Information processing apparatus, information processing method, and program

Abstract

To implement an information processing apparatus capable of presenting an analysis result related to an index of a medical institution in an easily understandable manner.SOLUTION: An information processing apparatus includes: first acquisition means configured to acquire index information including a plurality of indexes related to a target medical institution; second acquisition means configured to acquire an analysis result output by an analysis model that has referred to at least a part of the index information; third acquisition means configured to acquire explanatory information regarding at least a part of the analysis result, the explanatory information being information output from a generation model that has been subjected to machine learning and has referred to at least a part of the analysis result; and generation means configured to generate output data including at least a part of the analysis result and at least a part of the explanatory information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] There are known technologies for supporting the management of medical institutions. For example, Patent Document 1 discloses a hospital management evaluation support system that calculates costs per labor cost based on revenue data and expense data, and outputs hospital management evaluation data that includes the relationship between RMP and hospital management index data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-218448 Summary of the Invention [Problem to be solved by the invention]

[0004] The analysis results obtained by a system such as that described in Patent Document 1 are highly specialized, and are often difficult to understand unless you are an expert, or require a long time to understand.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and an exemplary purpose thereof is to provide a technology for presenting analysis results relating to indicators of medical institutions in an easy-to-understand manner. [Means for solving the problem]

[0006] An information processing device according to an exemplary aspect of the present disclosure includes a first acquisition means for acquiring index information including a plurality of indexes related to a target medical institution, a second acquisition means for acquiring an analysis result output by an analytical model that references at least a portion of the index information, a third acquisition means for acquiring explanatory information related to at least a portion of the analysis result, which is information output by a machine-learned generative model that references at least a portion of the analysis result, and a generation means for generating output data including at least a portion of the analysis result and at least a portion of the explanatory information.

[0007] An information processing method according to an exemplary aspect of the present disclosure includes acquiring index information including a plurality of indexes related to a target medical institution, acquiring analysis results output by an analytical model that references at least a portion of the index information, acquiring explanatory information related to at least a portion of the analysis results output by a machine-learned generative model that references at least a portion of the analysis results, and generating output data including at least a portion of the analysis results and at least a portion of the explanatory information.

[0008] A program according to an exemplary aspect of the present disclosure is a program that causes a computer to function as an information processing device, and causes the computer to function as a first acquisition means that acquires index information including a plurality of indexes related to a target medical institution, a second acquisition means that acquires analysis results output by an analytical model that references at least a portion of the index information, a third acquisition means that acquires explanatory information related to at least a portion of the analysis results, which is information output by a machine-learned generative model that references at least a portion of the analysis results, and a generation means that generates output data that includes at least a portion of the analysis results and at least a portion of the explanatory information. [Effects of the Invention]

[0009] According to an exemplary aspect of the present disclosure, an exemplary effect is achieved in that a technology can be provided that presents analysis results related to indicators of a medical institution in an easy-to-understand manner. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 2] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 3] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 4] FIG. 1 is a sequence diagram showing a processing flow in an information processing system according to the present disclosure. [Figure 5] FIG. 1 is a diagram for explaining information processing according to the present disclosure. [Figure 6] FIG. 1 is a diagram for explaining information processing according to the present disclosure. [Figure 7] FIG. 1 is a diagram for explaining information processing according to the present disclosure. [Figure 8] FIG. 1 is a diagram for explaining information processing according to the present disclosure. [Figure 9] FIG. 1 is a diagram for explaining information processing according to the present disclosure. [Figure 10] FIG. 1 is a block diagram illustrating a configuration of a computer that functions as an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technologies (part or all of the products or methods) employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the technologies employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in the exemplary embodiments, and do not define the scope of the present invention. In other words, embodiments that do not exhibit the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention.

[0012] [First embodiment] A first exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is the basic form of each exemplary embodiment described later. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technology shown in the drawings referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0013] (Configuration of information processing device 1) The configuration of an information processing device 1 according to this exemplary embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the information processing device 1. As shown in Fig. 1, the information processing device 1 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13, and a generation unit 14.

[0014] (First acquisition unit 11) The first acquisition unit 11 acquires index information including multiple indexes related to a target medical institution. Here, the target medical institution may be one medical institution or multiple medical institutions. Examples of medical institutions include hospitals, clinics, midwifery clinics, nursing homes, visiting nursing stations, and pharmacies, but these examples do not limit the present exemplary embodiment.

[0015] The "index" is not particularly limited as long as it is data that can be analyzed by the analytical model described below, but may include, as an example, indexes related to the management of a medical institution (also called management indexes). The "index" may also include data related to medical expenses, data included in electronic medical records, and other reference information, but these examples do not limit the present exemplary embodiment.

[0016] (Second acquisition unit 12) The second acquisition unit 12 acquires an analysis result output by an analysis model that references at least a part of the index information acquired by the first acquisition unit 11. As an example, the second acquisition unit 12 generating input data to be input into an analytical model by referring to the indicator information; · Input the generated input data into the analytical model, Obtain the analysis results output by the analysis model Alternatively, the second acquisition unit 12 may be expressed as generating an analysis result by inputting at least a part of the index information acquired by the first acquisition unit 11 into an analytical model. Here, the analytical model may be configured to be provided in the second acquisition unit 12 or the information processing device 1, or may be configured to be provided in a server device or the like external to the information processing device 1. Furthermore, a machine-learned analytical model can be used as the analytical model. Note that the specific processing content of the analytical model does not limit this exemplary embodiment, but as an example, Data preprocessing Feature design Data post-processing Here, feature engineering may include: ·Construction of feature space Feature extraction Feature verification The verification of the feature quantity may include the following processes: -Verification of correlations between multiple features In such a configuration, the analysis result may include, for example, a plurality of feature quantities extracted by the analysis model and information on the correlation between the plurality of feature quantities. Here, the plurality of feature quantities are Any of the multiple indicators included in the indicator information described above, or - Indicators obtained by combining multiple indicators included in the indicator information above Therefore, the above analysis results include One or more target indices obtained from the index information that are the subject of analysis; One or more indexes obtained from the index information, which are factor indexes that are factors of the target index; It may be expressed as including information about the correlation between the

[0017] (Third Acquisition Unit 13) The third acquisition unit 13 acquires explanatory information relating to at least a part of the analysis result, which is information output by a machine-learned generative model that references at least a part of the analysis result acquired by the second acquisition unit 12. As an example, the third acquisition unit 13 generating a prompt by referring to a portion of the analysis results; The generated prompts are input into the generative model, Obtain the results generated by the generative model Alternatively, the third acquisition unit 13 may be expressed as generating explanatory information by inputting at least a part of the analysis results acquired by the second acquisition unit 12 into a machine-learned generative model. Here, the generative model may be configured to be included in the third acquisition unit 13 or the information processing device 1, or may be configured to be included in a server device or the like external to the information processing device 1. Furthermore, a large-scale language model that has been machine-learned can be used as the generative model.

[0018] In addition, the generated results generated by the generative model include, for example: explanatory information for explaining at least part of the analysis results; and A prediction model for performing predictive processing based on the analysis results At least one of the following may be included. The specific example of the explanation information does not limit the present exemplary embodiment, but includes, as an example, information for explaining one or more feature quantities (in other words, target indicators or factor indicators) included in the analysis results. For example, - Information to explain one or more feature values ​​contained in the analysis results in a more understandable way A more specific definition of one or more features included in the analysis results etc.

[0019] (Generation unit 14) The generation unit 14 generates output data including at least a part of the analysis results acquired by the second acquisition unit 12 and at least a part of the explanation information acquired by the third acquisition unit 13. The generated output data is presented to the user via, for example, an input / output unit (not shown) or the like.

[0020] As an example, the user may include a person related to the medical institution in question (manager, accounting manager, or medical professional (doctor, nurse, etc.)), or may include an administrator (operator) of the information processing device 1.

[0021] (Effects of information processing device 1) As described above, in the information processing device 1, Obtain indicator information including multiple indicators for the target medical institution, Acquire an analysis result output by an analysis model that references at least a part of the index information, Acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; In this way, the output data generated by the information processing device 1 includes at least a part of the analysis results and at least a part of the explanation information, so that the analysis results of the indicators related to the target medical institution can be presented to the user in an easy-to-understand manner.

[0022] (Flow of information processing method S1) Next, the flow of information processing method S1 according to this exemplary embodiment will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of information processing method S1. As shown in Fig. 2, information processing method S1 includes a step (process) S11 of acquiring index information, a step (process) S12 of acquiring analysis results, a step (process) S13 of acquiring explanation information, and a step (process) S14 of generating output data.

[0023] (Step S11) In step S11, the first acquisition unit 11 acquires index information including a plurality of indexes related to the target medical institution. A more specific description of the first acquisition unit 11 has been given above, and therefore will not be repeated here.

[0024] (Step S12) In step S12, the second acquisition unit 12 acquires the analysis results output by the analysis model that references at least a part of the index information acquired by the first acquisition unit 11. A more specific description of the second acquisition unit 12 has been given above, and therefore will not be repeated here.

[0025] (Step S13) In step S13, the third acquisition unit 13 acquires explanatory information regarding at least a portion of the analysis results, which is information output by a machine-learned generative model that references at least a portion of the analysis results acquired by the second acquisition unit 12. A more specific description of the third acquisition unit 13 has been given above, and therefore will not be repeated here.

[0026] (Step S14) In step S14, the generation unit 14 generates output data including at least a part of the analysis results acquired by the second acquisition unit 12 and at least a part of the explanation information acquired by the third acquisition unit 13. A more specific description of the generation unit 14 has been given above, and therefore will not be repeated here.

[0027] (Effect of information processing method S1) As described above, in the information processing method S1, Obtain indicator information including multiple indicators for the target medical institution, Acquire an analysis result output by an analysis model that references at least a part of the index information, Acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; According to the above configuration, the same effects as those of the information processing device 1 are achieved.

[0028] Second Embodiment A second exemplary embodiment, which is one example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs. Furthermore, each technology shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs.

[0029] (Configuration of information processing system 1A) The configuration of an information processing system 1A according to this exemplary embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the information processing system 1A. As shown in Fig. 3, the information processing system 1A includes an information processing device 100, and a first server device 50 and a second server device 60 connected to the information processing device 100 via a network N. Here, the specific configuration of the network N does not limit this exemplary embodiment, but as an example, a wireless LAN (Local Area Network), a wired LAN, a WAN (Wide Area Network), a public line network, a mobile data communication network, or a combination of these networks can be used.

[0030] (First server device 50) As shown in FIG. 3 , the first server device 50 includes a control unit 51, a storage unit 52, and a communication unit 53. The communication unit 53 communicates with devices external to the first server device 50. As an example, the communication unit 53 communicates with an information processing device 100 included in the information processing system 1A. The communication unit 53 transmits data supplied from the control unit 51 to the information processing device 100, and supplies data received from the information processing device 100 to the control unit 51. Note that the data received by the communication unit 53 from the information processing device 100 may include input data generated by the information processing device 100. Furthermore, the data provided by the communication unit 53 to the information processing device 100 may include a result (analysis result) of an analysis of the input data by an analysis model AM described later.

[0031] An analytical model AM is stored in the storage unit 52. As an example, a plurality of parameters that define the analytical model AM are stored in the storage unit 52. As an example, these parameters are parameters that have been learned in advance by machine learning (parameters that have undergone an update process by machine learning), but this does not limit the present exemplary embodiment.

[0032] The control unit 51 uses the analysis model AM to obtain the analysis results of the analysis model AM. As an example, the control unit 51 inputs input data received from the information processing device 100, the input data including the index information obtained by the first obtaining unit 11 described above, into the analysis model AM, and obtains the analysis results of the input data generated by the analysis model AM. The control unit 51 also provides the analysis results to the information processing device 100 via the communication unit 53. Specific processing by the analysis model AM will be described later.

[0033] (Second server device 60) As shown in FIG. 3 , the second server device 60 includes a control unit 61, a storage unit 62, and a communication unit 63. The communication unit 63 communicates with devices external to the second server device 60. As an example, the communication unit 63 communicates with an information processing device 100 included in the information processing system 1A. The communication unit 63 transmits data supplied from the control unit 61 to the information processing device 100, and supplies data received from the information processing device 100 to the control unit 61. Note that the data received by the communication unit 63 from the information processing device 100 may include a prompt generated by the information processing device 100. Furthermore, the data provided by the communication unit 63 to the information processing device 100 may include a generation result generated by a generative model GM (described later) based on the prompt.

[0034] The storage unit 62 stores a generative model GM. As an example, the storage unit 62 stores a plurality of parameters that define the generative model AM. These parameters are, as an example, parameters that have been learned in advance by machine learning (parameters that have undergone an update process by machine learning), but this does not limit the present exemplary embodiment. A large-scale language model learned by machine learning can be used as the generative model GM.

[0035] The control unit 61 acquires information generated by the generative model GM by using the generative model GM. As an example, the control unit 61 acquires explanatory information generated by the generative model GM based on a prompt received from the information processing device 100, the prompt including the analysis result by the analytical model AM. The control unit 61 also provides the explanatory information to the information processing device 100 via the communication unit 63. Specific processing by the generative model GM will be described later. Note that, in the present exemplary embodiment, the first server device 50 and the second server device 60 are illustrated as devices separate from the information processing device 100, but this does not limit the present exemplary embodiment. The control unit 51 included in the first server device 50 or the function of the analytical model execution unit in the control unit 51 may be configured to be included in the control unit of the information processing device 100. The control unit 61 included in the second server device 60 or the function of the generative model execution unit in the control unit 61 may be configured to be included in the control unit of the information processing device 100. Similarly, the analytical model AM stored in the memory unit 52 of the first server device 50 may be stored in the memory unit of the information processing device 100, and the analytical model AM may be executed by the information processing device 100 itself. Also, the generative model GM stored in the memory unit 62 of the second server device 60 may be stored in the memory unit of the information processing device 100, and the generative model GM may be executed by the information processing device 100 itself.

[0036] (Configuration of information processing device 100) Next, the configuration of the information processing device 100 according to this exemplary embodiment will be described with reference to Fig. 3. As shown in Fig. 3, the information processing device 100 includes a control unit 10, a storage unit 20, a communication unit 30, and an input / output unit 40.

[0037] (Communication unit 30) The communication unit 30 communicates with devices external to the information processing device 100. As an example, the communication unit 30 communicates with a first server device 50 and a second server device 60. The communication unit 30 transmits data supplied from the control unit 10 to the first server device 50 and the second server device 60, and supplies data received from the first server device 50 and the second server device 60 to the control unit 10. The data transmitted by the communication unit 30 to the first server device 50 may include input data generated by an input data generation unit 121 (described later). The data transmitted by the communication unit 30 to the second server device 60 may include a prompt generated by a prompt generation unit 131 (described later). The data received by the communication unit 30 from the first server device 50 may include an analysis result of the input data by the analytical model AM. The data received by the communication unit 30 from the second server device 60 may include a generation result based on the prompt by the generative model GM.

[0038] (Input / output section 40) The input / output unit 40 is configured to include at least one of input / output devices such as a keyboard, a mouse, a display, a printer, and a touch panel. Alternatively, the input / output unit 40 may be configured to have input / output devices such as a keyboard, a mouse, a display, a printer, and a touch panel connected to it. In this configuration, the input / output unit 40 accepts various types of information input to the information processing device 100 from the connected input devices. Furthermore, the input / output unit 40 outputs various types of information to connected output devices under the control of the control unit 10. An example of the input / output unit 40 is an interface such as a USB (Universal Serial Bus).

[0039] (Storage unit 20) The storage unit 20 stores various data referenced by the control unit 10 and various data generated by the control unit 10. As an example, the storage unit 20 stores: ·Indicator information IND ·Analysis results AR Prompt PR ·Explanatory Information EI Output data OUT The index information IND contains a plurality of indexes relating to one or more medical institutions. For example, the index information IND may contain the following: -Indicators related to the management of the target medical institution (also called management indicators) Data on medical fees at target medical institutions Data contained in the electronic medical records of the medical institution in question, and Other references from the medical institution in question Here, the management indicators may include, for example, indicators such as the average length of hospital stay, the bed occupancy rate, and the number of referred patients, but the present exemplary embodiment is not limited to these examples.

[0040] The medical fee data may be, for example, part of DPC (Diagnosis Procedure Combination) data. The reference information may include various information that can be acquired from the medical administration system or various department systems of the medical institution. Also, as mentioned in exemplary embodiment 1, medical institutions may include, by way of example, hospitals, clinics, midwifery clinics, nursing homes, visiting nursing stations, pharmacies, etc., but these examples do not limit this exemplary embodiment.

[0041] The analysis result AR is an analysis result output by the analysis model AM that refers to at least a part of the index information IND, and an example thereof is: one or more target indicators TI obtained from the indicator information IND and which are the subject of analysis; one or more indexes obtained from the index information IND, which are factor indexes RI that are factors of the target index TI; and -Information on the correlation between the target indicator TI and the factor indicator RI Specific examples of the analysis results AR will be described later.

[0042] The prompt PR is generated by a prompt generating unit 131 included in the third acquisition unit 13, which will be described later, and is input to the generative model GM. Specific examples of the prompt PR will be described later.

[0043] The explanatory information EI is explanatory information generated by the generative model GM, and is explanatory information for explaining at least a part of the content of the analysis result AR. As mentioned in the first exemplary embodiment, the explanatory information EI is - Information to explain one or more features (indicators) included in the analysis results AR in a more understandable way A more specific definition of one or more features (indicators) included in the analysis results AR etc. Specific examples of the explanatory information EI will be described later.

[0044] The output data OUT is data generated by the output data generation unit 14, which will be described later, and includes, for example, at least a portion of the analysis result AR and at least a portion of the explanatory information EI. For example, the output data OUT is visually presented to the user via the input / output unit 40. Specific examples of the output data OUT will be described later.

[0045] (Control unit 10) As shown in FIG. 3, the control unit 10 includes a first acquisition unit 11, a second acquisition unit 12, a third acquisition unit 13, and an output data generation unit 14.

[0046] (First acquisition unit 11) The first acquisition unit 11 acquires index information IND including multiple indexes related to a target medical institution. Here, the target medical institution may be one medical institution or multiple medical institutions. Regarding the first acquisition unit 11, duplicated explanations of the contents already explained will be omitted.

[0047] (Second acquisition unit 12) The second acquisition unit 12 acquires an analysis result AR output by an analysis model AM that references at least a part of the index information IND acquired by the first acquisition unit 11. As shown in FIG. 3, the second acquisition unit 12 includes an input data generation unit 121 and an analysis result acquisition unit 122.

[0048] The input data generation unit 121 references the index information to generate input data to be input to the analysis model AM. Then, the input data generation unit 121 inputs the generated input data to the analysis model AM included in the first server device 50 via the communication unit 30. The analysis result acquisition unit 122 acquires the analysis results output by the analysis model AM.

[0049] The specific processing contents of the analysis model AM do not limit the present exemplary embodiment, but as an example, Data preprocessing Feature design Data post-processing Here, feature engineering may include: ·Construction of feature space Feature extraction Feature verification The verification of the feature quantity may include the following processes: -Verification of correlations between multiple features In such a configuration, the analysis result AR may include, for example, a plurality of feature quantities extracted from the input data by the analysis model AM and information on the correlation between the plurality of feature quantities. Here, these plurality of feature quantities are Any of the multiple indicators included in the indicator information described above, or - Indicators obtained by combining multiple indicators included in the indicator information above Therefore, the analysis result AR includes: One or more target indicators TI obtained from the indicator information IND and which are to be analyzed; One or more indexes obtained from the index information IND, which are factor indexes RI that are factors of the target index TI; It may be expressed as including information about the correlation between the

[0050] Here, as an example, one or more target indicators TI may include: -Indicators related to patient admission and discharge; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients As a specific example, the one or more target indicators TI may include at least one of the average length of hospital stay, the bed occupancy rate, and the number of referred patients.

[0051] As an example, one or more factor indicators RI may be indicators related to at least one of the doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the indicator information IND.

[0052] At least a part of the target indicators TI and factor indicators RI may include indicators designated by the user. Determine at least a portion of the target indicator TI and the factor indicator RI based on input from the user; Instruct the analytical model AM to generate an analytical result AR that includes at least a part of the target indicator TI and the factor indicator RI determined based on the input from the user. The above configuration may also be used.

[0053] After the analysis result acquisition unit 122 acquires the analysis result AR1 based on the first input data, the input data generation unit 121 may further execute post-processing (post-analysis) with reference to the analysis result AR1. Then, as one example, the input data generation unit 121 may generate second input data based on the results of the post-processing and input it again into the analysis model AM, and the analysis result acquisition unit 122 may acquire the analysis result AR2 based on the second input data.

[0054] (Third Acquisition Unit 13) The third acquisition unit 13 acquires explanatory information EI related to at least a part of the analysis result AR, which is information output by a machine-learned generative model GM that references at least a part of the analysis result AR acquired by the second acquisition unit 12. As shown in FIG. 3 , the third acquisition unit 13 includes a prompt generation unit 131 and an explanatory information acquisition unit 132.

[0055] The prompt generation unit 131 generates a prompt PR by referring to a part of the analysis result AR. Then, the prompt generation unit 131 inputs the generated prompt PR to the generative model GM provided in the second server device 60 via the communication unit 30. The explanation information acquisition unit 132 acquires the generation result generated by the generative model GM, which includes explanation information EI related to at least a part of the analysis result AR.

[0056] The generation results generated by the generative model may be configured to include a prediction model for performing prediction processing based on the analysis results. Also, the specific example of the explanation information EI does not limit this exemplary embodiment, but as an example, it includes information for explaining one or more feature quantities (in other words, target indicators TI or factor indicators RI) included in the analysis results AR. For example, - Information to explain one or more features included in the analysis result AR in a more understandable way A more specific definition of one or more features included in the analysis result AR etc.

[0057] The prompt generation unit 131 may generate a prompt PR to be input to the generative model GM by using a prompt template preset for each factor indicator RI. The prompt generation unit 131 may also generate the prompt PR by further referring to an input from a user. As an example, the prompt generation unit 131 Generate a first prompt PR1 using a prompt template preset for each of the factor indicators RI; Present the first prompt PR1 to the user via the input / output unit 40; generating a second prompt PR2 by modifying the first prompt PR1 based on input from a user; Input the second prompt PR2 into the generative model GM. The above configuration may also be used.

[0058] (Output data generation unit 14) The output data generation unit 14 generates output data including at least a portion of the analysis results acquired by the second acquisition unit 12 and at least a portion of the explanatory information acquired by the third acquisition unit 13. The generated output data is presented to a user via, for example, an input / output unit 40. Also, for example, the output data includes information for supporting decision-making regarding the management of the target medical institution.

[0059] (Flow of process S100 in information processing system 1A) Fig. 4 is a sequence diagram showing an example of the flow of processing S100 in the information processing system 1A. Note that Fig. 4 shows an example in which the analysis results by the analytical model AR provided in the first server device 50 are executed multiple times, but this does not limit the present exemplary embodiment.

[0060] (Step S11-1) In step S11-1, the first acquisition unit 11 included in the information processing device 100 acquires index information IND.

[0061] (Step S12-1) Next, in step S12-1, the input data generation unit 121 provided in the information processing device 100 refers to the index information IND to generate first input data to be input to the analysis model AM, the first input data including at least a part of the index information IND, and inputs the generated first input data to the analysis model AM via the communication unit 30.

[0062] (Step S21) In step S21, the analytical model AM executes an analysis on a plurality of indices included in the first input data, and provides the information processing device 100 with a first analysis result AR1.

[0063] (Step S12-2) In step S12-2, the analysis result acquisition unit 122 included in the information processing device 100 acquires the first analysis result AR1 output by the analysis model AM.

[0064] (Step S12-3) In step S12-3, the input data generation unit 121 performs post-processing by referring to the first analysis result AR1. Then, the input data generation unit 121 generates second input data to be input to the analysis model AM, the second input data being based on the results of the post-processing, and inputs the generated second input data to the analysis model AM via the communication unit 30.

[0065] (Step S22) In step S22, the analytical model AM executes an analysis on a plurality of indices included in the second input data, and provides the information processing device 100 with a second analysis result AR2.

[0066] (Step S12-4) In step S12-4, the analysis result acquisition unit 122 acquires the second analysis result AR2 output by the analysis model AM.

[0067] (Step S13-1) In step S13-1, the prompt generation unit 131 provided in the information processing device 100 generates a prompt PR to be input into the generative model GM by referring to the second analysis result AR2, and inputs the generated prompt PR into the generative model GM via the communication unit 30.

[0068] (Step S31) In step S31, the generative model GM executes the prompt PR and provides the explanation information EI to the information processing device 100.

[0069] (Step S13-2) In step S13-2, the explanation information acquisition unit 132 included in the information processing device 100 acquires the explanation information EI output by the generative model GM.

[0070] (Step S14-1) In step S14-1, the output data generation unit 14 included in the information processing device 100 generates output data OUT including the second analysis result AR2 and the explanation information EI.

[0071] (Step S14-2) In step S14-2, the output data generation unit 14 presents the output data OUT via the input / output unit 40.

[0072] (Processing example 1) The following describes a specific example of processing by the information processing device 100. Fig. 5 is a diagram showing an example of processing by the information processing device 100. In the example of Fig. 5, the flow of data in processing by the information processing device 100 is shown.

[0073] As shown in FIG. 5, in this example, the first input data IND1 generated by the input data generating unit 121 includes: List of management indicators for target medical institutions Data on medical fees at target medical institutions Data contained in the electronic medical records of the medical institution in question, and Other references from the medical institution in question As an example, the above management indicators include: Average length of stay Bed occupancy rate Referral rate These include indicators such as:

[0074] Here, as shown in FIG. 5, the information processing device 100, for example, inputs the first input data IND1 to the analytical model AM, and then: Obtaining analytical results AR from analytical models AM; · Perform post-processing (post-analysis) on the analysis results AR; Regenerating input data based on post-processing; and · Input the regenerated input data into the analysis model AM and obtain the analysis result AR again. By repeating the above process, the accuracy of the analysis of the target index may be improved.

[0075] In the post-processing (post-analysis) described above, as an example, data may be presented to the user, input may be received from the user, and input data to the analysis model AM may be generated reflecting the input from the user.

[0076] Furthermore, as an example, the information processing device 100, based on the analysis result AR acquired from the analysis model AM, - Generate prompts by referring to the analysis results AR, Inputting the generated prompt into the generative model GM to obtain explanatory information EI; and Generate output data OUT including the analysis results AR and explanatory information EI. may continue to be executed.

[0077] (Example of processing by the second acquisition unit 12) FIG. 6 is a diagram illustrating an example of processing by the second acquisition unit 12. In FIG. the analysis result AR based on the first input data IND1, Post-mortem analysis results for the analysis results AR, and Second input data IND2 Examples of each are shown.

[0078] In the example shown in the upper part of Fig. 6, "analysis results based on the first input data IND1" are shown as the analysis results AR by the analysis model AM acquired by the second acquisition unit 12. As shown in the upper part of Fig. 6, the "analysis results based on the first input data IND1" can be, for example, - "Average length of stay" in "XX medical department" as the target indicator TI Actual value for April 2024: "60" - The forecast value for May 2024 is "30" as predicted by the analytical model AM. In this way, the analysis results by the analytical model AM may include a prediction regarding the future of the target indicator TI or the factor indicator RI.

[0079] In the example shown in the middle of Fig. 6, the input data generation unit 121 included in the second acquisition unit 12 applies post-processing (post-analysis) to the "analysis results based on the first input data IND1." In the example shown in the middle of Fig. 6, the input data generation unit 121 calculates the difference between the "actual results" and the "prediction" included in the "analysis results based on the first input data IND1" as the post-processing (post-analysis). Then, "200%" is calculated as a value indicating the gap (GAP) between the "actual results" and the "prediction."

[0080] As an example, the input data generation unit 121 applies similar post-processing (post-analysis) to other target indices TI included in the "analysis results based on the first input data IND1" and calculates the deviation between "actual results" and "prediction" for each target indices. Then, the input data generation unit 121 identifies an index with a relatively large deviation or an index with a deviation larger than a predetermined threshold as an index to be further analyzed.

[0081] The lower part of Fig. 6 shows an example in which the input data generation unit 121 generates input data IND2 containing multiple candidate factor indicators RI that may be factors for the target indicator TI identified in this way, "average length of hospital stay". As an example, as shown in the lower part of Fig. 6, the input data generation unit 121 generates the following as candidates for the factor indicators RI: Average length of stay in hospital over the past three months Number of patients aged 70 or older in the past month Average number of days in orthopedic surgery in the past month The second input data IND2 including the feature quantities such as the above is generated and supplied to the analysis model AM again. Note that, as an example, such candidates for the factor indicators RI may be selected in the input data generation unit 121 with reference to the past analysis results AR by the analysis model AM. However, this example does not limit the present exemplary embodiment.

[0082] Then, the analytical model AM generates the following as an analysis result of the second input data IND2: Information showing the correlation between the target indicator TI, "average length of stay" in "XX medical department," and the candidate factor indicator RI, "average length of stay in hospital in the past three months." -Information showing the correlation between the above target indicator TI and the "number of patients aged 70 or older in the past month," which is a candidate for the factor indicator RI -Information showing the correlation between the above target indicator TI and the "average length of stay in orthopedics in the past month," which is a candidate for the factor indicator RI An analysis result AR including the above is generated, and the analysis result AR is acquired by the analysis result acquisition unit 122. Note that the above-mentioned term "candidates for factor index RI" does not limit the present exemplary embodiment, and may be simply expressed as "factor index RI."

[0083] (Processing example by the third acquisition unit 13) 7 is a diagram showing an example of processing by the third acquisition unit 13. In the example of FIG. 7, as shown in the upper part of the figure, the analysis result AR1 acquired by the second acquisition unit 12 includes the following: - As the target indicator, TI "Average length of stay" Contains · As a factor indicator RI for the target indicator TI, "The sum of coefficients by medical institution for records from -1 to 0 and with doctor code = 'aaaa'" "Average value of the act points for records from -1 to 0 and with prescription type code = 'bbbb'" Contains:

[0084] 7, the prompt generation unit 131 included in the third acquisition unit 13 references the analysis result AR1 and generates a prompt PR1. ·Instruction information PE1 Feature information F1 and F2 Additional information PE2 The instruction information PE1 indicates the content that the generative model GM should execute. In the example shown in the middle of FIG. 7, the instruction information PE1 includes: The features correlated with the target indicator TI, "average length of hospital stay," are as follows: Instructions to generate explanatory information The feature amount information F1 and F2 are factor indicators RI included in the above-mentioned analysis result AR1.

[0085] The additional information PE2 indicates matters to be referred to when the generative model GM generates explanatory information. In the example shown in the middle of FIG. 7, the additional information PE2 includes the following: The expression "-x month to 0 month" included in the features F1 and F2 (factor index RI) refers to the most recent x months. The additional information PE2 may differ for each factor index RI. A prompt template including additional information related to each of the factor indicators RI is generated in advance, -Select and use prompt templates according to the factor indicators RI included in the analysis results AR The above configuration may also be used.

[0086] The prompt PR1 generated in this way is input to the generative model GM via the communication unit 30. Then, the generative model GM generates explanatory information EI1 as shown in the lower part of Fig. 7 as an example, and the explanatory information acquisition unit 132 acquires the explanatory information EI1. In the example shown in the lower part of Fig. 7, the explanatory information EI1 includes -For the most recent month, among the data corresponding to physicians (aaaa), there is a correlation between the total coefficient by medical institution and the average length of stay (element EE1) - In the most recent month, there is a correlation between the average act score of records with receipt type code bbbb and the average length of hospital stay (element EE2). Contains:

[0087] (Example of processing by the output data generation unit 14) Fig. 8 shows an example of processing by the output data generation unit 14. In the example shown in Fig. 8, the output data OUT1 generated by the output data generation unit 14 includes, as explanation information EI, the elements EE1 and EE2 generated by the generative model GM. As shown in Figure 8, the output data OUT1 is presented as a proposal PS1 to the user based on the analysis result AR1. Implement measure C for doctors (aaaa) Implement measure d in relation to prescription type bbbb. Some of these proposals may be generated by the generative model GM, for example, or the output data generation unit 14 may generate them by referring to correspondence information in which the analysis results and the proposals are associated with each other. Note that these proposals are also an example of information for supporting decision-making regarding the management of the target medical institution.

[0088] 8, the output data OUT1 may include a suggestion to the user to encourage further analysis. In such a configuration, the first to third acquisition units 11 to 13 described above may perform the following operations based on the response from the user: - Re-acquisition of index information IND Regenerate input data and reacquire analysis results AR Regenerate prompt PR and reacquire explanatory information EI The output data generating unit 14 may be configured to perform the above processing, and regenerate the output data OUT based on the reacquired explanation information EI, and present it to the user.

[0089] The process performed by the output data generating unit 14 is not limited to the above example. As an example, the information processing device 100 performs the following process using any one of the first acquisition unit 11 to the third acquisition unit 13: Acquire actionable item information indicating actionable items for each medical institution and store it in the storage unit 20, When the output data generation unit 14 generates the output data OUT, it deletes proposals related to items that are difficult for the medical institution to handle by filtering with reference to the actionable item information, and generates output data OUT that includes proposals related to items that the medical institution can handle. The above configuration may also be used.

[0090] (Processing example 2) Next, another processing example by the information processing device 100 will be described with reference to Fig. 9. This processing example may be executed in combination with the above-described processing example 1, or may be executed separately from the above-described processing example 1. In the example shown in Fig. 9, the analysis result acquisition unit 122 obtains, from the analysis result AR2, the following as a factor index RI for a certain target index TI1: Elderly age Serious ○○ disease Severe dementia These features ("feature items" shown in Figure 9) are obtained and used as factor indicators RI for other target indicators TI2. Age: ○ or under In the example shown in FIG. 9, the analysis result AR2 includes the following: Score showing correlation between target indicator TI1 and factor indicator "elderly age": 0.98 Score showing correlation between target indicator TI1 and factor indicator "Serious XX disease": 0.95 Score showing correlation between target indicator TI1 and factor indicator "severe dementia": 0.89 Similarly, the analysis result AR2 includes the following scores (information on correlation): Score showing correlation between target indicator TI2 and factor indicator "Age below ○": 0.98 It contains each score (information on correlation) such as:

[0091] In this example, the prompt generation unit 131 generates a prompt PR2 including the analysis result AR2, - Instructions to generate explanatory information RI that explains the analysis results AR2, and - Instructions to create a forecast model based on the above analysis results AR2 A prompt PR2 including the above is generated, and the generated prompt PR2 is input to the generative model GM.

[0092] The generative model GM according to this example generates explanatory information RI that explains the analysis result AR2, and generates a prediction model PM based on the analysis result AR2. While the specific configuration of the prediction model PM is not limited to this example, it is, for example, a machine learning model trained using the analysis result AR2 as training data. Examples of such machine learning models include models using neural networks and regression models.

[0093] The output data generation unit 14 according to this example generates the generative model GM. ·Explanatory Information EI Predictive Model PM and outputs the generated output data OUT2 via the communication unit 30 or the input / output unit 40. Note that, in the above example, the prediction model PM is generated by the generation model GM, but this does not limit the present exemplary embodiment, and the prediction model PM may be generated by the analysis model AM.

[0094] (Additional notes regarding the indicator information IND) The index information IND to be processed by the information processing device 100 according to this exemplary embodiment may include multiple indexes other than those described above. Examples of such indexes are given below, but these examples do not limit this exemplary embodiment.

[0095] (Indicators related to hospitalization) a1 Number of treatment days (days) a2 Number of new patients (person / day) a3 Number of discharged patients (persons / day) a4 Number of patients per month (persons / month) a5 Number of patients by length of hospitalization a6 Number of patients per day (persons / day) a7 Average length of stay (average for all patients) a8 Average length of stay (average by department) a9 Bed occupancy rate (%) a10 Medical treatment cost (yen per person / day) a11 Cost rates for hospitalization (%) a12 Medical fee billed amount a13 Medical treatment cost (yen per person / day)

[0096] (Outpatient-related indicators) b1 Number of treatment days (days) b2 Total number of patients (persons / month) b3 Number of patients per day (persons / day) b4 First-time patient ratio (%) b5 Number of new patients (person / month) b6 Medical treatment cost (yen per person / day) b7 Outpatient costs (%) b8 Medical fee billed amount b9 Medical treatment cost (yen per person / day)

[0097] (Common indicators for inpatients and outpatients) c1 Number of referred patients (persons / month) c2 Referral rate (%) c3 Reverse referral rate (%) c4 Number of first-time patients (persons / month) c5 Number of surgeries (cases) c5-1 Gastrointestinal endoscopic surgery (cases) c5-2 Endoscopic examination (items) c5-3 Catheter surgery (cases) c5-4 Number of radiation treatments (cases) c6 Number of CT scans performed (cases) c6-1 (Hospitalization) Number of CT scans (cases) c6-2 (Outpatient) Number of CT scans performed (cases) c7 Number of PET-CT scans performed (cases) c7-1 (Hospitalization) Number of PET-CT scans performed (cases) c7-2 (Outpatient) Number of PET-CT scans performed

[0098] As described above, the factor index RI according to this exemplary embodiment may include at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information. Here, the doctor information may include, as an example, information associated with each doctor in each of the above-mentioned items. For example, the doctor information regarding Doctor A may include, among the above-mentioned items, the above-mentioned "a8 Average length of hospital stay (average by department)" and the like, which is based on the population of patients for whom Doctor A is the attending physician.

[0099] Furthermore, the time information may include, as an example, information related to time in each of the above-mentioned items. For example, the above-mentioned "a1 Number of days of treatment (days)", "a7 Average length of stay (average for all patients)", "a8 Average length of stay (average by department)", "b1 Number of days of treatment (days)", "a5 Number of patients per hospitalization period", etc. are examples of the time information. Furthermore, the hospitalization information may include, as an example, at least one of the above-mentioned (indicators related to hospitalization) (indicators common to inpatients and outpatients). Furthermore, the outpatient information may include, as an example, at least one of the above-mentioned (indicators related to outpatients) (indicators common to inpatients and outpatients).

[0100] The disease information may also include information about the type of disease each patient has, and may also include information related to a specific disease, such as "c5-4 Number of radiotherapy treatments" or "c7 Number of PET-CT scans" from among the above items.

[0101] The treatment information may also include information about what kind of treatment each patient has received (or is scheduled to receive), and may also include information related to specific treatments, such as "c5-4 Number of radiotherapy treatments" and "c7 Number of PET-CT scans" from among the above items.

[0102] The patient information may also include information related to each patient. For example, the patient information for patient B may include information about the disease that patient B has and the type of treatment that patient B has received (or is scheduled to receive). The patient information may also include, among the above-mentioned items, "a12 Medical fee claim amount" and "a13 Medical treatment unit price (yen per person / day)" related to the patient.

[0103] The medical information may include information about the medical treatment of each patient. For example, the medical information may include, among the items described above, "a1 Number of days of medical treatment (days)," "a12 Medical fee claim amount," "a13 Medical treatment unit price (yen per person / day)," etc.

[0104] As described above, the one or more target indices TI according to this exemplary embodiment include: -Indicators related to patient admission and discharge; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients Here, the "indicators related to patient admission and discharge" may include, as an example, at least one of the above-mentioned (indicators related to hospitalization) (indicators common to inpatients and outpatients). Furthermore, the "indicators related to hospital bed status" may include, as an example, "a9 bed occupancy rate (%)" from among the above-mentioned items. Furthermore, the "indicators related to costs incurred in providing medical care to patients" may include indices related to costs from among the above-mentioned items. For example, "a12 medical fee claim amount" and "a13 medical treatment unit price (yen / person / day)" are examples of the "indicators related to costs incurred in providing medical care to patients."

[0105] (Effects of Information Processing System 1A) As described above, in the information processing system 1A, Obtain indicator information including multiple indicators for the target medical institution, Acquire an analysis result output by an analysis model that references at least a part of the index information, Acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; In this way, the output data generated by the information processing system 1A includes at least a part of the analysis results and at least a part of the explanation information, so that the analysis results of the indicators related to the target medical institution can be presented to the user in an easy-to-understand manner.

[0106] [Software implementation example] Some or all of the functions of the information processing device 1,100 (hereinafter also referred to as "each of the above devices") may be realized by hardware such as an integrated circuit (IC chip), or by software.

[0107] In the latter case, each of the above devices is realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 10. Figure 10 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0108] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to operate as each of the above-mentioned devices. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing the functions of each of the above-mentioned devices.

[0109] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination thereof. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0110] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may also include a communication interface for transmitting and receiving data to and from other devices. The computer C may also include an input / output interface for connecting input / output devices such as a keyboard, mouse, display, and printer.

[0111] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can acquire the program P via such a recording medium M. The program P can also be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also acquire the program P via such a transmission medium.

[0112] Furthermore, the functions of each of the devices may be realized by a single processor provided in a single computer, by multiple processors provided in a single computer working in cooperation, or by multiple processors provided in each of multiple computers working in cooperation. Furthermore, the programs for causing each of the devices to realize the functions may be stored in a single memory provided in a single computer, or may be distributed and stored in multiple memories provided in a single computer, or may be distributed and stored in multiple memories provided in each of multiple computers.

[0113] [Appendix A] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0114] (Appendix A1) a first acquisition means for acquiring index information including a plurality of indexes related to the target medical institution; a second acquisition means for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition means for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generating means for generating output data including at least a part of the analysis result and at least a part of the explanatory information; An information processing device comprising:

[0115] (Appendix A2) The analysis results include: The information includes information on the correlation between one or more target indices obtained from the index information and one or more factor indices obtained from the index information. 10. The information processing device according to claim 1,

[0116] (Appendix A3) The third acquisition means a prompt generation means for generating a prompt to be input to the generative model by referring to at least a part of the analysis result; The information processing device according to appendix A2,

[0117] (Appendix A4) The prompt generation means generates a prompt to be input to the generation model using a prompt template that is preset for each of the factor indicators. 10. The information processing device according to claim 9, wherein the information processing device is a

[0118] (Appendix A5) The one or more factor indices are indices related to at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the index information. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing a plurality of data.

[0119] (Appendix A6) The one or more target indicators include: indicators related to patient admissions and discharges; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients At least one of the following is included: 10. The information processing device according to claim 9, wherein the information processing device is a device for processing a plurality of data.

[0120] (Appendix A7) The information processing device according to Appendix A6, wherein the one or more target indicators include at least one of an average length of hospital stay, a bed occupancy rate, and a number of referred patients.

[0121] (Appendix A8) The output data includes information for supporting decision-making regarding management of the target medical institution. 10. The information processing device according to claim 7,

[0122] [Appendix B] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0123] (Appendix B1) a first acquisition process in which at least one processor acquires index information including a plurality of indexes related to the target medical institution; a second acquisition process in which the at least one processor acquires an analysis result output by an analysis model that references at least a part of the index information; a third acquisition process in which the at least one processor acquires explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generation process in which the at least one processor generates output data including at least a portion of the analysis results and at least a portion of the explanatory information; An information processing method comprising:

[0124] (Appendix B2) The analysis results include: The information includes information on the correlation between one or more target indices obtained from the index information and one or more factor indices obtained from the index information. 1. The information processing method described in Appendix B1.

[0125] (Appendix B3) The third acquisition process includes: a prompt generation process in which the at least one processor generates a prompt to be input to the generative model by referring to at least a part of the analysis result; 2. An information processing method according to claim 1, comprising:

[0126] (Appendix B4) In the prompt generation process, the at least one processor generates a prompt to be input to the generation model using a prompt template that is preset for each of the factor indicators. The information processing method described in Appendix B3.

[0127] (Appendix B5) The one or more factor indices are indices related to at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the index information. 1. An information processing method according to any one of Appendices B2 to B4.

[0128] (Appendix B6) The one or more target indicators include: indicators related to patient admissions and discharges; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients At least one of the following is included: 1. An information processing method according to any one of Appendices B2 to B4.

[0129] (Appendix B7) An information processing method according to Appendix B6, wherein the one or more target indicators include at least one of average length of hospital stay, bed occupancy rate, and number of referred patients.

[0130] (Appendix B8) The output data includes information for supporting decision-making regarding management of the target medical institution. An information processing method as described in Appendix B7.

[0131] [Appendix C] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0132] (Appendix C1) A program that causes a computer to function as an information processing device, The computer a first acquisition means for acquiring index information including a plurality of indexes related to the target medical institution; a second acquisition means for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition means for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generating means for generating output data including at least a part of the analysis result and at least a part of the explanatory information; An information processing program that functions as a

[0133] (Appendix C2) The analysis results include: The information includes information on the correlation between one or more target indices obtained from the index information and one or more factor indices obtained from the index information. An information processing program as described in Appendix C1.

[0134] (Appendix C3) The computer The third acquisition means a prompt generation process for generating a prompt to be input to the generative model by referring to at least a part of the analysis results; 2. The information processing program according to claim 1, wherein the information processing program functions as

[0135] (Appendix C4) The prompt generation means generates a prompt to be input to the generation model using a prompt template that is preset for each of the factor indicators. An information processing program as described in Appendix C3.

[0136] (Appendix C5) The one or more factor indices are indices related to at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the index information. An information processing program according to any one of appendices C2 to C4.

[0137] (Appendix C6) The one or more target indicators include: indicators related to patient admissions and discharges; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients At least one of the following is included: An information processing program according to any one of appendices C2 to C4.

[0138] (Appendix C7) The information processing program according to Appendix C6, wherein the one or more target indicators include at least one of average length of stay, bed occupancy rate, and number of referred patients.

[0139] (Appendix C8) The output data includes information for supporting decision-making regarding management of the target medical institution. An information processing program as described in Appendix C7.

[0140] [Appendix D] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0141] (Appendix D1) at least one processor, A first acquisition process for acquiring index information including a plurality of indexes related to a target medical institution; a second acquisition process for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition process for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generation process for generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; An information processing device that executes the above.

[0142] The information processing device may further include a memory, and the memory may store a program for causing the at least one processor to execute each of the processes.

[0143] (Appendix D2) The analysis results include: The information includes information on the correlation between one or more target indices obtained from the index information and one or more factor indices obtained from the index information. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0144] (Appendix D3) The at least one processor: The third acquisition process includes: a prompt generation process for generating a prompt to be input to the generative model by referring to at least a part of the analysis results; The information processing device according to appendix D2,

[0145] (Appendix D4) In the prompt generation process, the at least one processor generates a prompt to be input to the generation model using a prompt template that is preset for each of the factor indicators. 10. The information processing device according to claim 9, wherein the information processing device is an information processing device according to claim 1, wherein

[0146] (Appendix D5) The one or more factor indices are indices related to at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the index information. An information processing device according to any one of appendices D2 to D4.

[0147] (Appendix D6) The one or more target indicators include: indicators related to patient admissions and discharges; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients At least one of the following is included: An information processing device according to any one of appendices D2 to D4.

[0148] (Appendix D7) The information processing device according to Appendix D6, wherein the one or more target indicators include at least one of an average length of hospital stay, a bed occupancy rate, and a number of referred patients.

[0149] (Appendix D8) The output data includes information for supporting decision-making regarding management of the target medical institution. 10. The information processing device according to claim 7,

[0150] [Appendix E] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0151] (Appendix E1) A program that causes a computer to function as an information processing device, The computer, A first acquisition process for acquiring index information including a plurality of indexes related to a target medical institution; a second acquisition process for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition process for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generation process for generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; A non-transitory recording medium on which an information processing program for executing the above is recorded. [Explanation of symbols]

[0152] 1, 100 Information processing equipment 1A Information Processing System 10, 51, 61 Control unit 11 First Acquisition Section 12 Second Acquisition Section 13 Third Acquisition Section 14 Generation unit (output data generation unit) 20, 52, 62 storage section 30, 53, 63 Communications Department 40 Input / output section 50 First server device 60 Second server device 121 Input data generation unit 122 Analysis result acquisition section 131 Prompt Generation Unit 132 Explanation information acquisition unit C1 processor C2 Memory

Claims

1. a first acquisition means for acquiring index information including a plurality of indexes related to a target medical institution; a second acquisition means for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition means for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generating means for generating output data including at least a part of the analysis result and at least a part of the explanatory information; An information processing device comprising:

2. The analysis results include: The information includes information regarding the correlation between one or more target indices obtained from the index information and one or more factor indices obtained from the index information. The information processing device according to claim 1 .

3. The third acquisition means a prompt generation means for generating a prompt to be input to the generative model by referring to at least a part of the analysis result; The information processing device according to claim 2 , further comprising:

4. The prompt generation means generates a prompt to be input to the generation model using a prompt template that is preset for each of the factor indicators. The information processing device according to claim 3 .

5. The one or more factor indices are indices related to at least one of doctor information, time information, hospitalization information, outpatient information, disease information, treatment information, patient information, and medical information included in the index information. The information processing device according to claim 2 .

6. The one or more target indicators include: indicators related to patient admissions and discharges; Indicators of hospital bed availability, and Indicators related to costs incurred in providing medical care to patients At least one of the following is included: The information processing device according to claim 2 .

7. The information processing device according to claim 6 , wherein the one or more target indices include at least one of an average length of hospital stay, a bed occupancy rate, and a number of referred patients.

8. The output data includes information for supporting decision-making regarding management of the target medical institution. The information processing device according to claim 7 .

9. Obtaining indicator information including a plurality of indicators related to a target medical institution; acquiring an analysis result output by an analysis model that references at least a part of the index information; acquiring explanatory information regarding at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; generating output data including at least a portion of the analysis results and at least a portion of the explanatory information; An information processing method comprising:

10. A program that causes a computer to function as an information processing device, The computer a first acquisition means for acquiring index information including a plurality of indexes related to a target medical institution; a second acquisition means for acquiring an analysis result output by an analysis model that references at least a part of the index information; a third acquisition means for acquiring explanatory information relating to at least a portion of the analysis results, the explanatory information being output by a machine-learned generative model that references at least a portion of the analysis results; a generating means for generating output data including at least a part of the analysis result and at least a part of the explanatory information; A program that functions as a

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