Medical information processing device, medical information processing system, and medical information processing method

Abstract

To properly display measurement results taken in different environments. [Solution] The medical information processing device according to the embodiment includes: an acquisition unit that acquires first measurement information showing a plurality of first data relating to a predetermined item measured under a first condition for a subject, and second measurement information showing a plurality of second data relating to a predetermined item measured under a second condition different from the first condition for a subject; a conversion unit that associates at least one of the plurality of first data with the plurality of second data, and converts the first data to be converted from the plurality of first data into third measurement information showing third data relating to a predetermined item based on a third condition obtained from the association; and a display control unit that displays the third measurement information.

Description

Technical Field

[0001] Embodiments of the present invention relate to a medical information processing apparatus, a medical information processing system, and a medical information processing method.

Background Art

[0002] Conventionally, in the medical field, in order to support a doctor's diagnosis and consideration of a treatment policy, measurement values of a patient acquired at multiple time points are displayed in chronological order. For example, there is a system in which data such as measurement values measured by a patient at home is transmitted to an information system possessed by a medical institution and can be remotely shared with medical staff. Thereby, medical staff can confirm the measurement values measured by the patient at home in combination with in-hospital vital signs, test data, etc. already registered in the information system.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, data measured in the hospital is measured by medical staff, and data measured at home is measured by patients or their families. Errors derived from the measuring device or the measurement environment, such as the management state of the measuring device, the measurement method, the measurement timing, and the measurement environment, may occur in the data measured at home. Therefore, if medical staff confirm the home data on the same basis as the in-hospital data, they may misrecognize the trend of the home data.

[0005] The problem that the present invention aims to solve is to provide a medical information processing system that can improve the convenience of displaying measurement results measured at multiple medical institutions. One of the problems that the embodiments disclosed herein aim to solve is to appropriately display measurement results measured in different environments. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] The medical information processing device according to this embodiment includes: an acquisition unit that acquires first measurement information showing a plurality of first data regarding a predetermined item measured under a first condition for a subject, and second measurement information showing a plurality of second data regarding a predetermined item measured under a second condition different from the first condition for a subject; a conversion unit that associates at least one of the plurality of first data with the plurality of second data, and converts the first data to be converted from the plurality of first data into third measurement information showing third data regarding a predetermined item based on a third condition obtained from the association; and a display control unit that displays the third measurement information. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of the configuration of a medical information processing system according to an embodiment. [Figure 2] Figure 2 shows an example of the configuration of an information provision server according to the embodiment. [Figure 3] Figure 3 is a table showing an example of the first measurement information according to the embodiment. [Figure 4] Figure 4 is a graph showing an example of the first measurement information and the second measurement information according to the embodiment. [Figure 5] Figure 5 is a graph illustrating the third measurement information according to this embodiment. [Figure 6] Figure 6 is a schematic diagram showing an example of the display of the third measurement information according to the embodiment. [Figure 7] Figure 7 is a flowchart showing an example of information provision processing performed by the information provision server according to this embodiment. [Figure 8] Figure 8 shows an example of the configuration of the information provision server related to the first modified example. [Figure 9] Figure 9 is a schematic diagram showing an example of the display of confidence information related to the first modified example. [Figure 10] Figure 10 is a flowchart showing an example of the information provision process performed by the information provision server related to the first modification. [Figure 11] Figure 11 shows an example of the configuration of the information provision server related to the second modified example. [Figure 12] Figure 12 is a schematic diagram showing an example of how recommended information related to the second modified example is displayed. [Figure 13] Figure 13 is a table showing an example of the cost of remeasurement related to the second modified example. [Figure 14] Figure 14 is a flowchart showing an example of the information provision process performed by the information provision server related to the second modified example. [Figure 15] Figure 15 is a schematic diagram showing an example of a data correspondence specification table related to the fifth modified example. [Figure 16] Figure 16 is a schematic diagram showing an example of a data correspondence relationship specification table related to the sixth modified example. [Modes for carrying out the invention]

[0008] The following describes an embodiment of the medical information processing system with reference to the drawings.

[0009] Figure 1 shows an example configuration of the medical information processing system 1 according to this embodiment. As shown in Figure 1, the medical information processing system 1 according to this embodiment includes a medical institution system 10, a second terminal device 20, and an information provision server 30, which are connected to each other via a network N1. The number of medical institution systems 10 connected to the network N1 is not particularly limited.

[0010] The medical institution system 10 is a system provided by each of medical institutions such as hospitals and examination institutions (hereinafter collectively referred to as medical institutions). A hospital (medical institution system 10) is, for example, in a relationship of regional medical cooperation, and it is possible to mutually refer to the medical data of a subject such as a patient.

[0011] Each of the medical institution systems 10 has, for example, a specimen examination system 11, an image diagnosis system 12, an electronic medical record system 13, a first terminal device 14, etc. The specimen examination system 11, the image diagnosis system 12, the electronic medical record system 13, and the first terminal device 14 are communicably connected via an in-hospital network N2. Also, the in-hospital network N2 is connected to the network N1 via network devices such as a router.

[0012] The specimen examination system 11 generates medical data regarding the specimen examination performed on a subject and stores it in a storage circuit within the system. For example, the specimen examination system 11 generates medical data including examination values (numerical data) indicating the results of hematological examinations and biochemical examinations performed on a subject. Also, the specimen examination system 11 provides the medical data stored in the storage circuit to the information providing server 30.

[0013] The image diagnosis system 12 generates medical data regarding the image diagnosis performed on a subject and stores it in a storage circuit within the system. Here, image diagnosis includes examinations using CT images taken by an X-ray CT (Computed Tomography) device, examinations using MR images taken by an MRI (Magnetic Resonance Imaging) device, examinations using ultrasonic images taken by an ultrasonic diagnostic device, examinations using X-ray images taken by an X-ray diagnostic device, etc. For example, the image diagnosis system 12 generates medical data including examination values (numerical data) such as blood vessel diameter and blood flow volume obtained by image diagnosis. Also, the image diagnosis system 12 provides the medical data stored in the storage circuit to the information providing server 30.

[0014] The electronic medical record system 13 generates medical data related to prescriptions and nursing records performed on the patient and stores it in a memory circuit within the system. For example, the electronic medical record system 13 generates medical data that includes test values ​​(numerical data) such as the patient's vital signs (pulse, heart rate, respiratory rate, blood pressure, body temperature, etc.). The electronic medical record system 13 also provides the medical data stored in the memory circuit to the information provision server 30.

[0015] The medical data generated by the aforementioned specimen testing system 11, imaging diagnostic system 12, and electronic medical record system 13 includes the following information in addition to the test values ​​corresponding to the measured values. For example, the medical data includes information such as the date and time the test was performed (measurement date and time), the type of test (test type), the unit of the test value, and an abnormality flag indicating whether it is normal or abnormal.

[0016] Furthermore, the medical data includes a patient ID that can uniquely identify the subject. The patient ID may be common to all medical institutions, or it may be different for each medical institution. However, in the latter case, the system should be configured so that the subject of the same person can be identified from the patient ID of each medical institution, for example, by linking the patient IDs of each medical institution relating to the same person.

[0017] Furthermore, the medical data includes information that indicates the environment in which the subject's test was conducted (testing environment), which allows for the unique identification of the medical institution where the test was performed.

[0018] Furthermore, the information included in the medical data is not limited to the examples described above. For example, the medical data may include information about the subject, such as the subject's gender, age, BMI (Body Mass Index), and whether or not they smoke or drink. The medical data may also include information about the testing environment, such as information that identifies the equipment used for the test and information that identifies the reagents used for the test. The medical data may also include the date and time of sample collection.

[0019] The first terminal device 14 is a computer device such as a PC (Personal Computer) or tablet terminal used by medical professionals such as doctors, nurses, and laboratory technicians. As shown in Figure 1, the first terminal device 14 has an input circuit 141 and a display 142. The input circuit 141 receives input operations from the operator. For example, the input circuit 141 can be implemented by a trackball, switch buttons, mouse, keyboard, touch panel, etc. The display 142 displays various types of data. For example, the display 142 can be implemented by an LCD monitor, CRT (Cathode Ray Tube) monitor, touch panel, etc. The input circuit 141 and the display 142 may be integrated. For example, the input circuit 141 and the display 142 may be configured as a touch panel.

[0020] Medical professionals can access the systems within their own medical institutions via the first terminal device 14. They can also access the information provision server 30 via the first terminal device 14. When accessing the information provision server 30, the medical institution ID of the medical institution where the first terminal device 14 is installed, and information identifying the user (medical professional) operating the first terminal device 14, are transmitted to the information provision server 30.

[0021] The second terminal device 20 is a computer device such as a PC, tablet, or smartphone used by the patient. As shown in Figure 1, the second terminal device 20 has an input circuit 211 and a display 212. The input circuit 211 receives input operations from the patient. For example, the input circuit 211 can be implemented by a trackball, switch buttons, mouse, keyboard, touch panel, etc. The display 212 displays various types of data. For example, the display 212 can be implemented by an LCD monitor, CRT (Cathode Ray Tube) monitor, touch panel, etc. The input circuit 211 and the display 212 may be integrated. For example, the input circuit 211 and the display 212 may be configured as a touch panel.

[0022] The patient can transmit data such as measurement values ​​measured at home via the second terminal device 20. This data is also referred to as the first measurement information. The first measurement information includes, for example, data on vital signs measured at home using measuring equipment, the patient's patient ID, etc. When transmitting the first measurement information to the information provision server 30, identifiable information, such as the patient ID of the patient operating the second terminal device 20, is transmitted to the information provision server 30.

[0023] The information provision server 30 acquires various medical data from each of the medical institution systems 10 via the network N1 and performs various information processing using the acquired medical data. For example, the information provision server 30 can be implemented using computer equipment such as a server device or a workstation. The information provision server 30 is an example of a medical information processing device. Note that the information provision server 30 is not limited to a single device, but may also be implemented through the collaboration of multiple network-connected computer devices (e.g., cloud computing).

[0024] Figure 2 shows an example of the configuration of the information provision server 30. As shown in Figure 2, the information provision server 30 has a communication interface 310, a storage circuit 320, and a processing circuit 330.

[0025] The communication interface 310 is connected to the processing circuit 330 and controls the transmission and communication of various data between the information provision server 30, the medical institution system 10, and the second terminal device 20. Specifically, the communication interface 210 receives medical data from the medical institution system 10 and outputs the received medical data to the processing circuit 230. The communication interface 210 also receives first measurement information from the second terminal device 20 and outputs the received first measurement information to the processing circuit 230. The communication interface 210 can be implemented, for example, by a network card, network adapter, or NIC (Network Interface Controller).

[0026] The memory circuit 320 is connected to the processing circuit 330 and stores various types of data. Specifically, the memory circuit 320 stores various programs executed by each of the processing circuits 30, as well as various setting information related to the operation of the information provision server 30. The memory circuit 320 also holds a home data DB 321 for storing first measurement information received from the second terminal device 20, which corresponds to the memory unit of this embodiment, and an in-hospital data DB 322 for storing medical treatment data received from each medical institution system 10.

[0027] The memory circuit 320 can be implemented using, for example, semiconductor memory elements such as RAM (Random Access Memory) or flash memory, or a hard disk or optical disc. Note that the home data DB 321 and the in-hospital data DB 322 are not limited to being stored in the memory circuit 320; they may also be stored in an external device accessible by the information provision server 30.

[0028] The processing circuit 330 comprehensively controls the operation of the information provision server 30. The processing circuit 330 is implemented by a processor such as a CPU (Central Processing Unit). The processing circuit 330 includes a receiving function 331, a collection function 332, an acquisition function 333, a conversion function 334, and a display control function 335.

[0029] The receiving function 331 receives the first measurement information transmitted by the second terminal device 20. The receiving function 331 is an example of a receiving unit. The receiving function 331 stores the received first measurement information in the home data DB 321 of the storage circuit 320.

[0030] Here, we will explain the home data DB321. The home data stored in the home data DB321 contains first measurement information measured on the subject under first conditions. First conditions are methods of measurement performed by non-medical personnel such as the patient or their family. First measurement information is, for example, measurement data measured by the patient themselves using measuring equipment at home, a nursing home, or a care facility for the elderly. Home data also includes measurement items measured by the patient independently at home. For example, home data includes measurement data when the patient takes measurements in the hospital for practice during hospitalization, or when the patient measures their blood pressure in the waiting room before an outpatient consultation. Hereafter, first measurement information will be described as measurement data related to the patient's vital signs, but is not limited to this.

[0031] Figure 3 is a table showing an example of first measurement information according to the embodiment. Table T1 shown in Figure 3 shows, for example, items related to blood pressure and pulse rate measured by the patient. As shown in Figure 3, the first measurement information is information associated with the date and time of measurement by the patient, the systolic blood pressure, diastolic blood pressure, and pulse rate at the time of measurement.

[0032] Returning to Figure 2, the collection function 332 collects medical data from each of the medical institution systems 10. The collection function 332 is an example of a collection unit. Specifically, the collection function 332 collects medical data provided by the specimen testing system 11, the diagnostic imaging system 12, and the electronic medical record system 13 of the medical institution system 10. The collection function 332 then stores the collected medical data in the in-hospital data DB 322 of the memory circuit 320. The medical data is an example of second measurement information.

[0033] Here, we will explain the in-hospital data DB322. The in-hospital data stored in the in-hospital data DB322 contains second measurement information measured under a second condition different from the first condition for the subject. The second condition is the method of measurement performed by a healthcare professional. The second measurement information is, for example, data on vital signs measured by a healthcare professional using measuring equipment at a medical facility. The second measurement information also includes data on vital signs measured by the patient themselves using measuring equipment under the instruction of a healthcare professional at the medical facility. In addition, the in-hospital data includes measurement data taken at home by visiting nurses.

[0034] The acquisition function 333 acquires first measurement information and second measurement information. The acquisition function 333 is an example of an acquisition unit. Specifically, the acquisition function 333 acquires first measurement information from the home data DB 321 stored in the memory circuit 320, which shows multiple first data points for a predetermined item measured under first conditions for the subject, and second measurement information from the in-hospital data DB 322 stored in the memory circuit 320, which shows multiple second data points for a predetermined item measured under second conditions different from the first conditions for the subject. For example, the acquisition function 333 identifies the subject from the patient ID included in the first measurement information stored in the home data DB 321, and acquires second measurement information associated with the identified patient ID from the in-hospital data DB 322 stored in the memory circuit 320.

[0035] Furthermore, the acquisition function 333 acquires the first data and the second data along with their measurement timings. Specifically, the acquisition function 333 acquires the first data and the second data along with their measurement timings for the measurement date and time of the first data and the measurement timings for the measurement date and time of the second data.

[0036] Here, the first measurement information and the second measurement information acquired by the acquisition function 333 will be explained using Figure 4. Figure 4 is a graph showing an example of the first measurement information and the second measurement information according to the embodiment. In Figure 4, the horizontal axis represents the date and time, and the vertical axis represents the systolic blood pressure [mmHG]. Figure 4 shows the state in which the patient is regularly receiving outpatient treatment at a hospital, or the state in which the patient is hospitalized. Graph G1 in Figure 4 is a graph that displays multiple first data included in the first measurement information arranged in chronological order. Also, graph G2 in Figure 4 is a graph that displays multiple second data included in the second measurement information arranged in chronological order.

[0037] For example, patients regularly receive outpatient treatment at the hospital, and tests are performed in-hospital at those times (see graph G2 in Figure 4). In addition, patients take measurements at home using measuring devices during that period, so the frequency of home-based data increases (see graph G1 in Figure 4). For example, if a patient's condition worsens and they are hospitalized, home-based data measurement ceases, and the frequency of in-hospital data measurement increases (see graph G2 in Figure 4).

[0038] Comparing the first and second sets of data, the measured values ​​for the first set range from 40 to 100 mmHg, while those for the second set range from 60 to 120 mmHg. This difference in range may be due to the patient's physical condition, or it may reflect differences in equipment performance or the measurement environment.

[0039] For example, a patient's physical condition may cause an increase in heart rate when measuring heart rate at the hospital, such as due to walking to the examination room. Similarly, differences in equipment performance can occur, such as using precision equipment at the hospital versus simpler measuring devices at home. Furthermore, differences in the measurement environment can lead to variations in weight readings, for instance, due to differences in altitude between home and the hospital. Therefore, even for the same patient, the first and second measurement data may differ due to variations in equipment maintenance, measurement methods, timing, and environment.

[0040] Returning to Figure 2, the conversion function 334 associates at least one of the multiple first data with the multiple second data, and converts the first data to be converted from the multiple first data into third measurement information indicating third data related to a predetermined item, based on the third condition obtained from the association. The conversion function 334 is an example of a conversion unit. Specifically, the conversion function 334 associates at least one of the multiple first data with the multiple second data, based on the multiple first data included in the first measurement information acquired by the acquisition function 333 and the multiple second data included in the second measurement information, and converts the first data to be converted from the multiple first data into third measurement information indicating third data related to a predetermined item, based on the third condition obtained from the association.

[0041] Here, the third measurement information will be explained using Figure 5. Figure 5 is a graph illustrating the third measurement information according to the embodiment. In Figure 5, the horizontal axis represents the date and time, and the vertical axis represents the systolic blood pressure [mmHG]. Similar to Figure 4, Figure 5 shows the state in which the patient is regularly receiving outpatient treatment at the hospital or is hospitalized. Graphs G1 and G2 in Figure 5 correspond to graphs G1 and G2 in Figure 4. Graph G3 in Figure 5 is a graph that displays multiple third data points included in the third measurement information arranged in chronological order.

[0042] For example, the third data point P3 shown in graph G3 is data relating to a predetermined item that has been transformed based on a third condition obtained from a correspondence between the multiple first data points and at least one of the multiple second data points, with respect to the first data point P1 shown in graph G1, which is the target of the transformation, among the multiple first data points. For example, the third condition is a condition in which the first data point P1 and the second data point shown in graph G2 are associated at predetermined measurement timings, based on the second condition.

[0043] Here, we will explain the predetermined measurement timing. The predetermined measurement timing involves comparing the measurement date and time of the first data with the measurement date and time of the second data to identify an appropriate point in time. For example, if the measurement timing of the first data is close to the measurement timing of the second data, which is the reference data, the accuracy of the data conversion can typically be verified with high reliability, and therefore the predetermined measurement timing can be identified as appropriate. On the other hand, if the measurement timing of the second data, which is measured by a healthcare professional and is correct data, is far from the measurement timing of the first data, even if the measured values ​​are close, the first data may be incorrect, and therefore the predetermined measurement timing can be identified as inappropriate.

[0044] For example, since the measurement dates and times of the first data P1 and the second data P2 shown in Figure 5 are close together, the predetermined measurement timing is appropriate. When the predetermined measurement timing is appropriate, comparing the measured values ​​of the first data P1 and the second data P2 shown in Figure 5, the difference is 60 [mmHg]. Then, the third data P3 shown in Figure 5, which is converted by the conversion function 334, is the converted data obtained by adding 60 [mmHg] to the first data P1, based on the second condition. In addition, the range of the vertical axis of graph G3 shown in Figure 5 is the same as the range of the vertical axis of graph G1, which was converted from 40~60 [mmHg] to 100~120 [mmHg].

[0045] Furthermore, when the conversion function 334 converts the first data to the third data, it may input the measured value of the first data into a certain function and use the resulting value as the measured value of the third data. The certain function is a conversion formula that converts the first data to the conditions under which the second data was measured, and can be set arbitrarily. For example, the conversion formula can be an approximation formula set in advance from the first data and the second data. Note that the conversion formula is not limited to this, and any formula that is defined for converting the first data to the conditions under which the second data was measured is acceptable.

[0046] For example, a function may be defined by a machine learning model. For instance, the machine learning model might take the first data set as input and the second data set as output. In this model, the measurements input to the first data set are blood pressure, weight, body temperature, and heart rate, while the measurements input to the second data set are also blood pressure, weight, body temperature, and heart rate. While the above example describes conversions between data of the same type, conversions between different data types are also possible.

[0047] Returning to Figure 2, the display control function 335 displays the third measurement information. The display control function 335 is an example of a display control unit. Specifically, the display control function 335 transmits the third measurement information converted by the conversion function 334 to the first terminal device 14, thereby displaying it on the display 142 of the first terminal device 14. For example, the display control function 335 arranges and displays multiple third data included in the third measurement information in chronological order based on the measurement timing, and also associates the arranged third data with multiple second data included in the second measurement information, thereby displaying the third measurement information on the display 142 of the first terminal device 14.

[0048] Here, an example of the display of the third measurement information will be explained using Figure 6. Figure 6 is a schematic diagram showing an example of the display of the third measurement information according to the embodiment. The display 61 of the third measurement information shown in Figure 6 shows an example related to vital signs. The vertical axis of the graph shown in display 61 is the date and time. The horizontal axis represents systolic blood pressure [mmHG], and the vertical axis represents systolic blood pressure [mmHG]. Furthermore, graphs G11, G12, and G13 shown in Figure 6 correspond to graphs G1, G2, and G3 shown in Figure 5.

[0049] The display 61 of the third measurement information shown in Figure 6 associates the arranged third data with the multiple second data included in the second measurement information. Specifically, since the horizontal and vertical axes of graphs G31 and G21 are displayed together, the third data and the second data are associated. Furthermore, the area 62 of the display 61 is the area that displays the period in which the time range of the measurement date and time of the third data and the time range of the measurement date and time of the second data overlap. This can also be said to indicate that the predetermined measurement timing described above is appropriate. In addition, the display 61 may also display the time range of the first data to which the third data corresponds.

[0050] Figure 7 is a flowchart showing an example of information provision processing performed by the information provision server 30 according to this embodiment.

[0051] First, the receiving function 331 receives first measurement information from the subject transmitted by the second terminal device 20, which shows multiple first data points relating to predetermined items measured under first conditions (step S71). The receiving function 331 then stores the received first measurement information in the home data DB 321 of the memory circuit 320. Next, the collection function 332 collects multiple second measurement information from each of the medical institution systems 10 for the subject, relating to predetermined items measured under second conditions different from the first conditions (step S72). The collection function 332 then stores the collected second measurement information in the in-hospital data DB 322 of the memory circuit 320.

[0052] Next, the acquisition function 333 acquires the first measurement information from the home data DB 321 and the second measurement information from the in-hospital data DB 322, which are stored in the memory circuit 320 (step S73). Subsequently, the conversion function 334 associates the first data included in the first measurement information acquired by the acquisition function 333 with at least one of the multiple first data and multiple second data. Based on the third condition obtained from the association, the first data to be converted is converted into third measurement information indicating third data related to a predetermined item (step S74).

[0053] Next, the display control function 335 transmits the third measurement information converted by the conversion function 334 to the first terminal device 14, causing it to be displayed on the display 142 of the first terminal device 14 (step S75), and then terminates this process.

[0054] As described above, in this embodiment, the system obtains first measurement information showing a plurality of first data points relating to a predetermined item measured under a first condition for the subject, and second measurement information showing a plurality of second data points relating to a predetermined item measured under a second condition different from the first condition for the subject. The system associates the plurality of first data points with at least one of the plurality of second data points, and based on the third condition obtained from the association, the first data points to be converted are converted into third measurement information showing third data points relating to the predetermined item, and the third measurement information is displayed.

[0055] This allows healthcare professionals, for example, to convert data measured by patients at home to the same measurement conditions used when measuring patients at the hospital, thereby standardizing the measurement method for patients and enabling them to correctly recognize trends in home data. Therefore, in this embodiment, measurement results measured in different environments can be displayed appropriately.

[0056] Furthermore, the above-described embodiment can be modified as appropriate by changing some of the configuration or functions of the information provision server 30. Therefore, the above-described embodiment will be described below. Several modifications relating to the form will be described as other embodiments. In the following, we will mainly describe the differences from the embodiments described above, and will omit detailed explanations of points that are common to those already described. Furthermore, the modifications described below may be implemented individually or in combination as appropriate.

[0057] (First variation) For example, the information providing server 30 may calculate reliability information indicating the reliability of the third measurement information based on at least one of the number of measurements, measurement values, and measurement timings of the first measurement information, the second measurement information, and the third measurement information. Figure 8 is a diagram showing an example of the configuration of the information providing server 30 according to the first modified example. As shown in Figure 8, the processing circuit 330 of the information providing server 30 according to the first modified example includes a receiving function 331, a collection function 332, an acquisition function 333, a conversion function 334, a display control function 335, and a calculation function 336.

[0058] The calculation function 336 calculates confidence information indicating the reliability of the third measurement information based on at least one of the following: the number of measurements, the measured value, or the measurement timing of the first measurement information, the second measurement information, or the third measurement information. The calculation function 336 is an example of a calculation unit.

[0059] Here, we will explain confidence. As mentioned above, confidence is calculated based on at least one of the following: the number of measurements, the measured value, or the measurement timing, from the first measurement information, the second measurement information, or the third measurement information. Regarding the number of measurements, a larger number of data points before conversion results in higher confidence. For example, if the first data points of the first measurement information are 2 points / day and 24*60 points / day, the latter is considered to have higher confidence.

[0060] Regarding the measured values, the reliability is high if the measured values ​​are close before and after conversion. Alternatively, the reliability is high if the values ​​of the third data included in the third measurement information and the second data included in the second measurement information are close. For example, when converting home blood pressure values ​​from the first measurement information to hospital blood pressure values ​​from the second measurement information, the greater the difference in the mean values ​​of the converted measurements, the lower the reliability. Also, for example, when converting home blood pressure values ​​from the first measurement information to hospital blood pressure values, that is, when converting home blood pressure values ​​to hospital blood pressure values ​​using the second condition as a baseline, the greater the difference in the mean values ​​of the converted measurements, the lower the reliability.

[0061] Regarding measurement timing, the reliability is high if the measurement timing before conversion is close to the measurement timing of the reference second data. Alternatively, the reliability is high if the measurement timing before conversion is close to the measurement timing after conversion. For example, if home blood pressure values ​​(first data) obtained from September 1st to September 30th, 2023 are converted to in-hospital blood pressure values ​​(third data) based on the second condition, the reliability will be high if the blood pressure values ​​actually recorded in the hospital (second data) were measured on September 15th, 2023. Furthermore, since the measurement timings overlap during the above period, the reliability may be further increased.

[0062] For example, if we convert home blood pressure readings (first data) taken from April 1st to April 30th, 2023, to hospital blood pressure readings (third data) taken from September 1st to September 30th, 2023, using the second condition as a baseline, the reliability is low because the measurement timings are separated by six months.

[0063] While the above explanation often compares two sets of data, it is also possible to pre-define absolute ranges for the number of measurements, measured values, and measurement timings, and then calculate the confidence level based on whether the data falls within or outside those ranges. Alternatively, the confidence level may be calculated by considering all three factors: the number of measurements, measured values, and measurement timings.

[0064] Next, the calculation function 336 will explain how it calculates confidence information indicating the reliability of the third measurement information. For example, when the calculation function 336 calculates confidence information based on the measurement timing, it uses a certain time width t of the third data. 3,0 from t 3,T If there is no second data X2 during this period, the confidence level is calculated to be low. This is because there is no second data measured under the same reference second condition during the same period, and therefore it cannot be converted using the same standard. Also, a certain time width t of the third data X3 3,0 from t 3,T If a second data point X2 exists between these two points, the confidence level is calculated to be medium or above medium.

[0065] Furthermore, for example, when the calculation function 336 calculates reliability information based on measurement timing and interpolation, the reliability is defined as the ratio of the difference between the measured value of the second data and the measured value of the third data to the variance of the second data. However, the time T of the third data is the measurement timing of the third data, and if the measurement timing of the second data differs from the measurement timing of the third data, the measured value of the second data obtained at the measurement timing of the third data can be used by interpolating the second data.

[0066] For example, in the case of equation (1) shown below, the interpolation coefficient 1 / C can be expressed as equation (2) shown below.

[0067]

number

[0068]

number

[0069] Here, the measurement timing t m,n When n=0,1,...,T, the mth data point (m=1,2,3) is Xm(t m,n ), VAR will be distributed.

[0070] The display control function 335 displays the multiple second data points included in the second measurement information in association with the confidence information calculated by the calculation function 336. Alternatively, the display control function 335 may also display the third measurement information in association with the second measurement information and the confidence information.

[0071] Figure 9 is a schematic diagram showing an example of the display of reliability information related to the first modification. In the graph shown in display 63 in Figure 9, the vertical axis is date and time, and the horizontal axis is systolic blood pressure [mmHG]. In addition, graph G22 in Figure 9 is a graph that displays multiple second data included in the second measurement information arranged in chronological order, and graph G41 is a graph that displays the reliability of the reliability information arranged in chronological order. Graph G41 in Figure 9 shows the error of the measured values ​​when multiple third data are displayed arranged in chronological order.

[0072] Figure 10 is a flowchart showing an example of the information provision process performed by the information provision server 30 according to the first modified example. Note that the processes in steps S71 to S74 shown in Figure 10 are the same as the processes in steps S71 to S74 shown in Figure 7, so their explanation is omitted.

[0073] In step S91, the calculation function 336 calculates confidence information indicating the reliability of the third measurement information based on at least one of the number of measurements, measured values, or measurement timings of the first measurement information, second measurement information, or third measurement information (step S91). In step S92, the display control function 335 displays the multiple second data included in the second measurement information in association with the confidence information calculated by the calculation function 336 (step S92), and then terminates this process.

[0074] As described above, in the first modified example, confidence information indicating the reliability of the third measurement information is calculated based on at least one of the following: the number of measurements, the measured value, or the measurement timing of the first, second, or third measurement information. This confidence information is then displayed in association with multiple second data points included in the second measurement information. This allows, for example, healthcare professionals to understand the reliability of data measured at home by a patient compared to data measured at the hospital, and to correctly recognize trends in the home data.

[0075] (Second variation) For example, if the confidence level included in the confidence level information is below a predetermined threshold, the information provision server 30 may determine recommendation information regarding the recommendation of remeasurement based on the first or second condition. Figure 11 is a diagram showing an example of the configuration of the information provision server 30 according to the second modified example. As shown in Figure 11, the processing circuit 330 of the information provision server 30 according to the second modified example includes a receiving function 331, a collection function 332, an acquisition function 333, a conversion function 334, a display control function 335, a calculation function 336, a judgment function 337, and a determination function 338.

[0076] The determination function 337 determines whether the confidence level included in the confidence information is below a predetermined threshold. The determination function 337 is an example of a determination unit. Specifically, the determination function 337 determines whether the confidence level included in the confidence information calculated by the calculation function 336 is below a predetermined threshold.

[0077] The decision function 338 determines recommendation information regarding the recommendation of remeasurement based on the first or second condition if the confidence level included in the confidence information is below a predetermined threshold. The decision function 338 is an example of a decision unit. Specifically, if the judgment function 337 determines that the confidence level included in the confidence information is below a predetermined threshold, the decision function 338 determines recommendation information regarding the recommendation of remeasurement based on the first or second condition. The display control function 335 then displays the multiple second data included in the second measurement information, the confidence information, and the recommendation information in association with each other.

[0078] Figure 12 is a schematic diagram showing an example of the display of recommended information related to the second modified example. Display 64 in Figure 12 is a schematic diagram in which recommended information 121 is added to display 63 in Figure 9. Recommended information 121 in Figure 12 shows an example of the text, for example, "Recommendation: Remeasure blood pressure (in-hospital)". Healthcare professionals may be able to view reliability information (basis values ​​and calculation methods) by clicking on the display prompting remeasurement. For example, the "i" shown in recommended information 121 shows an example of information that can be viewed by clicking on an information button.

[0079] Furthermore, the decision function 338 determines the recommendation information based on the confidence level included in the confidence information and the cost of remeasurement. Here, the cost of remeasurement will be explained using Figure 13. Figure 13 is a table showing an example of the cost of remeasurement related to the second modified example. In table T2 shown in Figure 13, the cost of remeasurement is associated with the content of the examination, the time required for the examination, the cost required for the examination, the physical burden on the patient, and the patient's physical burden.

[0080] For example, if the test is blood pressure, the decision function 338 adds the values ​​assigned to the time required for the test, the cost of the test, the physical burden on the patient, and the patient's physical burden to calculate the cost of remeasurement. Then, the decision function 338 determines the recommendation information based on the calculated cost of remeasurement and the confidence level included in the confidence information. Also, Table T2 shown in Figure 13 is just an example, and the contents regarding the cost of remeasurement are not limited to this. Furthermore, the values ​​assigned to each item may differ depending on the patient.

[0081] Figure 14 is a flowchart showing an example of information provision processing performed by the information provision server 30 according to the second modified example. Note that the processing in steps S71 to S74 and steps S91 to S92 shown in Figure 14 is the same as the processing in steps S71 to S74 and steps S91 to S92 shown in Figure 10, so the explanation is omitted.

[0082] In step S141, the determination function 337 determines whether the confidence level included in the confidence information is lower than a predetermined threshold (step S141). If the determination function 337 determines that the confidence level included in the confidence information is higher than the predetermined threshold (step S141: No), the process proceeds to step S92. On the other hand, if the determination function 337 determines that the confidence level included in the confidence information is lower than the predetermined threshold (step S141: Yes), the process proceeds to step S142.

[0083] In step S142, if the determination function 337 determines that the confidence level included in the confidence information is below a predetermined threshold, the determination function 338 determines recommendation information regarding the recommendation of remeasurement based on the first or second condition (step S142). In step S143, the display control function 335 displays the multiple second data included in the second measurement information, the confidence information, and the recommendation information in association (step S143), and terminates this process. Note that if the determination function 337 determines that the confidence level included in the confidence information is above a predetermined threshold, the display control function 335 does not display the recommendation information because the determination function 338 has not yet determined the recommendation information.

[0084] As described above, in the second modified version, if the confidence level included in the confidence information is below a predetermined threshold, recommendation information regarding the recommendation of remeasurement based on the first or second condition is determined, and the multiple second data points included in the second measurement information, the confidence information, and the recommendation information are displayed in association. This allows, for example, healthcare professionals to understand the confidence level when comparing data measured by the patient at home with data measured by the patient at the hospital, and to recognize the content regarding remeasurement.

[0085] (Third variation) For example, if the confidence level included in the confidence level information is below a predetermined threshold, the information provision server 30 may use a machine learning model to determine recommendation information regarding the recommendation for remeasurement based on the first or second condition.

[0086] For example, if we consider a machine learning model as a clinical support model, this model takes blood pressure data as input and determines recommendation information regarding recommendations for re-measurement. Specifically, the decision function 338 of the information provision server 30 inputs third data to the clinical support model and outputs first output data. The decision function 338 of the information provision server 30 also inputs second data to the clinical support model and outputs second output data.

[0087] For example, the first and second output data are the output results of blood pressure data from a machine learning model. The decision function 338 decides not to recommend remeasurement if the first and second output data match. On the other hand, if the first and second output data do not match, the decision function 338 decides to recommend remeasurement and determines the recommendation information.

[0088] For example, if we consider a machine learning model as a deterioration prediction model, this model takes blood pressure data as input and predicts whether or not there will be deterioration (Positive / Negative) within one year. Here, "Positive" means that deterioration is predicted, while "Negative" means that stability is predicted.

[0089] Specifically, the decision function 338 of the information provision server 30 inputs third data into the deterioration prediction model and outputs third output data predicting whether deterioration will occur within one year. The decision function 338 of the information provision server 30 also inputs second data into the deterioration prediction model and outputs fourth output data predicting whether deterioration will occur within one year. For example, if the third output data and the fourth output data match, the decision function 338 decides not to recommend remeasurement. On the other hand, if the third output data and the fourth output data do not match, the decision function 338 decides to recommend remeasurement and determines the recommendation information.

[0090] In other words, the decision function 338 decides not to recommend remeasurement if both the third output data and the fourth output data are output as "Positive" or "Negative". On the other hand, the decision function 338 decides to recommend remeasurement and determines the recommendation information if the third output data is output as "Positive" and the fourth output data is output as "Negative". Also, the decision function 338 decides to recommend remeasurement and determines the recommendation information if the third output data is output as "Negative" and the fourth output data is output as "Positive".

[0091] This allows healthcare professionals, for example, to understand the output of a machine learning model that corresponds to the confidence level when comparing data measured by the patient at home with data measured by the patient at the hospital, and to recognize information regarding remeasurement.

[0092] (Fourth variation) For example, the decision function 338 of the information provision server 30 may use a machine learning model to determine recommended information based on the confidence level to be included in the confidence level information and the cost of remeasurement. Here, the machine learning model is, for example, the deterioration prediction model shown in the third modified example.

[0093] For example, if the data input to the deterioration prediction model is cardiothoracic ratio data, the decision function 338 assumes that the third output data is "Negative" and the fourth output data is "Positive". Remeasuring the cardiothoracic ratio data is equivalent to re-imaging a chest X-ray, but it is possible that the cost of remeasuring, the radiation dose during re-imaging, and the costs required for re-imaging may exceed predetermined values. Even if the third output data is "Negative" and the fourth output data is "Positive", the decision function 338 decides not to recommend remeasuring from the perspective of the cost of remeasuring.

[0094] This allows healthcare professionals, for example, to understand the output of a machine learning model that corresponds to the confidence level when comparing data measured at home with data measured at the hospital, as well as the cost of remeasurement and to be aware of the details related to remeasurement.

[0095] (Fifth variation) For example, the information provision server 30 may associate the first measurement information and the second measurement information with the measuring devices used by the patient at home and the measuring devices used by the patient at the hospital, and store this information in the memory circuit 320 as a data correspondence definition table. Figure 15 is a schematic diagram showing an example of a data correspondence definition table according to the fifth modified example. In the table T3 shown in Figure 15, the correspondence between the in-hospital data DB 322 and the home data DB 321 is associated with the measuring devices used by the patient at the hospital and the measuring devices used by the patient at home. This allows healthcare professionals to understand which measuring devices the patient uses at home.

[0096] (Sixth variation) For example, the information provision server 30 may add a correspondence period to the data correspondence relationship specification table described above. Figure 16 is a schematic diagram showing an example of a data correspondence relationship specification table according to the sixth modified example. Table T4 shown in Figure 16 is a table in which an item related to the correspondence period has been added to table T3 shown in Figure 15. The correspondence period shown in Figure 16 corresponds to the predetermined measurement timing shown in the third condition. In other words, the correspondence period can be said to be a period with high reliability. If the measurement date and time of the first data and the second data are within the correspondence period shown in Figure 16, the conversion function 334 can convert them into the third measurement information, which has high reliability, because the reliability is high.

[0097] (Seventh variation) In the embodiment described above, an information provision server 30 is provided on a network N1 independent of the medical institution system 10. However, the location of the information provision server 30 is not limited to this. For example, the information provision server 30 may be installed in the medical institution system 10. Furthermore, some or all of the functions of the information provision server 30 (receiving function 331, collection function 332, acquisition function 333, conversion function 334, display control function 335, calculation function 336, judgment function 337, and decision function 338) may be provided by any device within the medical information processing system, such as a terminal device 14.

[0098] In the embodiments described above, examples were given in which the receiving unit, collection unit, acquisition unit, conversion unit, display control unit, calculation unit, determination unit, and decision unit in this specification are implemented by the receiving function 331, collection function 332, acquisition function 333, conversion function 334, display control function 335, calculation function 336, determination function 337, and decision function 338 of the processing circuit 330, respectively. However, the embodiments are not limited to these. For example, the receiving unit, collection unit, acquisition unit, conversion unit, display control unit, calculation unit, determination unit, and decision unit in this specification may be implemented not only by the receiving function 331, collection function 332, acquisition function 333, conversion function 334, display control function 335, calculation function 336, determination function 337, and decision function 338 described in the embodiments, but may also be implemented by hardware alone or by a combination of hardware and software.

[0099] When the technical concept of this embodiment is realized in a medical information processing method, the medical information processing method acquires first measurement information indicating first data measured under first conditions for a subject, and second measurement information indicating second data measured under second conditions different from the first conditions for the subject. Based on the second conditions as a reference, the first data is converted into third measurement information indicating third data based on a third condition that associates the first data and the second data, and the third measurement information is displayed. The processing procedure and effects of the medical information processing method are the same as in the embodiment, so a description is omitted.

[0100] Furthermore, the term "processor" used in the above explanation refers to circuits such as CPUs (Central Processing Units), GPUs (Graphics Processing Units), Application Specific Integrated Circuits (ASICs), and Programmable Logic Devices (e.g., Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs)). The processor realizes its functions by reading and executing programs stored in the memory circuit 320.

[0101] Alternatively, instead of saving the program in the memory circuit 320, the program may be directly incorporated into the processor's circuitry. In this case, the processor functions by reading and executing the program incorporated into the circuitry. Furthermore, the processor in this embodiment is not limited to being configured as a single circuit; it may be configured as a single processor by combining multiple independent circuits, and its functions may be realized in this way.

[0102] Here, the program executed by the processor is provided pre-installed in ROM (Read Only Memory) or memory circuits. This program may also be provided as a file in an installable or executable format on computer-readable storage media such as CD (Compact Disk)-ROM, FD (Flexible Disk), CD-R (Recordable), or DVD (Digital Versatile Disk). Furthermore, this program may be stored on a computer connected to a network such as the Internet and provided or distributed by downloading it via the network. For example, this program consists of modules containing the various functional units described above. In actual hardware, the CPU reads the program from a storage medium such as ROM and executes it, loading each module onto main memory and generating it in main memory.

[0103] According to at least one embodiment described above, measurement results measured in different environments can be displayed appropriately.

[0104] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0105] 10…Medical institution system, 20…Second terminal device, 30…Information provision server, 331...Receiving function, 332...Collection function, 333...Acquisition function, 334...Conversion function, 335...Display control function, 336...Calculation function, 337...Judgment function, 338...Decision function

Claims

1. An acquisition unit that acquires, for a subject, first measurement information showing a plurality of first data regarding a predetermined item measured under a first condition, and second measurement information showing a plurality of second data regarding the predetermined item measured under a second condition different from the first condition, A conversion unit that associates a plurality of first data with at least one of a plurality of second data, and converts the first data to be converted from the plurality of first data into third measurement information indicating third data relating to a predetermined item, based on a third condition obtained from the association, The display control unit for displaying the aforementioned third measurement information, A medical information processing device equipped with [a specific feature].

2. The acquisition unit acquires the first data and the second data along with the measurement timing. The display control unit arranges and displays a plurality of the third data included in the third measurement information in chronological order based on the measurement timing, and displays the third measurement information by associating the arranged third data with a plurality of the second data included in the second measurement information. The medical information processing device according to claim 1.

3. The system includes a calculation unit that calculates reliability information indicating the reliability of the third measurement information based on at least one of the following: the number of measurements, the measured value, and the measurement timing of any of the first measurement information, the second measurement information, and the third measurement information. The display control unit displays the plurality of second data included in the second measurement information and the reliability information in association with each other. The medical information processing device according to claim 2.

4. If the confidence level included in the confidence information is below a predetermined threshold, the system includes a determination unit that determines recommendation information regarding the recommendation of remeasurement based on the first condition or the second condition. The display control unit displays the plurality of second data included in the second measurement information, the reliability information, and the recommendation information in association with each other. The medical information processing device according to claim 3.

5. The determination unit determines the recommendation information based on the reliability included in the reliability information and the cost of the remeasurement. The medical information processing device according to claim 4.

6. The first condition is a method of measurement performed by a non-medical professional, and the second condition is a method of measurement performed by a medical professional. A medical information processing device according to any one of claims 1 to 5.

7. An acquisition unit that acquires, for a subject, first measurement information showing a plurality of first data regarding a predetermined item measured under a first condition, and second measurement information showing a plurality of second data regarding the predetermined item measured under a second condition different from the first condition, A conversion unit that associates a plurality of first data with at least one of a plurality of second data, and converts the first data to be converted from the plurality of first data into third measurement information indicating third data relating to a predetermined item, based on a third condition obtained from the association, The display control unit for displaying the aforementioned third measurement information, A medical information processing system equipped with [a specific feature / feature].

8. For a subject, first measurement information is obtained, which shows a plurality of first data points relating to a predetermined item measured under a first condition, and second measurement information is obtained, which shows a plurality of second data points relating to the predetermined item measured under a second condition different from the first condition. A correspondence is made between the multiple first data and at least one of the multiple second data, and the first data to be converted from the multiple first data is converted into third measurement information indicating the third data relating to the predetermined item based on the third condition obtained from the correspondence, The above 3. Display the measurement information. Medical information processing method.

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