Medical information processing device, medical information processing method, and program
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- CANON KK
- Filing Date
- 2022-07-21
- Publication Date
- 2026-08-03
Smart Images
Figure 0007898974000001 
Figure 0007898974000002 
Figure 0007898974000003
Abstract
Description
Technical Field
[0001] The embodiments disclosed in this specification and the drawings relate to a medical information processing apparatus, a medical information processing method, and a program.
Background Art
[0002] It is said that there are 60 to 80 attribute factors related to cancer diagnosis and treatment. These numerous attribute factors exist in the information systems of multiple different medical departments such as radiology and oncology, for example, and cannot be covered by just an electronic medical record. Therefore, there is a situation where not all attribute factors can be managed. Furthermore, the number of attribute factors is large, and there are also a variety of systems capable of searching these attribute factors. It takes time to examine all the attribute factors and it is also easy to overlook something. Also, by focusing on selecting an appropriate treatment method, the relationship with the patient's outcome may be overlooked.
Prior Art Documents
Patent Documents
[0003]
Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0004] The problem to be solved by the embodiments disclosed in this specification and the drawings is to select a treatment method that can improve the patient's outcome while enhancing the certainty and efficiency of collecting attribute factors related to a disease. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of each configuration shown in the embodiments described later can also be regarded as other problems.
Means for Solving the Problems
[0005] The medical information processing device of this embodiment includes an acquisition unit, an identification unit, and an output control unit. The acquisition unit acquires multiple attribute factors related to the disease of the target patient. The identification unit identifies related attribute factors from among the multiple attribute factors that are highly related to the disease. The output control unit outputs information based on the related attribute factors via an output interface. [Brief explanation of the drawing]
[0006] [Figure 1] A diagram showing an example configuration of the medical information processing system 1 in the first embodiment. [Figure 2] A diagram showing an example configuration of the terminal device 10 in the first embodiment. [Figure 3] A diagram showing an example configuration of the medical information processing device 100 in the first embodiment. [Figure 4] A flowchart showing the sequence of processes of the processing circuit 120 according to the first embodiment. [Figure 5] A diagram representing the clinical tree of the guidelines. [Figure 6] This figure shows an example of how related attribute factors are displayed. [Figure 7] A diagram showing an example of displaying a predetermined number of related attribute factors. [Figure 8] A diagram illustrating an example of a treatment method. [Figure 9] A diagram showing an example of how the overall map is displayed. [Figure 10] A diagram showing an example of how the overall map is displayed. [Figure 11] A flowchart showing the sequence of processes of the processing circuit 120 according to the second embodiment. [Figure 12] A diagram illustrating the stratification process. [Figure 13] A diagram illustrating how to compare attribute distributions. [Modes for carrying out the invention]
[0007] The medical information processing device, medical information processing method, and program of the embodiment will be described below with reference to the drawings.
[0008] (First Embodiment) [Configuration of the medical information processing system] Figure 1 is a diagram showing an example configuration of the medical information processing system 1 in the first embodiment. The medical information processing system 1 includes, for example, a terminal device 10 and a medical information processing device 100. The terminal device 10 and the medical information processing device 100 are connected to each other via a communication network NW.
[0009] The term "communication network (NW)" can refer to any information and communication network that utilizes telecommunications technology. For example, communication networks (NW) include wireless / wired LANs such as hospital backbone LANs (Local Area Networks), the Internet network, as well as telephone communication lines, fiber optic communication networks, cable communication networks, and satellite communication networks.
[0010] Terminal device 10 is a terminal device such as a personal computer, tablet, or mobile phone used by healthcare professional P2. Healthcare professional P2 is typically a doctor, but may be a nurse or other person involved in medical care, or a person involved in community care services. Healthcare professional P2 inputs information about the patient to be treated (hereinafter referred to as target patient P1) into terminal device 10, for example.
[0011] Alternatively, instead of the medical professional P2 inputting the information, the patient P1 or their family may input information about patient P1 into the terminal device 10. Similarly, family members may also input information about themselves into the terminal device 10.
[0012] In this embodiment, "treatment" may include not only direct treatments such as surgery, drug therapy, chemotherapy, and photoimmunotherapy, but also all medical actions that may be performed before, during, or after treatment. For example, "treatment" in this embodiment may include "prerehabilitation" performed before direct treatments such as surgery, drug therapy, chemotherapy, and photoimmunotherapy, or it may include "rehabilitation" performed after such direct treatments.
[0013] The terminal device 10 transmits information input by medical staff P2 or the like to the medical information processing device 100 via the communication network NW, or receives information from the medical information processing device 100.
[0014] The medical information processing device 100 receives information from the terminal device 10 via the communication network NW and processes the received information. Then, the medical information processing device 100 transmits the processed information to the terminal device 10 via the communication network NW.
[0015] The medical information processing device 100 may be a single device, or may be a system in which a plurality of devices connected via the communication network NW cooperate with each other. That is, the medical information processing device 一00 may be realized by a plurality of computers (processors) included in a distributed computing system or a cloud computing system. Further, the medical information processing device 100 does not necessarily have to be a separate device different from the terminal device 10, and may be a device integrated with the terminal device 10.
[0016] [Configuration of Terminal Device] FIG. 2 is a diagram showing a configuration example of the terminal device 10 in the first embodiment. The terminal device 10 includes, for example, a communication interface 11, an input interface 12, an output interface 13, a memory 14, and a processing circuit 20.
[0017] The communication interface 11 communicates with the medical information processing device 100 and the like via the communication network NW. The communication interface 11 includes, for example, a NIC (Network Interface Card) or an antenna for wireless communication.
[0018] The input interface 12 receives various input operations from the operator (e.g., medical professional P2), converts the received input operations into electrical signals, and outputs them to the processing circuit 20. For example, the input interface 12 includes a mouse, keyboard, trackball, switch, button, joystick, touch panel, etc. The input interface 12 may also be a user interface that accepts audio input, such as from a microphone. If the input interface 12 is a touch panel, it may also have the display function of the display 13a described later.
[0019] In this specification, the input interface 12 is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit is also included as an example of the input interface 12.
[0020] The output interface 13 outputs information under the control of the processing circuit 20. For example, the output interface 13 includes a display 13a and a speaker 13b.
[0021] The display 13a displays various types of information. For example, the display 13a displays images generated by the processing circuit 20, or a GUI (Graphical User Interface) for receiving various input operations from the operator. For example, the display 13a may be an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, or an organic EL (Electro Luminescence) display.
[0022] Speaker 13b converts various types of information into sound and outputs it. For example, speaker 13b outputs information input from processing circuit 20 as sound.
[0023] Memory 14 can be implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disc. These non-transient storage media may also be implemented by other storage devices connected via a communication network NW, such as NAS (Network Attached Storage) or external storage server devices. Memory 14 may also include non-transient storage media such as ROM (Read Only Memory) or registers.
[0024] The processing circuit 20 includes, for example, an acquisition function 21, an output control function 22, and a communication control function 23. The processing circuit 20 realizes these functions, for example, by a hardware processor (computer) executing a program stored in the memory 14 (storage circuit).
[0025] In the processing circuit 20, the hardware processor refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD) or a Complex Programmable Logic Device (CPLD), or a Field Programmable Gate Array (FPGA)). Instead of storing the program in memory 14, the program may be directly embedded within the hardware processor's circuitry. In this case, the hardware processor functions by reading and executing the program embedded within the circuitry. The program may be stored in memory 14 beforehand, or it may be stored on a non-temporary storage medium such as a DVD or CD-ROM, and installed from the non-temporary storage medium to memory 14 when the non-temporary storage medium is inserted into the drive device (not shown) of the terminal device 10. A hardware processor is not limited to being a single circuit; it may also be composed of multiple independent circuits combined to perform various functions. Alternatively, multiple components may be integrated into a single hardware processor to perform various functions.
[0026] The acquisition function 21 acquires input information via the input interface 12 or acquires information from the medical information processing device 100 via the communication interface 11.
[0027] The output control function 22 displays the information acquired by the acquisition function 21 as an image on the display 13a or outputs it as sound from the speaker 13b.
[0028] The communication control function 23 transmits the information input to the input interface 12 to the medical information processing device 100 via the communication interface 11.
[0029] [Configuration of medical information processing device] Figure 3 is a diagram showing an example configuration of the medical information processing device 100 in the first embodiment. The medical information processing device 100 includes, for example, a communication interface 111, an input interface 112, an output interface 113, a memory 114, and a processing circuit 120.
[0030] The communication interface 111 communicates with terminal devices 10 and the like via the communication network NW. The communication interface 111 includes, for example, a NIC.
[0031] The input interface 112 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs them to the processing circuit 120. For example, the input interface 112 includes a mouse, keyboard, trackball, switch, button, joystick, touch panel, etc. The input interface 112 may also be a user interface that accepts audio input, such as from a microphone. If the input interface 112 is a touch panel, it may also have the display function of the display 113a described later.
[0032] In this specification, the input interface 112 is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit is also included as an example of the input interface 112.
[0033] The output interface 113 outputs information under the control of the processing circuit 120. For example, the output interface 113 includes a display 113a and a speaker 113b.
[0034] Display 113a displays various types of information. For example, display 13a displays images generated by the processing circuit 120, a GUI for receiving various input operations from the operator, etc. For example, display 113a may be an LCD, CRT display, organic EL display, etc.
[0035] Speaker 113b converts various types of information into sound and outputs it. For example, speaker 113b outputs information input from processing circuit 120 as sound.
[0036] Memory 114 can be implemented by, for example, semiconductor memory elements such as RAM or flash memory, a hard disk, or an optical disc. These non-transient storage media may also be implemented by other storage devices connected via a communication network NW, such as a NAS or an external storage server device. Memory 114 may also include non-transient storage media such as ROM or registers.
[0037] The processing circuit 120 includes, for example, an acquisition function 121, an estimation function 122, a specification function 123, and an output control function 124. The acquisition function 121 is an example of an "acquisition unit," the estimation function 122 is an example of an "estimation unit," the specification function 123 is an example of a "specification unit," and the output control function 124 is an example of an "output control unit."
[0038] The processing circuit 120 realizes these functions, for example, by having a hardware processor (computer) execute a program stored in the memory 114 (storage circuit).
[0039] In the processing circuit 120, the hardware processor refers to, for example, a CPU, GPU, application-specific integrated circuit, or programmable logic device (e.g., a simple programmable logic device or a complex programmable logic device, a field-programmable gate array, etc.). Instead of storing the program in memory 114, the program may be directly incorporated into the hardware processor's circuit. In this case, the hardware processor realizes its function by reading and executing the program incorporated into the circuit. The program may be stored in memory 114 beforehand, or it may be stored in a non-temporary storage medium such as a DVD or CD-ROM, and installed from the non-temporary storage medium to memory 114 when the non-temporary storage medium is mounted in the drive device (not shown) of the medical information processing device 100. The hardware processor is not limited to being configured as a single circuit; it may be configured as a single hardware processor by combining multiple independent circuits to realize each function. Alternatively, multiple components may be integrated into a single hardware processor to realize each function.
[0040] [Processing flow of medical information processing equipment] The following describes the processing of each function by the processing circuit 120 of the medical information processing device 100, following the flowchart. Figure 4 is a flowchart showing the sequence of processing of the processing circuit 120 according to the first embodiment.
[0041] First, the acquisition function 121 acquires attribute factors related to the disease of the target patient P1 (step S100).
[0042] An attribute factor group consists of multiple attribute factors of different types. Attribute factors (attribute parameters) include various factors such as epidemiological information, patient functional capacity indicators, primary disease state, overall health status, genetic predisposition, medical history, family history, treatment history, lifestyle, diseased organ status, non-disease organ status, and tumor status. Non-disease organ status includes, for example, factors related to the body's immune function and cardiopulmonary function.
[0043] More specifically, attribute factors may include factors indicating the patient's current condition, factors indicating the progression of the patient's condition since their first visit, factors indicating the progression of the patient's condition during follow-up, factors indicating pre-existing conditions, or factors indicating genetic information. Some of these attribute factors may be omitted or replaced with other attribute factors not exemplified. For example, attribute factors may include treatment methods and treatment history.
[0044] Current status includes, for example, age, sex, weight, blood type, vital signs, co-existing diseases, and expected complications. Changes in the patient's condition since admission include, for example, weight, cardiac function status, respiratory status, metabolic status, imaging parameters indicating disease characteristics, and non-imaging parameters indicating disease characteristics. Changes in the patient's condition during follow-up include, for example, weight, cardiac function status, respiratory status, metabolic status, imaging parameters indicating disease characteristics, and non-imaging parameters indicating disease characteristics.
[0045] For example, suppose a healthcare professional P2 interviews each patient and their family to gather information about the patient P1's current condition, the progression of their condition, their medical history, and genetic information, and inputs the results of this interview into the terminal device 10. In this case, the terminal device 10 transmits this input information as a group of attribute factors to the medical information processing device 100. When the communication interface 111 receives the group of attribute factors from the terminal device 10, the acquisition function 121 of the medical information processing device 100 acquires the group of attribute factors from the communication interface 111.
[0046] The attribute factors described above vary in type and number depending on the disease and its treatment method. For example, the attribute factors related to cancer treatment consist of 60 to 80 attribute factors. In this flowchart, attribute factors with a high correlation to the disease are identified from among these dozens of attribute factors.
[0047] Attribute factors may be graded according to guidelines established by administrative agencies, etc., to determine their degree (factor value). Alternatively, attribute factors may be graded according to diagnostic criteria or diagnostic trees independently established by each medical institution. Furthermore, attribute factors may be graded using databases, machine learning, or deep learning. Attribute factors that have not been quantified (e.g., medical history, family history, lifestyle, etc.) may be quantified based on their impact on the disease.
[0048] Next, the specific function 123 identifies one or more relevant attribute factors (typically multiple relevant attribute factors) within the group of attribute factors of the target patient P1 (step S102). Relevant attribute factors are, as described above, attribute factors that are highly associated with the disease, and specifically, attribute factors that have a greater influence than other attribute factors on at least one or both of (1) the selection of treatment methods for the disease and (2) improvement of outcomes.
[0049] Outcomes refer to various indicators that patients should achieve during their treatment, including, for example, the time taken to remission, survival period, and period of independent living.
[0050] For example, specific function 123 may refer to guidelines or the latest evidence and identify attribute factors of the criteria defined in those guidelines or the latest evidence as related attribute factors. More specifically, specific function 123 compares each attribute factor included in the attribute factor group of target patient P1 with the attribute factors of the criteria defined in the guidelines or the latest evidence, and identifies attribute factors within the attribute factor group whose deviation from the criterion attribute factors is above a threshold as related attribute factors.
[0051] Furthermore, for example, specific function 123 may use a clinical tree of guidelines, as illustrated in Figure 5, to establish an input-output relationship between attribute factors and the selection of treatment methods, and identify attribute factors that have a higher degree of influence on the selection of treatment methods for a disease compared to other attribute factors, and identify them as related attribute factors.
[0052] Furthermore, for example, specific function 123 may use statistical analysis and machine learning to identify attribute factors that have a higher degree of influence on the improvement of the outcome compared to other attribute factors, by relating attribute factors to the input and output of attribute factors and outcomes, and identify these as related attribute factors. For example, specific function 123 compares each attribute factor included in the attribute factor group of target patient P1 with the attribute factors of other patient groups in which the outcome has improved, and identifies attribute factors within the attribute factor group whose deviation from the attribute factors of other patient groups is above a threshold as related attribute factors. The attribute factors of other patient groups in which the outcome has improved are an example of a "second attribute factor".
[0053] Next, the specific function 123 collects information about related attribute factors (step S104). For example, the specific function 123 accesses terminal devices 10 available to healthcare professionals P2 or systems within the medical institution via the communication interface 111, and collects various information such as text and numerical values associated with related attribute factors from the medical data of the target patient P1, using the names of the related attribute factors as keywords. The medical data of the target patient P1 may be electronic data in a general-purpose format such as Portable Document Format, or it may be electronic data in a dedicated format or protocol determined between each system (e.g., HL7 V2.5 or HL7 FHIR).
[0054] Next, the output control function 124 outputs information about the collected related attribute factors (step S106). For example, the output control function 124 may display the information about the related attribute factors on the display 113a of the output interface 113. Alternatively, the output control function 124 may transmit the information about the related attribute factors to the terminal device 10 via the communication interface 111. This completes the processing of this flowchart.
[0055] [Example 1] Figure 6 shows an example of the display of related attribute factors. As shown in the figure, the output control function 124 may display multiple related attribute factors in a list on the display 113a. In this case, the output control function 124 may normalize the scale of each of the multiple related attribute factors into three stages (within the reference range / above the reference range / below the reference range) or five stages, based on a certain criterion. The criterion may be a fixed value based on medical statistics or guidelines, or a positive or negative margin may be set relative to a reference value (national average, average within the facility), or it may be within or outside the range of the upper or lower 2σ or 80%CV of the overall distribution.
[0056] The output control function 124 may, when displaying multiple related attribute factors, display only the top predetermined number of related attribute factors that have a high influence on the selection of treatment methods for the disease, or display only the top predetermined number of related attribute factors that have a high influence on the improvement of outcomes.
[0057] [Example 2] Figure 7 shows an example of displaying a predetermined number of top-ranking related attribute factors. As shown in the figure, the output control function 124 may display only the predetermined number of top-ranking related attribute factors in a list on the display 113a. In this case, the output control function 124 may also display the optimal range R for application to each treatment method. This optimal range R is set, for example, to be within the range of ±2σ or 80%CV when assuming a normal distribution and mean values for each related attribute factor in a patient group with good outcomes (Group A described later). The optimal range R may also be set by calculating the mean and variance for each of the patient groups with average outcomes (Group B described later), patients with poor outcomes (Group C described later), and patients with good outcomes (Group A described later), and setting the optimal range R to be within the range of ±2σ or 80%CV of the distribution. This value may be stored and used as a database, or it may be calculated each time. Alternatively, values from guidelines or evidence may be used.
[0058] Furthermore, the output control function 124 may display multiple treatment methods applicable to the target patient P1 while comparing them based on guidelines, evidence, disease groups, and a predetermined number of top-ranking related attribute factors.
[0059] [Example 3] Figure 8 shows an example of how treatment methods are displayed. As shown in the figure, the output control function 124 may provide user-selectable tabs TB and display treatment methods for each tab TB. For example, tab TB1 may display treatment methods in "recommended order," tab TB2 may display treatment methods in "highest survival rate order," tab TB3 may display treatment methods in "optimal treatment order," tab TB4 may display treatment methods in "lowest treatment cost order," and tab TB5 may display treatment methods in "shortest hospital stay order." The recommended order is an order that balances the order determined in the other tabs.
[0060] Furthermore, the output control function 124 may display on an overall map which scene and which decision is being made during the patient journey, and visualize and display the current position on that overall map.
[0061] A patient journey is information that represents the entirety of each patient's past, present, and / or future medical treatment. Specifically, it is structured information that associates each patient's patient information with the medical treatments they have received in the past, are currently receiving, or will receive in the future. Patient information is information about the patient that arises from medical treatment, and includes, for example, the attribute factors mentioned above, as well as test results, nursing records, and medical images (such as X-ray images and CT images).
[0062] [Example 4] Figures 9 and 10 show examples of the overall map display. As shown in the figures, the output control function 124 displays the decision tree in the patient journey of the target patient P1, and the related attribute factors displayed may be changed for each decision in the decision tree. For example, as shown in Figure 9, the output control function 124 may display only the related attribute factors that contribute to "Decision X" in the case of "Decision X," and as shown in Figure 10, it may display only the related attribute factors that contribute to "Decision Y" in the case of "Decision Y." In this case, these related attribute factors may be displayed as a radar chart with the degree of each attribute factor graded on a scale of 0 to 5.
[0063] According to the first embodiment described above, the medical information processing device 100 acquires a group of attribute factors related to the disease of the target patient P1. The medical information processing device 100 identifies related attribute factors from the group of attribute factors that are highly related to the disease. The medical information processing device 100 then outputs information based on the related attribute factors via the output interface 113. This makes it possible to select a treatment method that can improve the patient's outcome while increasing the reliability and efficiency of collecting attribute factors related to the disease.
[0064] (Second Embodiment) The second embodiment will now be described. The second embodiment differs from the first embodiment in that, when a certain treatment method is applied to a target patient P1, the treatment cost of that treatment method is estimated and output. The following description will focus on the differences from the first embodiment, and the points common to the first embodiment will not be explained. In the description of the second embodiment, the same reference numerals will be used for parts that are the same as in the first embodiment.
[0065] Figure 11 is a flowchart showing a series of processing steps of the processing circuit 120 according to the second embodiment. First, the acquisition function 121 acquires attribute factors of the target patient P1 and information representing the treatment method selected by the medical professional P2 from the terminal device 10 via the communication interface 110 (step S200).
[0066] For example, the output control function 22 of the terminal device 10 displays multiple treatment methods applicable to the target patient P1 on the display 13a. A medical professional P2 (especially a doctor) selects one or more treatment methods from the multiple treatment methods displayed on the display 13a and inputs the selection result to the input interface 12. The communication control function 23 transmits the selection result of the treatment method input by the medical professional P2 to the input interface 12 to the medical information processing device 100 via the communication interface 11. In response, the acquisition function 121 of the medical information processing device 100 acquires the selection result of the treatment method selected by the medical professional P2 from the terminal device 10 via the communication interface 110.
[0067] Next, the estimation function 122 calculates an attribute distribution that quantitatively represents the attribute factors of the target patient P1 obtained by the acquisition function 121 (step S202). The attribute distribution of the target patient P1 is an example of the "first distribution".
[0068] For example, the estimation function 122 converts each of the multiple attribute factors of the target patient P1 into quantitative values according to guidelines established by administrative agencies or medical standards established by each medical institution, and calculates the quantified attribute factors as a distribution. The estimation function 122 may quantify the attributes of the target patient P1 using a predetermined database, or it may quantify the attributes of the target patient P1 using machine learning (deep learning, etc.).
[0069] For example, the attribute distribution may be represented as a radar chart with the degree of each attribute factor graded on a scale of 0 to 5. In other words, the values of each attribute factor may be normalized so that the minimum value is 0 and the maximum value is 5, and then represented as a distribution like a radar chart.
[0070] Furthermore, attribute distribution is not limited to radar charts. For example, attribute distribution may be represented by other statistical charts such as histograms, stacked bar graphs, or heatmaps. Also, the number of attribute levels is not limited to 5; it may be 4 or less, or 6 or more.
[0071] Next, the estimation function 122 filters the population to include various patients based on the treatment method selection results obtained by the acquisition function 121 (step S204).
[0072] The population may consist, for example, of a healthcare professional P2 who is treating a target patient P1, or of multiple patients who have previously received treatment at the healthcare institution where that healthcare professional P2 works. A healthcare institution is, for example, a hospital, clinic, or other facility that provides medical care. The population may also be a patient population in medical statistics. The population can be stratified (classified, grouped, or clustered) into multiple groups based on a type of outcome, such as a hospital Performance Index (PI) and / or a disease Quality Index (QI).
[0073] Hospital PIs are indicators of the time or economic costs incurred by each patient in a population, such as length of hospital stay and treatment costs. From another perspective, hospital PIs are indicators of the time or economic costs incurred by the healthcare institution, such as length of hospital stay and medical fees.
[0074] A disease quality indicator (QI) is an index used to measure the effectiveness of treatment, specifically how well a patient's disease was treated when treated according to a particular treatment method in a given population. For example, if a patient's disease is cancer, disease QI could be the 5-year survival rate, length of hospital stay after surgery, recurrence rate, cancer survival rate, or the percentage of breast-conserving surgeries. If a patient's disease is acute myocardial infarction, disease QI could be the average length of hospital stay. If a patient's disease is diabetes, disease QI could be the HbA1c (Hemoglobin A1c) improvement rate, the number of patient referrals, or the number of patient reverse referrals. If a patient's disease is pneumonia, disease QI could be the average length of hospital stay or the initial treatment success rate.
[0075] For example, if the treatment method selected by healthcare professional P2 is "AAA", the estimation function 122 will extract multiple patients from the population who have previously received the "AAA" treatment method.
[0076] Next, the estimation function 122 stratifies multiple patients (hereinafter referred to as the sample) extracted from the population based on their treatment methods into multiple groups, and calculates the attribute distribution of each stratified group (step S206). The attribute distribution of each group is an example of a "second distribution".
[0077] First, the estimation function 122 selects one or more indicator values related to the treatment method selected by healthcare professional P2 from among several indicator values for measuring treatment effectiveness as the hospital PI and / or disease QI in order to calculate the attribute distribution of each group.
[0078] The estimation function 122 then stratifies the sample into multiple groups based on the selected hospital PI and / or disease QI, and calculates the attribute distribution of each group. For example, if the treatment method "AAA" is a cancer-specific treatment method, the estimation function 122 selects indicator values such as "recurrence rate" and "cancer survival rate" as disease QIs related to cancer, and stratifies the sample into multiple groups based on those cancer-related disease QIs.
[0079] Note that the estimation function 122 may perform the stratification process at a different time than shown in this flowchart. This different time could be, for example, on a day when the medical institution is closed or at night when there are relatively few patients to treat. In other words, when the processing of this flowchart begins, the sample may already be stratified into multiple groups. In this case, the estimation function 122 only needs to select from the already stratified groups the group that has the same disease as target patient P1 and has been treated in the past with the same treatment method that is planned to be applied to target patient P1.
[0080] Figure 12 is a diagram illustrating the stratification process. As shown in the figure, for example, the estimation function 122 calculates the probability density distribution F(X) of the population when the hospital PI and disease QI are random variables X. Then, the estimation function 122 stratifies the population into multiple groups on the probability density distribution F(X) according to a certain criterion.
[0081] For example, the estimation function 122 may classify groups where the random variable X is less than the second threshold TH2 into group A, groups where the random variable X is greater than or equal to the second threshold TH2 and less than the first threshold TH1 into group B, and groups where the random variable X is greater than or equal to the first threshold TH1 into group C. The first threshold TH1 and the second threshold TH2 may be fixed values determined based on, for example, medical statistical results or guidelines, or they may be reference values to which a certain margin has been added or subtracted from the national average or the average within each medical institution. The first threshold TH1 and the second threshold TH2 may also be standard deviations such as ±1σ, ±2σ, or ±3σ. There are not limited to two thresholds; there may be one or more. In other words, there may be two groups or more than four.
[0082] For example, if the estimation function 122 selects cancer recurrence rate as the disease QI, it filters out cancer patients and patients to whom cancer treatment methods have been applied from the population, and calculates a probability density distribution F(X) where the cancer recurrence rate of the extracted set of patients (i.e., the sample group) is the random variable X. Then, the estimation function 122 stratifies the population on the probability density distribution F(X) for cancer recurrence rate into three groups, for example, A, B, and C. In this case, group A is the group with a low cancer recurrence rate, group B is the group with a higher cancer recurrence rate than group A, and group C is the group with a higher cancer recurrence rate than group B. In other words, group A is the group whose outcome improved the most after treatment, group B is the group whose outcome improved the next most, and group C is the group whose outcome did not improve the least.
[0083] Estimation function 122 calculates the attribute distribution of each group when the population is stratified into multiple groups. For example, estimation function 122 averages the attribute distributions of multiple patients included in each group and uses that averaged attribute distribution as the attribute distribution of each group. Specifically, if group A includes 100 patients, estimation function 122 averages the attribute distributions of each of those 100 patients and uses the single attribute distribution obtained by averaging the attribute distributions of those 100 patients as the attribute distribution of group A. Estimation function 122 can similarly calculate the attribute distribution of other groups such as group B and group C by averaging the attribute distributions of multiple patients.
[0084] Returning to the flowchart explanation, next, the estimation function 122 compares the attribute distribution of the target patient P1 with the attribute distribution of each group (step S208).
[0085] Figure 13 is a diagram illustrating how attribute distributions are compared. For example, suppose there are attribute distributions for three groups: Group A, Group B, and Group C. In this case, the estimation function 122 compares the attribute distribution of target patient P1 with the attribute distributions of Group A, Group B, and Group C, and calculates the similarity between the attribute distributions.
[0086] For example, if the attribute distribution is a chart where the shape is distinctive, like a radar chart, the estimation function 122 calculates the similarity between the geometric shape of the attribute distribution of target patient P1 and the geometric shape of the attribute distribution of group A as similarity 'a'. Similarly, the estimation function 122 calculates the similarity between the geometric shape of the attribute distribution of target patient P1 and the geometric shape of the attribute distribution of group B as similarity 'b', and the similarity between the geometric shape of the attribute distribution of target patient P1 and the geometric shape of the attribute distribution of group C as similarity 'c'. The closer the two attribute distributions being compared are to similar shapes, the greater the similarity. Furthermore, if the attribute distribution is a chart where the shape is distinctive, like a heatmap, the estimation function 122 may calculate the similarity by using the distance between the colors or shades (so-called color difference) of the two attribute distributions being compared. Specifically, the estimation function 122 may calculate a color histogram for each attribute distribution and calculate the similarity between two attribute distributions from the Euclidean distance or cosine similarity of the color histograms.
[0087] Returning to the flowchart explanation, the estimation function 122 then estimates at least one of the hospital PI and disease QI indicators, or both, for patient P1 based on the comparison of the attribute distribution of patient P1 with the attribute distribution of each group (step S210).
[0088] For example, the estimation function 122 estimates the hospital PI used when stratifying the group with the greatest similarity in attribute distribution among multiple groups compared with the attribute distribution of the target patient P1 as the hospital PI for the target patient P1, and estimates the disease QI used when stratifying the group with the greatest similarity in attribute distribution as the disease QI for the target patient P1.
[0089] For example, suppose that in Figure 13, similarity b is the largest. In this case, the estimation function 122 estimates the cancer recurrence rate used when stratifying group B from the population as the disease QI for patient P1. Group B is the group whose cancer recurrence rate is above the second threshold TH2 and below the first threshold TH1. Therefore, the estimated value for the cancer recurrence rate of patient P1 will be in the range from the second threshold TH2 to the first threshold TH1.
[0090] Similarly, if "treatment cost" was used as the hospital PI when stratifying Group B from the population, the estimation function 122 estimates the "treatment cost" of Group B used as the hospital PI as the hospital PI for target patient P1. Group B is also the group whose "treatment cost" is greater than or equal to the second threshold TH2 and less than the first threshold TH1. Therefore, the "treatment cost" that can be charged to target patient P1 when the treatment method selected in processing S200 is applied to target patient P1 is estimated to be in the range from the second threshold TH2 to the first threshold TH1.
[0091] Next, the estimation function 122 determines whether the estimated treatment cost for the patient P1 is within the range covered by insurance (step S212).
[0092] If the estimated treatment cost for patient P1 is outside the range of the amount covered by insurance, the estimation function 122 selects a treatment method that has not been selected before as a new treatment method (step S214) and returns to S204. This allows the system to re-estimate the "treatment cost" that could be charged to patient P1 if the re-selected treatment method were applied to patient P1.
[0093] On the other hand, if the estimated treatment cost for patient P1 is within the range covered by insurance, the output control function 124 outputs the hospital PI (e.g., treatment cost) and disease QI (e.g., survival rate) for patient P1 estimated in the S210 process, along with the treatment method assumed to be applied to patient P1 when estimating these indicator values, via the output interface 113 (step S216). The output control function 124 may also transmit this information to the terminal device 10 via the communication interface 111. This completes the processing of this flowchart.
[0094] According to the second embodiment described above, the medical information processing device 100 filters the population by the treatment method selected by the medical professional P2, and estimates the hospital PI and / or disease QI of the target patient P1 based on the comparison result between the attribute distribution of the stratified sample group and the attribute distribution of the target patient P1. Furthermore, the medical information processing device 100 determines whether the treatment cost of the target patient P1, which is estimated as the hospital PI, is within the range of the amount covered by insurance, and outputs the hospital PI, disease QI, and treatment method if the treatment cost is within the range of the amount covered by insurance. This allows the patient and their family to select a treatment method that is less financially burdensome.
[0095] (Other embodiments) Other embodiments will be described below. In the embodiments described above, the terminal device 10 and the medical information processing device 100 were described as separate devices, but the invention is not limited to this. For example, the terminal device 10 and the medical information processing device 100 may be a single integrated device. For example, the processing circuit 20 of the terminal device 10 may, in addition to the acquisition function 21, output control function 22, and communication control function 23, further include the estimation function 122 and identification function 123 provided by the processing circuit 120 of the medical information processing device 100. In this case, the terminal device 10 can perform the various flowchart processing described above in a standalone (offline) manner.
[0096] Furthermore, although the flowchart processes in Figures 4 and 11 have been described as being performed solely by the medical information processing device 100, this is not limited to that. For example, some of the processes in these flowcharts may be performed by the terminal device 10.
[0097] While several embodiments 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]
[0098] 1...Medical information processing system, 10...Terminal device, 11...Communication interface, 12...Input interface, 13...Output interface, 14...Memory, 20...Processing circuit, 21...Acquisition function, 22...Output control function, 23...Communication control function, 100...Medical information processing device, 111...Communication interface, 112...Input interface, 113...Output interface, 114...Memory, 120...Processing circuit, 121...Acquisition function, 122...Estimation function, 123...Identification function, 124...Output control function
Claims
1. An acquisition unit that acquires multiple attribute factors related to the disease of the target patient, A selection unit identifies related attribute factors among the aforementioned multiple attribute factors that are highly associated with the disease, A medical information processing device comprising: an output control unit that outputs information based on the aforementioned related attribute factors via an output interface;
2. The identifying unit identifies attribute factors as related attribute factors that have a higher degree of influence on at least one of the selection of treatment methods for the disease and the improvement of the outcome of the target patient compared to other attribute factors. The medical information processing device according to claim 1.
3. The specified part is, Each of the aforementioned attribute factors is compared with the reference values established by guidelines or evidence. Among the aforementioned multiple attribute factors, those attribute factors whose deviation from the reference value is greater than or equal to a threshold are identified as the relevant attribute factors. The medical information processing device according to claim 1 or 2.
4. The specified part is, Each of the aforementioned attribute factors is compared with a second attribute factor, which is the attribute factor of another patient group in which the outcome was improved. Among the multiple attribute factors, the attribute factor whose deviation from the second attribute factor is greater than or equal to a threshold is identified as the related attribute factor. The medical information processing device according to claim 1 or 2.
5. The system further includes an estimation unit that estimates the outcome of the target patient based on the attribute factors of the target patient. The medical information processing device according to claim 1 or 2.
6. The estimation unit, A first distribution quantitatively representing the attribute factors of the aforementioned target patients was calculated. Based on the outcomes of other patient groups that have received treatment in the past, a second distribution is calculated that quantitatively represents the attribute factors of each of the multiple groups stratified from the aforementioned other patient groups. Based on the comparison results of the first distribution and the second distribution, the outcome of the target patient is estimated. The medical information processing device according to claim 5.
7. The aforementioned outcome includes the cost of treatment for the treatment method to be applied to the aforementioned patient. The estimation unit determines whether the estimated treatment cost, which is the outcome for the target patient, is within the range of the amount covered by insurance. The output control unit outputs the patient's outcome and the treatment method to be applied to the patient via the output interface if the treatment cost is within the range of the amount covered by insurance. The medical information processing device according to claim 6.
8. A medical information processing method performed by a computer, To obtain multiple attribute factors related to the disease of the target patient, Among the aforementioned multiple attribute factors, identify the relevant attribute factors that are highly associated with the disease. To output information based on the aforementioned related attribute factors via the output interface. A medical information processing method including [the specified term].
9. A program to be executed by a computer, To obtain multiple attribute factors related to the disease of the target patient, Among the aforementioned multiple attribute factors, identify the relevant attribute factors that are highly associated with the disease. To output information based on the aforementioned related attribute factors via the output interface. A program that includes this.