processing unit 30 copies to the storage unit 31, similar to the information processing program P3.

[0096] FIG. 14 is a schematic diagram of the second learning model M32. The second learning model M32 is a model trained to output an evaluation value estimated from available data, even when the data necessary to derive a value related to a specialized ADL evaluation, such as a parcel index, is insufficient. The second learning model M32 includes an input layer 321 that inputs vital data and / or ADL-related data obtainable by the patient terminal device 1 or device 2 of the medical support system 300. The second learning model M32 includes an output layer 322 that outputs an ADL evaluation value used in the specialized field of ADL. The output layer 322 may output a multidimensional data string or a single score.

[0097] The second learning model M32 includes an intermediate layer 323 having parameters trained using past data of a large number of ADL-related parameters and vital data, and ADL evaluation value scores calculated from the parameters and vital data as training data. The intermediate layer 323 is trained using the past training data so that when vital data and / or ADL-related data obtainable by the patient terminal device 1 or the device 2, among the ADL-related parameters and vital data used to calculate the ADL evaluation value scores, are input to the input layer 321, the difference between the ADL evaluation value output from the output layer 322 and the ADL evaluation value calculated from all parameters including the input data is reduced. The intermediate layer 323 preferably includes multiple nodes and multiple layers.

[0098] To calculate the ADL evaluation value used in the specialized field of ADL evaluation, it is necessary to input, for example, 20 or more items, such as whether the patient was able to eat by themselves, whether they were able to eat with assistance, how their upper body moved, and how their lower body moved. With the second learning model M32, although it is difficult to obtain results on whether or not the patient was able to eat by themselves or with assistance for 20 items, if data on four items, such as time spent getting out of bed, bathing level (self-help: 10, assistance: 5, not possible: 0), eating level (self-help: 10, assistance: 5, not possible: 0), and medical condition (heart disease, etc.), can be obtained, and when data on these four items is input, it is possible to derive the ADL evaluation value by supplementing the other missing items with other vital data (age, blood pressure, heart rate).

[0099] Using the second learning model M32 shown in Fig. 14, the processing unit 30 of the information processing device 3 derives step S108 of the processing procedure shown in the flowchart of Fig. 5 by utilizing the second learning model M32. Fig. 15 is a flowchart showing an example of a procedure for deriving an ADL evaluation value. The processing procedure shown in the flowchart of Fig. 15 corresponds to details of step S108.

[0100] The processing unit 30 creates data for items to be input to the second learning model M32 from the read medical history, vital signs, and / or ADL-related data (step S801). The processing unit 30 inputs the created data to the second learning model M32 (step S802). The processing unit 30 acquires the ADL evaluation value output from the second learning model M32 (step S803), and proceeds to step S109 in the flowchart of FIG. 5.

[0101] This makes it possible to derive an ADL evaluation value with fewer input items and more accurately determine the patient's condition based on ADL after medical treatment.

[0102] The embodiments disclosed above are illustrative in all respects and are not restrictive. The scope of the present invention is defined by the claims, and includes all modifications within the meaning and scope of the claims.

[0103] DESCRIPTION OF SYMBOLS 1 Patient terminal device 10 Processing unit 11 Storage unit P1 Application program 2 Device 3 Information processing device 30 Processing unit 31 Storage unit 310 Database P3 Information processing program (computer program) M31 First learning model M32 Second learning model 4 Medical professional terminal device 43 Display unit

Claims

1. A computer program that causes a computer to execute a process of acquiring medical data corresponding to the contents of medical treatment for a patient and ADL (Activities of Daily Living) data relating to the patient's activities of daily living, and outputting condition data indicating the condition of the patient after the medical treatment based on the acquired medical data and ADL data.

2. The computer program according to claim 1, which causes the computer to execute the following process: use a learning model trained using medical records of multiple patients so as to output the condition data when medical data corresponding to the contents of past medical treatment and ADL data after the treatment are input; input the acquired medical data and ADL data into the learning model; and output the condition data obtained from the learning model.

3. The computer program of claim 2, wherein the condition data indicates whether or not the patient requires treatment and, if treatment is required, the content of the treatment, and the learning model is trained so that a reward is given more the closer the evaluation value of the patient's condition is to a numerical value indicating good after the treatment indicated by the condition data output from the learning model is performed or after no treatment is performed.

4. A computer program according to any one of claims 1 to 3, which causes the computer to execute a process of deriving an evaluation value of the patient's activities of daily living based on the acquired medical data and ADL data, and outputting the condition data based on the derived evaluation value.

5. A computer program according to any one of claims 1 to 4, wherein the ADL data includes at least one of answers to questions output on a patient terminal device of the patient, detection results of the patient's activities of daily living, and vital data of the patient.

6. A computer program as claimed in any one of claims 1 to 5, which causes the computer to execute a process of transmitting screen data including text or images showing the condition of the patient after the medical treatment to a medical provider's medical terminal device or a patient terminal device of the patient.

7. The computer program according to claim 6, wherein the screen data includes a graph showing a time series change in the evaluation value of the patient's activities of daily living after the medical treatment.

8. The computer program according to claim 6 or 7, wherein the screen data includes at least one of medication information, examination information, subjective pain, and medical history of the patient, related to medical treatment for the patient.

9. A computer program according to any one of claims 1 to 8, which causes the computer to execute a process of: outputting control data for a medication device or prescription information based on status data indicating the condition of the patient after the medical treatment for the patient.

10. An information processing device having a processing unit that acquires medical data corresponding to the contents of medical treatment for a patient and ADL data related to the patient's activities of daily living, and outputs status data indicating the condition of the patient after the medical treatment for the patient based on the acquired medical data and ADL data.

11. An information processing method including a process of acquiring medical data corresponding to the contents of medical treatment for a patient and ADL data relating to the activities of daily living of said patient, and outputting status data indicating the status of said patient after said medical treatment based on said acquired medical data and ADL data.

12. A medical support system including a patient terminal device, a medical professional terminal device, and an information processing device capable of communicating with the patient terminal device and the medical professional terminal device, wherein the information processing device: acquires medical data corresponding to the content of medical treatment for a patient from the medical professional terminal device; acquires ADL data relating to the patient's activities of daily living from the patient terminal device; derives an evaluation value for evaluating the patient's activities of daily living based on the acquired medical data and ADL data; judges the patient's condition after the medical treatment based on the derived evaluation value; and outputs the judgment result to the patient terminal device or the medical professional terminal device.

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