Information processing system and information processing method

Abstract

An information processing system (10) evaluates values of a plurality of examination items respectively. The information processing system (10) has: an information acquisition unit (201) that acquires patient information (215) respectively associated with a plurality of patients who are subjects of examinations, and examination value information (216) respectively associated with effects obtained by performing examinations on the plurality of patients for each examination item; a classification unit (202) that classifies the plurality of patients into a plurality of groups based on the patient information (215) and the examination value information (216) acquired by the information acquisition unit (201); and an examination value representative value calculation unit (204) that calculates, for each group classified by the classification unit (202), an index value, i.e., an examination value representative value (218), which is statistically obtained from the plurality of examination value information (216).

Description

Technical Field

[0001] This disclosure relates to technologies such as information processing systems and information processing methods for enabling efficient operations in facilities such as medical facilities. Background Technology

[0002] In recent years, the efficiency of various operations in hospitals and other medical facilities has received much attention in the healthcare industry, given the shortage of medical professionals.

[0003] Patent document 1 describes a system for efficiently extracting appropriate information in a medical information system containing large amounts of data such as patient information and clinical information, in order to generate a patient cohort as a group of patients with defined similarities for comparison in medical research, in order to achieve various purposes.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Publication No. 2018-535488 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] Patient examinations are an essential part of the operations in facilities such as hospitals. Therefore, evaluating and improving the efficiency of examinations is particularly helpful in improving the overall operations of the facility. To evaluate and improve the efficiency of clinical examinations, it is necessary to assess the value of each examination based on patient characteristics and the tendency of the examination to be of high or low value depending on the type of patient. Evaluating the value of examinations not only improves efficiency but also leads to operational improvements. This is because the evaluation of examination value can be effectively utilized in a wide range of scenarios, such as the appropriateness of examination orders by examination technicians, the appropriateness of examination result values, the methods used in the measurement of examinations, and comparisons of examination value based on differences in outsourced examination centers.

[0009] While the patent document 1 described above describes the extraction of patient attributes from clinical information, the prediction of one of the specific attributes using other attributes, and the resulting pattern for generating a patient cohort, it does not consider the evaluation of the value of the examination.

[0010] Furthermore, regarding examinations, such as clinical examinations, they play a crucial role in patient care. Therefore, it is also necessary to quantitatively evaluate the value of clinical examinations—in other words, the extent to which clinical examinations contribute to the care process.

[0011] The purpose of this disclosure is to provide an information processing system and method that can reduce the workload of operations within a facility.

[0012] Methods for solving problems

[0013] Representative embodiments of this disclosure have the following structure. One embodiment of the information processing system evaluates the value of multiple examination items. The information processing system includes: an information acquisition unit that acquires patient information related to multiple patients who are the subjects of examinations, and examination value information related to the effects obtained by performing the examinations on the multiple patients for each of the examination items; a classification unit that classifies the multiple patients into multiple groups based on the patient information acquired by the information acquisition unit and the examination value information; and a representative value calculation unit that calculates a representative value, statistically derived from the multiple examination value information, according to the groups classified by the classification unit.

[0014] One embodiment of the information processing method evaluates the value of multiple examination items. In this method, patient information related to multiple patients who are the subjects of the examinations, and examination value information related to the effects obtained from performing the examinations on the multiple patients for each of the examination items, are obtained. Based on the obtained patient information and the examination value information, the multiple patients are classified into multiple groups. For each group, a representative value, calculated statistically based on the multiple examination value information, is then determined.

[0015] Invention Effects

[0016] According to the representative embodiments disclosed herein, techniques for improving the efficiency of operations in facilities such as hospitals can be provided. Other issues, structures, and effects not described above are shown in the embodiments for carrying out the invention. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating an example of the general structure of the information processing system according to the first embodiment.

[0018] Figure 2 This is a diagram illustrating an example of the structure of functional blocks in the information processing system of the first embodiment.

[0019] Figure 3 This is a flowchart illustrating an example of the classification process in which the classification department of the first embodiment classifies patient information.

[0020] Figure 4 This is a diagram showing an example of a first display screen for inputting extraction conditions in the first embodiment.

[0021] Figure 5This is a diagram showing an example of a second display screen that displays the extraction results of the first embodiment.

[0022] Figure 6 This is a diagram showing an example of a third display screen where the extraction conditions of the first embodiment are input.

[0023] Figure 7 This is a diagram showing an example of a fourth display screen that displays the extraction results of the first embodiment.

[0024] Figure 8 This is a diagram illustrating an example of the structure within a medical facility in a variation of the first embodiment.

[0025] Figure 9 This is a diagram illustrating an example of the structure of functional blocks in the information processing system of the second embodiment.

[0026] Figure 10 This is a diagram showing an example of the fifth display screen of the output unit in the second embodiment.

[0027] Figure 11 This is a diagram showing an example of the sixth display screen of the output unit in the second embodiment. Detailed Implementation

[0028] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the drawings, the same symbols are generally used to label the same parts, and repeated descriptions are omitted. In the drawings, for ease of understanding of the invention, the representation of constituent elements sometimes does not indicate their actual position, size, shape, extent, etc.

[0029] In descriptions of program-based processing, the main focus may be on programs, functions, or processing units. However, the hardware entity responsible for these functions is the processor, or a controller, device, computer system, or system comprised of such a processor. The computer system, through the processor, appropriately utilizes resources such as memory and communication interfaces while executing the program read from memory. This achieves the specified functions and processing units. Processors may be composed of semiconductor devices such as CPUs / MPUs and GPUs. Processing is not limited to software program processing; it can also be implemented using dedicated circuitry. Dedicated circuitry can utilize FPGAs, ASICs, CPLDs, etc.

[0030] The program can be pre-installed as data on the target computer system, or it can be distributed as data from a program source to the target computer system. The program source can be a program distribution server on a communication network, or a non-transitory computer-readable storage medium, such as a memory card or disk. The program can also consist of multiple modules. The computer system can also consist of multiple devices. The computer system can also consist of a client / server system, a cloud computing system, an IoT system, etc. Various data and information can be constructed, such as tables and lists, but are not limited to these. Identification information, identifiers, IDs, names, numbers, etc., can be interchanged.

[0031] The embodiments will now be described using the accompanying drawings.

[0032] <First Implementation Method>

[0033] In the first embodiment, as an example, an information processing system is described that can classify patients under specified conditions, evaluate the value and efficiency of examination items according to the groups of classified patients, and output recommended examination items for improvement based on the evaluation results.

[0034] <Outline Structure Diagram of an Information Processing System>

[0035] Figure 1 This is a diagram illustrating an example of the general structure of the information processing system 10. Furthermore, the structure of the information processing system 10 is common to the structure of the information processing system 11 described in the first embodiment and the second embodiment described later.

[0036] like Figure 1 As shown, the information processing system 10 is used, for example, in an environment including medical facilities 100a, 100b, 100c and supplier 113. Medical facilities 100a-100c perform examinations such as physical examinations for patients as a service. Supplier 113 is a service provider that processes information received from medical facilities 100a-100c. Supplier 113 is connected to the facility-internal networks 108a-108c within medical facilities 100a-100c via an external network 112. In this embodiment, supplier 113 is located outside medical facilities 100a-100c and is shared by medical facilities 100a-100c.

[0037] Medical facility 100a includes: an information processing device 101a, an external storage device 109a, and a diagnostic analysis device 111a. The information processing device 101a, the external storage device 109a, and the diagnostic analysis device 111a are communicatively connected via an intra-facility network 108a within the medical facility 100a.

[0038] The information processing device 101a is, for example, a computer system such as a personal computer or a tablet terminal. Details of the information processing device 101a will be described later.

[0039] The diagnostic analysis device 111a is a device capable of performing examinations on patients, and in this embodiment, it is used to perform clinical examinations. For example, the diagnostic analysis device 111a is an automated analysis device that analyzes samples such as blood and urine collected from a patient. The diagnostic analysis device 111a can also be other analysis devices. Furthermore, the diagnostic analysis device 111a can also include multiple or various types of analysis devices.

[0040] External storage device 109a includes medical information server 110a. Medical information server 110a collects and accumulates, for example, various patient-related information (described later) including information on the examination results of the specimen obtained by diagnostic analysis device 111a.

[0041] Medical facility 100b includes an information processing device 101b, an external storage device 109b, and a diagnostic analysis device 111b. The information processing device 101b, external storage device 109b, and diagnostic analysis device 111b are communicatively connected via an intra-facility network 108b within medical facility 100b. Medical facility 100c includes an information processing device 101c, external storage device 109c, and diagnostic analysis device 111c. The information processing device 101c, external storage device 109c, and diagnostic analysis device 111c are communicatively connected via an intra-facility network 108c within medical facility 100c. The information processing devices 101b and 101c, external storage devices 109b and 109c, and diagnostic analysis devices 111b and 111c are identical to the information processing device 101a, external storage device 109a, and diagnostic analysis device 111a, respectively; therefore, detailed descriptions are omitted.

[0042] Supplier 113 receives information stored in information processing devices 101a-101c and external storage devices 109a-109c contained in medical facilities 100a-100c via facilities-internal networks 108a-108c and facilities-external networks 112, and processes the received information.

[0043] Next, the information processing device 101a will be described. The information processing devices 101b and 101c will also be described in the same way. Therefore, the information processing device 101a will be described, and the description of the information processing devices 101b and 101c will be omitted.

[0044] The information processing apparatus 101a includes an arithmetic unit 102, a main storage unit 103, a communication unit 104, an input unit 105, an output unit 106, and an auxiliary storage unit 107. The arithmetic unit 102 is communicatively connected to the main storage unit 103, the communication unit 104, the input unit 105, the output unit 106, and the auxiliary storage unit 107, respectively.

[0045] The arithmetic unit 102 is a device that executes programs loaded into the main memory device 103. The arithmetic unit 102 is, for example, a processor such as a CPU.

[0046] The main storage device 103 is a device that stores programs executed by the computing device 102, processed information, and information including processing results. The main storage device 103 is, for example, an HDD (hard disk drive) or an SSD (solid-state drive).

[0047] Communication unit 104 is a device for sending and receiving data with external devices via a communication network. Communication unit 104 is, for example, a network interface. Communication unit 104 receives information stored in external storage device 109a, for example, via facility network 108a.

[0048] Input unit 105 is a device for allowing a user to input instructions into information processing device 101a. Input unit 105 may be, for example, a keyboard, mouse, touch panel, etc. Output unit 106 is a device for displaying information to the user. Output unit 106 may be, for example, a monitor, printer, etc.

[0049] The auxiliary storage device 107 is a device in the information processing system 10 that stores various procedures for evaluating the value of examination items for each patient group.

[0050] <An example of the functional structure of an information processing system>

[0051] Figure 2 This is a diagram illustrating an example of the structure of functional blocks in the information processing system 10.

[0052] The information processing system 10 functionally includes: an information acquisition unit 201, a classification unit 202, and a calculation unit 203. The calculation unit 203 includes: an inspection value representative value calculation unit 204, an inspection implementation rate calculation unit 205, an improvement inspection candidate calculation unit 206, and an estimated loss calculation unit 207. Figure 2 The diagram also shows the input section 105 and the output section 106.

[0053] Figure 2The functions of the information processing system 10 shown are implemented by the arithmetic unit 102 executing a program stored in the auxiliary storage device 107. This processing is implemented in conjunction with other hardware. Furthermore, the program can also be installed on the auxiliary storage device 107 or the main storage device 103. Alternatively, it can be provided as an installable or executable file via download from an external server or recorded on a recording medium readable by the information processing device 101a, such as a CD-ROM.

[0054] Input information 208 is stored in the medical information server 110a. Input information 208 includes: electronic medical record information 209, examination command information 210, medical bill information 211, hospital information 212, guideline information 213, examination value information 216, and operation information 214 input from the input unit 105.

[0055] Electronic medical record information 209 contains information related to the patient's medical treatment process. By referring to this electronic medical record information 209, doctors can access all the information obtained from the patient at a given point in time. The information included in electronic medical record information 209 includes, for example, medical records, diagnostic forms, prescriptions, surgical records, nursing records, and inpatient treatment plans—information used to monitor the patient's condition. Electronic medical record information 209 contains information created by the medical facility 101a, healthcare institutions, and the individual patient.

[0056] The examination command information 210 includes command information related to the command issued by the doctor, and examination implementation information based on the examiner of the examination department performing the examination. Command information may include, for example, information indicating the date and time the command was issued, information about the command issuer (indicating the department and doctor), and command content information indicating the command content (examination items). Examination implementation information is related to the examination implementation and result reporting. Examination implementation information may include, for example, information indicating the date and time the examination was performed, examination result information (indicating examination result values, outlier distinctions, result annotations, errors, and cancellation information), and information indicating the date and time the examination result was reported. Furthermore, the command information and examination implementation information may each contain other information.

[0057] Medical expense billing information 211 contains information related to insured medical treatment received by patients enrolled in national health insurance, social insurance, or other insurance programs. Medical expense billing information 211 may include, for example, the patient's personal information, the department visited, the diagnosed disease, information related to procedures performed, examinations, rehabilitation, etc., and medical remuneration information 223 regarding payments made to medical facility 101a for those procedures. Medical remuneration information 223 may also be information expressed as a score. Furthermore, medical expense billing information 211 may also contain other information.

[0058] Internal information 212 is information related to the operation of medical facility 101a. Internal information 212 may include, for example, work status information indicating the working status of medical practitioners, cost information indicating the costs of various treatment procedures (indicating labor costs, equipment costs, and material costs), and clinical indicator information such as the number of visiting and inpatient patients, and average length of stay in the hospital. In addition, internal information 212 may also include other information.

[0059] Guideline information 213 contains information indicating the guidelines that physicians or other healthcare practitioners refer to in each consultation. Here, guidelines include various guidelines such as those issued by societies and other organizations, facility rules, and facility guidelines.

[0060] Examination value information 216 represents information related to the effectiveness obtained by performing an examination on a patient. Examination value information 216 varies according to the patient-related information at the time of the examination, and varies by examination item. Even in the case of the same examination item performed on the same patient, if the examination period is different and the patient-related information is different, the examination value information 216 will represent different values. Examination value information 216 is calculated, for example, each time a patient is examined, based on examination result information representing the examination result and patient information 215, and is obtained by the medical information server 110a. An example of the processing used to calculate examination value information 216 will be described in a variation of the information processing system 10 described later.

[0061] Operation information 214 includes operation-related information such as switching the display of the screen on the output unit 106 and changing the displayed content. Operation information 214 is information input by the user operation input unit 105 of the information processing device 101a.

[0062] The information acquisition unit 201 acquires at least patient information 215 related to each of the multiple patients who are the subjects of examinations, such as clinical examinations, and examination value information 216 related to the effects obtained by performing examinations on each of the multiple patients for each examination item. Additionally, the information acquisition unit 201 acquires treatment fee information 223 indicating the cost of each of the multiple examination items, and examination cost information 224 indicating the examination costs incurred by performing the multiple examination items separately. Treatment fee information 223 is obtained, for example, from treatment fee bill information 211. Examination cost information 224 is obtained, for example, from hospital information 212.

[0063] The information acquisition unit 201 extracts, for example, the information required for processing from the input information 208. The information acquisition unit 201 parses the text data recorded in the electronic medical record information 209, guideline information 213, etc., in the medical information server 110a, decomposes it into category data and numerical data, and extracts the required information. In this embodiment, patient information 215 and examination value information 216 are extracted. Patient information 215 and examination value information 216 are output to the classification unit 202. The improvement threshold 221, treatment remuneration information 223, and examination cost information 224, described later, are output to the calculation unit 203.

[0064] Patient information 215 refers to patient-related information as the subject of an examination. Patient information 215 is the collective term for all patient-related information obtained by medical facility 100a. Patient information 215 may include, for example, age, gender, chief complaint, opinion, symptoms, past medical history, family medical history, examinations performed on the patient, treatment history, medication information, various examination results, and other information related to medical procedures.

[0065] The classification department 202 classifies multiple patients into multiple groups based on the patient information 215 and examination value information 216 obtained by the information acquisition department 201.

[0066] The classification unit 202, for example, classifies multiple patients into multiple groups under specified conditions based on patient information 215 and examination value information 216 extracted by the information acquisition unit 201, and calculates patient classification information 217 representing the optimal classification conditions. Here, the optimal classification conditions refer to classification convergence. The classification processing of the classification unit 202 will be described later. Figure 3 The details are as follows. Patient classification information 217 is output to the calculation unit 203 and the output unit 106 respectively.

[0067] The calculation unit 203 includes: an inspection value representative value calculation unit 204, an inspection implementation rate calculation unit 205, an improvement inspection candidate calculation unit 206, and an estimated loss calculation unit 207. The inspection value representative value calculation unit 204 calculates the inspection value representative value 218. The inspection implementation rate calculation unit 205 calculates the inspection implementation rate 219. The improvement inspection candidate calculation unit 206 calculates the improvement inspection candidate 222. The estimated loss calculation unit 207 calculates the estimated loss 220. The calculated inspection value representative value 218, inspection implementation rate 219, improvement inspection candidate 222, and estimated loss 220 are output to the output unit 106.

[0068] The inspection value representative value calculation unit 204 calculates the index value, i.e. the representative value (hereinafter referred to as the inspection value representative value 218), which is statistically derived from multiple inspection value information 216 according to the groups classified by the classification unit 202.

[0069] The diagnostic value representative value calculation unit 204 calculates, for example, the representative value calculated statistically based on the diagnostic value information 216 according to the groups classified by the classification unit 202, and uses it as the diagnostic value representative value 218. As described above, the diagnostic value information 216 is calculated based on the patient information 215 at the time of the examination. Therefore, even for patients belonging to the same group, the diagnostic value information 216 has a bias. In this state of bias, the calculation device 102 has difficulty interpreting the diagnostic value information 216. In addition, similarly, in the state of bias, even if the user confirms the output result through the screen of the output unit 106, the visual recognition on the screen is poor. Therefore, it is preferable to perform the processing of calculating the diagnostic value representative value 218 based on the diagnostic value information 216. Here, regarding the diagnostic value representative value 218, for example, if the performance of the diagnostic value information 216 is a score, the average value, median value, or most frequent value can be used; or, if it is an ordinal scale such as a 5-level evaluation, the median value or most frequent value can be used. The setting for calculating the representative value 218 of the inspection value can be preset by the user through the input unit 105. In this way, the setting of the representative value 218 of the inspection value can be flexibly selected.

[0070] The inspection implementation rate calculation unit 205 calculates the inspection implementation rate 219, which represents the proportion of each inspection item implemented, according to the groups classified by the classification unit 202.

[0071] The improvement inspection candidate calculation unit 206 calculates the inspection efficiency of each inspection item in the group classified by the classification unit 202 based on the inspection implementation rate 219 calculated by the inspection implementation rate calculation unit 205 and the inspection value representative value calculation unit 204. Based on the calculated inspection efficiency and the improvement threshold 221 for the specified improvement efficiency, the improvement inspection candidate 222 for the inspection items in the group is calculated. Alternatively, the improvement inspection candidate calculation unit 206 can also calculate the improvement inspection candidate 222 based on the inspection value representative value 218, the inspection implementation rate 219, and the estimated loss 220 calculated by the estimated loss calculation unit 207 (described later).

[0072] The improvement inspection candidate calculation unit 206 evaluates inspection efficiency based on the inspection value representative value 218, inspection implementation rate 219, and estimated loss 220, for example, according to the groups classified by the classification unit 202. It extracts inspections that are judged to be less efficient than the improvement threshold 221 as improvement inspection candidates 222, and thus calculates the improvement inspection candidates 222.

[0073] The improvement inspection candidate 222 refers to inspection items for which the implementation rate of improvement inspections is recommended. When the inspection items that become improvement inspection candidates 222 are displayed on the output unit 106, they are highlighted. The coordinated display of the improvement inspection candidates 222 includes, for example, background color highlighting, display of markings indicating attention and warnings, and textual information promoting improvements in the implementation rate of inspections. In this embodiment, the coordinated display of the improvement inspection candidates 222 is displayed on the output unit 106 as the improvement inspection candidate display 409, described later.

[0074] The improvement threshold 221 includes information on the inspection value representative value 218, the upper and lower limits for the inspection implementation rate 219, and the allowable value for the estimated loss 220. The improvement threshold 221 can be set via the user operation input unit 105. The inspection value representative value 218 and the inspection implementation rate 219 are essential elements in the processing of the improvement inspection candidate calculation unit 206. Therefore, in the absence of a setting for the inspection value representative value 218 and the inspection implementation rate 219, any preset default values ​​are used.

[0075] The improvement inspection candidate 222 targets inspection items that are either over-checked or under-checked. For example, when the improvement inspection candidate calculation unit 206 extracts inspection items using an improvement threshold 221 based on the inspection value representative value 218 and the inspection implementation rate 219, it performs the following processing: The improvement inspection candidate calculation unit 206 extracts inspections where the inspection value representative value 218 is below the lower limit and the inspection implementation rate 219 exceeds the upper limit, classifying them as over-checked. Conversely, it extracts inspections where the inspection value representative value 218 exceeds the upper limit and the inspection implementation rate 219 is below the lower limit, classifying them as under-checked. The improvement inspection candidate calculation unit 206 then outputs these calculated inspection items as improvement inspection candidates 222 to the output unit 106.

[0076] The estimated loss calculation unit 207 calculates the estimated loss 220 related to the cost of the examination items based on the examination implementation rate 219, the representative value of the examination 218, the medical remuneration information 223, and the examination cost information 224.

[0077] Treatment remuneration information 223 indicates the amount of remuneration received for providing treatment. Treatment remuneration information 223 includes information on remuneration received for examinations performed. Examination cost information 224 includes information related to the actual costs incurred during each examination, such as reagent costs, equipment costs, system costs, and labor costs; workload such as operating time; and patient workload including risks of invasiveness or side effects, such as blood collection volume. Here, estimated loss 220 includes operating losses in the medical facility 100a accompanying the examination, loss of human resources due to workload such as operating time, and patient workload due to risks of invasiveness and / or side effects.

[0078] In the case of over-examination (examination with low examination value information 216 and high examination implementation rate 219), the estimated loss 220 represents the difference between the current situation and the situation where the examination was not performed. Similarly, in the case of under-examination (examination with high examination value information 216 and low examination implementation rate 219), the estimated loss 220 represents the difference between the current situation and the situation where the examination was performed. For example, regarding operating loss, the estimated loss calculation unit 207 calculates the examination difference obtained by subtracting the actual expenses included in the examination cost information 224 from the revenue (i.e., treatment remuneration information 223) accompanying the examination implementation, and the difference is compared and output as the estimated loss 220. For under-examination, the estimated loss calculation unit 207 calculates the difference between the examination difference when the examination implementation rate 219 increases to 100% and the current examination difference as the estimated loss 220. The estimated loss calculation unit 207 calculates the difference between the estimated loss 220 and the current estimated loss 220 when the examination implementation rate 219 is reduced to 0% for excessive examinations. Since medical procedures involving examinations are not entirely based on output, conditions for calculating treatment remuneration are stipulated. Therefore, there are many cases where reducing examinations can generate monetary benefits within the medical facility 100a. Thus, the information processing system 10 estimates the losses from excessive or insufficient examinations resulting from the current situation and outputs the estimated losses to the output unit 106. This allows the user to consider the improvement in financial efficiency and select the examination to be improved from the improved examination candidate 222.

[0079] The improvement candidate calculation unit 206, for example, when using the improvement threshold 221 of the representative value of examination 218 and the estimated loss 220, calculates examination items that match the improvement threshold 221 from the lower values ​​of the representative value of examination 218, such as examinations with a monthly benefit of 100,000 yen or more, or examinations that can reduce working hours by more than 4 hours per month, as improvement candidates 222. Thus, the calculation unit 203 can quantitatively calculate different improvement effects according to the purpose of improving the utilization efficiency of examinations, and can output the calculated results to the output unit 106. Therefore, the user can select the improvement candidate 222 of the examination item that matches the purpose as the improvement candidate. The estimated loss calculation unit 207 can also calculate operating loss, workload, and patient workload as estimated loss 220 respectively. Operating loss, workload, and patient workload can also be calculated and output to the output unit 106 using real values ​​such as amount, working time, and blood collection volume, or classification values ​​such as large / medium / small three-level display.

[0080] Output unit 106 outputs the inspection value representative value 218 calculated by inspection value representative value calculation unit 204. Additionally, output unit 106 emphasizes outputting the improvement inspection candidate display 409, which displays the improvement inspection candidates 222 calculated by improvement inspection candidate calculation unit 206. Thus, the user can visually identify the inspection value representative value 218 and the improvement inspection candidate display 409 for each inspection item.

[0081] The output unit 106 outputs patient classification information 217, representative examination value 218, examination implementation rate 219, estimated loss 220, and improvement examination candidates 222 to the display in a prescribed format. By receiving operation information 214 based on the input unit 105 from the information acquisition unit 201, the output unit 106 can realize screen changes and screen updates based on changes in display conditions. Specific examples of the display screen of the output unit 106 (described later) Figures 4-7 The details of this will be described later.

[0082] <Classification Processing by the Taxonomy Department>

[0083] Figure 3 This is a flowchart illustrating an example of the classification process performed by the classification unit 202 on patient information 215. In the following description, the classification unit 202 is designated as the action subject for each step. However, since the classification unit 202 reads the program into the main storage device 103 and it is executed by the processing unit 102, the processing unit 102 can also be designated as the action subject. Furthermore, in the following description, the difference test between two groups, frequently used in pharmaceutical efficacy evaluation and other statistical methods, is employed as a traditional statistical method in the medical field. However, other data analysis methods such as clustering, linear analysis, and logistic regression can also be used.

[0084] (Step S1)

[0085] The classification department 202 receives patient information 215 and examination value information 216 from the information acquisition department 201.

[0086] (Step S2)

[0087] The classification unit 202 performs preprocessing such as transforming the data format of the patient information 215 received in step S1 to enable subsequent classification processing. As preprocessing, the classification unit 202 transforms items in the patient information 215, such as age and examination values, from continuous values ​​to categorical values. Regarding the transformation method, in addition to equal division such as quartiles or dividing intervals into 10-value intervals, any method can be used, such as allocation based on medical evidence like BMI (obesity, standard, or weight loss).

[0088] (Step S3)

[0089] In step S2, classification unit 202 sums the frequency of occurrence of values ​​contained in each item of the preprocessed patient information 215. Furthermore, missing values ​​not entered in the items and unclear details are also included in the summation as information indicating the patient's status.

[0090] (Step S4)

[0091] Based on the total frequency of occurrence in step S3, classification unit 202 divides patient information 215 into a matching group and a non-matching group using the most frequent value. The items used as criteria for this division and their values ​​can also be specified by the user using input unit 105.

[0092] (Step S5)

[0093] The classification unit 202 determines whether the number of items in each of the conforming and non-conforming groups separated in step S4 is above or below the lower limit. If the number of items in both the conforming and non-conforming groups is above the lower limit, the process proceeds to step S6. Otherwise, if either the conforming or non-conforming group is below the lower limit, the process proceeds to step S8. Here, the lower limit is a ratio relative to a real number, the total number of items, etc. The value of the lower limit can be arbitrarily set.

[0094] (Step S6)

[0095] The classification unit 202 examines the difference between the two groups of examination value information 216 corresponding to the patient information 215 divided into a conforming group and a non-conforming group in step S4, and determines whether there is a significant difference. Here, the test for significant difference between the two groups is performed, for example, by a non-parametric test such as Wilcoxon's order sum test. Furthermore, when determining significant differences in examination value information 216 for multiple examination items, in this embodiment, the classification unit 202 examines the difference between the two groups in the examination value information 216 for each examination item. If a significant difference exists even for a single examination item, it is determined that there is a significant difference in the examination value information 216 between the two groups. If a significant difference is determined to exist, the process proceeds to step S7. If no significant difference is determined, or if there is no significant difference among all examination items, the process proceeds to step S8.

[0096] (Step S7)

[0097] The classification unit 202 sets the patient information 215 that is in the matching group and the non-matching group, which were determined to have significant differences in the examination value information 216 in step S6, as different patient information 215. For the information that is thus divided into different patient information 215, the processing described in step S3 and below is performed. That is, recursive processing is performed on the information that is divided into different patient information 215.

[0098] (Step S8)

[0099] Based on the total result in step S3, classification unit 202 determines whether the frequency of the item value second only to the most frequent value (the second-most frequent value) is above or below the lower limit. Here, the lower limit can be arbitrarily set, similar to the case in step S5. Furthermore, if multiple conditions (items and item values) are specified in step S4, classification unit 202 uses the item value that meets the specified conditions for the second point instead of the second-most frequent value. If it is determined to be above or below the lower limit, the process proceeds to step S9. If it is determined to be below the lower limit, the process proceeds to step S10.

[0100] (Step S9)

[0101] The classification unit 202 divides the patient information 215 into a conforming group and a non-conforming group based on the second most frequent value in step S8 or other conditions specified in step S8. The processing described in step S5 and below is then performed on the patient information 215 that has been divided in this way. That is, recursive processing is performed on the patient information 215 that has been divided in this way.

[0102] (Step S10)

[0103] Classification unit 202, through steps S1-S9, outputs the converged classification conditions, or in other words, the optimal classification conditions, as patient classification information 217 to calculation unit 203 and output unit 106. Patient classification information 217 is, for example, as described later. Figure 5 The second display screen, as shown in Example 405, is displayed on the output unit 106. Additionally, the patient classification information 217 can also be stored in the main storage device 103.

[0104] <Example of the display screen on the output section>

[0105] Figure 4 , Figure 5 , Figure 6 as well as Figure 7 These are examples of display screens of the output unit 106. Figure 4 This is a diagram of the first display screen, example 401, representing the input extraction conditions. Figure 5 This is a diagram of the second display screen, example 405, showing the extraction results. Figure 6 This is a diagram of the third display screen, example 401a, which represents the input extraction conditions. Figure 7 This is a diagram showing a fourth display screen example 501 that displays detailed information about the inspection value. First display screen example 401, second display screen example 405, third display screen example 401a, and fourth display screen example 501 can also be displayed on the same screen. Hereinafter, the output of the output unit 106 will be described in the case of displaying information as shown in the first display screen example 401, second display screen example 405, third display screen example 401a, and fourth display screen example 501. However, it is not limited to this; the information displayed on the screens can also be printed on paper or stored as data in a predetermined storage device, such as the main storage device 103.

[0106] like Figure 4 As shown, the first display screen example 401 includes: an update button 402, an improvement threshold setting unit 403, and a patient information setting unit 404. The improvement threshold setting unit 403 can set values ​​for over-examination and under-examination for three items: the representative value of examination 218, the examination implementation rate 219, and the estimated loss 220, respectively.

[0107] The patient information setting unit 404 sets the conditions for retrieving patient information 215. The patient information setting unit 404 includes: period 404a, admission / exit classification 404b, treatment department 404c, diagnosis name 404d, age 404e, and gender 404f. Period 404a sets the first and last days of the period. Admission / exit classification 404b sets whether the patient is an inpatient or an outpatient. Treatment department 404c sets the department responsible for the treatment. Diagnosis name 404d sets the diagnosis name. Age 404e sets the age range. Gender 404f sets the gender.

[0108] For example, in Figure 4 In this context, data is extracted from the period from January 1, 20XX to December 31, 20XX. Additionally, inspection items with a representative inspection value (218) of less than 30 points and an inspection implementation rate (219) of 50% or higher are classified as over-inspected. Inspection items with a representative inspection value (218) of 90 points or higher and an inspection implementation rate (219) of less than 50% are classified as under-inspected. Furthermore, in... Figure 4 In the section on the presumed loss 220, it shows the case where no improvement threshold 221 (tolerance) for the presumed loss 220 is set.

[0109] After the conditions are set in the improvement threshold setting unit 403 and the patient information setting unit 404, when the user presses the update button 402, the aforementioned processing is executed, and output is sent to the output unit 106. Figure 5 The extraction results are shown.

[0110] like Figure 5 As shown, the second display screen example 405 displays information processed under the conditions set by the first display screen example 401. In the second display screen example 405, for example, the corresponding data number 406, classification items, and classification item values ​​are displayed. The classification items display the names of the items used for classifying the patient. Classification items include, for example, admission / external classification 407a, treatment department 407b1, 407b2, diagnosed disease name 407c1, 407c2, age 407d, and others 407e. Classification item values ​​408a to 408d are displayed in admission / external classification 407a, treatment department 407b1, 407b2, diagnosed disease name 407c1, 407c2, and age 407d, respectively. The classification item values ​​for others 407e are omitted from the illustration.

[0111] The corresponding data count of 406, such as the number of examination commands and the number of patients, provides information that allows us to grasp the approximate results of the extraction. Figure 5 The data indicates that there were 312,227 inspection orders and 14,665 patients.

[0112] For example, in category item value 408a, the value for the entry / exit distinction 407a is "external". Therefore, the user is aware that the examination value information 216 as external is significantly different from the examination value information 216 other than external (hospitalized). The corresponding number of examination orders is 52,297, and the number of patients is 6,887. Links are provided for each category item such as entry / exit distinction 407a. For example, by clicking each link through the operation input unit 105, the user can switch the screen display from the second display screen example 405 to the fourth display screen example 501 (see reference) for confirming the details of the examination value information 216. Figure 7 ).

[0113] The improved examination candidate display 409 may be displayed as an alarm marker, for example, within the displays for the entry / exit area 407a, clinic departments 407b1, 407b2, diagnosed diseases 407c1, 407c2, and age 407d. In this embodiment, the improved examination candidate display 409 will be described as a triangular display including an exclamation mark. The improved examination candidate display 409 is not limited to this; it can be any display that is recognized as an alarm by the user.

[0114] exist Figure 5 In the diagnostic diagnostic name 407c1, an improvement examination candidate display 409 is shown. That is, it indicates that among the groups classified according to patient information 215 and examination value information 216, diagnostic diagnostic name 407c1 includes examination items that meet the conditions specified as the improvement threshold 221. Regarding the specific examination items and examination value information 216, as already described, by the user clicking the link for diagnostic diagnostic name 407c1 using the input unit 105, the screen displays from the second display screen example 405 to a display screen example for confirming details (e.g., described later). Figure 7 The fourth display screen (example 501) changes.

[0115] The display of the extraction results in the second display screen, example 405, can also be replaced. Figure 5 The inspection items limited to those including the improvement inspection candidate display 409 are displayed in a list format. Additionally, in Figure 5 As an example, this section shows the extraction results for a patient whose conditions match those set by the patient information setting unit 404, but it is not limited to this. Specific patients can also be further specified through the patient information setting unit 404. In this case, a setting unit for specifying specific patients can be provided in the patient information setting unit 404. For example, the patient's ID and name can be specified in this setting unit. Furthermore, the output unit 106 prominently displays the classification item 407 and / or the value of the classification item 407 that matches the specified specific patient on the second display screen example 405. For example, the user can also operate the input unit 105 and click on the collaboratively displayed classification item 407, thereby displaying the patient information 215 of the specified patient on the output unit 106. Thus, the user can visually identify the patient information 215 of the specified patient and understand the basis for the value of the examination value information 216 for that patient. Therefore, the application of the information processing system 10 can be expanded.

[0116] Figure 6 The third display screen example 401a shown is the same as the first display screen example 401, but the conditions set by the patient information setting unit 404 are different. For example, in Figure 6In the middle, it is set to extract data from the period from January 1, 20XX to December 31, 20XX, which is consistent with... Figure 4 The situation is the same. On the other hand, in Figure 6 In this example, the entry / exit classification 404b is defined as "external," the treatment department 404c is defined as "treatment department A," and the diagnosis name 404d is defined as "disease A." Furthermore, in the third display screen example 401a, examination items with a representative value 218 score less than 30 and an examination implementation rate 219 of 50% or higher are defined as over-examinations. Examination items with a representative value 218 score of 90 or higher and an examination implementation rate 219 of less than 50% are defined as under-examinations. Regarding the presumed loss 220, a case where the improvement threshold 221 (tolerance value) for presumed loss 220 is not specified is shown. These are related to... Figure 4 The situation is the same.

[0117] After the conditions are set in the improvement threshold setting unit 403 and the patient information setting unit 404, when the user presses the update button 402, the aforementioned processing is executed, and the output unit 106 performs the corresponding operation. Figure 6 Following the same display, the user clicks on any of the improvement check candidate displays in category item values ​​408a~408d, thereby... Figure 7 The extraction results shown are output to the output unit 106.

[0118] like Figure 7 As shown, the fourth display screen example 501 is an example of displaying detailed inspection value information. The fourth display screen example 501 displays the inspection item display unit 502, the inspection value representative value display unit 503, the inspection implementation rate display unit 504, the estimated loss display unit 505, and the improvement inspection candidate display unit 506.

[0119] The inspection item display unit 502 displays the names of the inspection items included in the categorized groups. The inspection item names displayed in the inspection item display unit 502 only need to be information that can identify the inspection item, such as the inspection item code. The inspection value representative value display unit 503, the inspection implementation rate display unit 504, the estimated loss display unit 505, and the improvement inspection candidate display unit 506 respectively display the inspection value representative value 218, the inspection implementation rate 219, the estimated loss 220, and the improvement inspection candidate display 409, which are associated with the inspection items displayed in the inspection item display unit 502.

[0120] For example, in the fourth display screen example 501, no improvement examination candidate display 409 is displayed for examinations A, B, and C. On the other hand, in the fourth display screen example 501, improvement examination candidate display 409 is displayed for examination D. More specifically, regarding examination D, in the estimated loss display section 505, as estimated loss 220, the examination value representative value display section 503 shows 28 points, the examination implementation rate display section 504 shows 75%, and the estimated loss display section 505 shows an annual operating loss of 560,000 yen, a workload of 3 hours per month, and a patient workload level of 1 (very low). Therefore, the improvement examination candidate display section 506 displays a message indicating the possibility of over-examination, suggesting improvement for examination D, and displays a matching improvement examination candidate display 409. By confirming the information of examinations matching the improvement examination candidate display 409 in the fourth display screen example 501, the user can identify the examinations that need improvement and confirm the losses of performing the examinations, which can help improve the examination business.

[0121] As explained above, the information processing system 10 can evaluate the value of examinations based on the individual value of each examination item calculated each time an examination is performed on a patient, according to the characteristics of the patients and the tendency of the examination value information 216 after being classified by the classification department 202. Therefore, the information processing system 10 can quantitatively evaluate the value of examinations by group of patients with common characteristics. In addition, by utilizing the information processing system 10, in facilities such as hospitals, for example, examinations with low examination value are not performed, or excessive examinations are not performed, thereby achieving high efficiency in the operations within medical facilities 100a to 100c.

[0122] <Variation Example>

[0123] Next, a variation of the first embodiment will be described. Figure 8 This diagram illustrates a variation of the structure within medical facility 100a. In the modified information processing system 10, a diagnostic value calculation system 120 is added to medical facility 100a. The diagnostic value calculation system 120 is connected to the facility network 108a. The diagnostic value calculation system 120 is configured to communicate with the information processing device 101a, the external storage device 109a, and the diagnostic analysis device 111a via the facility network 108a. Although not shown in the diagram, diagnostic value calculation systems are similarly added to medical facilities 100b and 100c.

[0124] The examination value information calculation system 120 calculates examination value information 216. Examination value information 216 is calculated each time a patient undergoes an examination. The calculated examination value information 216 is output to the medical information server 110a.

[0125] The examination value information calculation system 120 has a learning unit 121 and a calculation unit 122. The learning unit 121 uses a dataset with patient information 215 and examination result information related to the examination results obtained from performing clinical examinations on patients regarding examination items as input, and examination value information 216 as output, to learn a model used in calculating the value of examination items.

[0126] The learning department 121 performs the following process, for example, each time an inspection is conducted.

[0127] The learning unit 121 synthesizes the patient information 215 and extracts the feature quantities of the patient information 215. Specifically, the learning unit 121 quantifies the patient information 215 received for examination into text and quantifies it in a way that can be processed by a neural network (synthesis of patient information). The synthesized patient information 215 is then applied to a neural network (e.g., deep learning) to calculate the feature quantities of the numerical information.

[0128] Next, the Learning Department 121 compiles multiple inspection results according to the inspection results information, categorized by inspection item, and uses a probability distribution model to estimate the probability distribution (function) for each inspection item. The Learning Department 121 then reconstructs the inspection results based on the estimated probability distribution. This is because the inspection result, which serves as the positive solution value, is known; therefore, this process confirms whether a positive solution value was obtained based on the estimated probability distribution.

[0129] Next, the learning unit 121 estimates the inspection value based on the restored inspection result. If the original inspection result can be restored from the estimated probability distribution, then the estimated probability distribution is correctly estimated. Next, the learning unit 121 estimates the learning error in the estimated inspection value. Here, the learning error refers to the error between the original inspection result and the restored inspection result.

[0130] Next, the learning unit 121 determines whether the estimated learning error value has converged. Convergence is determined based on whether it is less than a pre-set threshold. The learning unit 121 sets the learning parameters for learning error convergence in the learned model. Here, the learning parameters include the parameters of the neural network described above, the parameters used in probability distribution estimation, etc. The learning unit 121 updates the learning parameters (e.g., the parameters of the neural network) so that the learning error decreases according to the optimization function used for updating. When the learning error is low, a model capable of predicting the patient's examination value can be constructed when new patient information 215 is input.

[0131] The calculation unit 122 calculates the examination value information 216 of the examination items based on the patient information 215 input each time a clinical examination is performed, the examination result information, and the model learned by the learning unit 121. The calculation unit 122 outputs the examination value information 216 each time it calculates the examination value information. For example, the calculation unit 122 outputs the examination value information 216 to the medical information server 110a.

[0132] The examination value information calculation system 120 calculates examination value information 216 each time an examination is performed and outputs it to the medical information server 110a (examination value information storage unit). This ensures that the examination value information 216 stored in the medical information server 110a is up-to-date. Since the calculation of examination value information is performed separately in each medical facility 100a, 100b, and 100c, leakage of examination-related information outside the medical facility 100a is prevented. Furthermore, the learning unit 121 enables the model to learn, so the calculation unit 122 can calculate the examination value information 216 using a model that reflects the latest examination results.

[0133] <Second Implementation Method>

[0134] The second embodiment describes the information processing system 11, which, in addition to the evaluation of the examination value of each group performed by the information processing system 10 of the first embodiment, also performs the anonymization of the patient name, the name of the medical facility performing the examination, and the name of the medical practitioner, and can compare the examination value including other facilities under specified conditions.

[0135] The hardware structure of the information processing system 11 in the second embodiment is the same as that described above. Figure 1 The information processing system 10 shown is common to all systems. Therefore, a detailed description of the hardware structure of the information processing system 11 is omitted.

[0136] <An example of the functional structure of an information processing system>

[0137] Figure 9 This is a diagram illustrating an example of the structure of functional blocks in the information processing system 11.

[0138] The information processing system 11 functionally includes: an in-facility anonymization processing unit 601 (first anonymization processing unit), an access control unit 603, and an inter-facility anonymization processing unit 602 (second anonymization processing unit). An input unit 105 and an output unit 106 are also shown. The in-facility anonymization processing unit 601 and the access control unit 603 are provided in the information processing device 101a. That is, in addition to the information acquisition unit 201, the classification unit 202, and the calculation unit 203, the information processing device 101a functionally includes the in-facility anonymization processing unit 601 and the access control unit 603. Although not shown in the figures, the information processing devices 101b and 101c have the same structure as the information processing device 101a. The inter-facility anonymization processing unit 602 is provided on an external shared server 114. The external shared server 114 is located outside the facilities of medical facilities 100a-100c. When the information processing devices 101a to 101c are set up in the cloud, any one of the information processing devices 101a to 101c can also have the function of an external shared server 114.

[0139] The facility-wide anonymization processing unit 601 anonymizes patient information 215 and examination value information 216. Patient information 215 includes patient identification information identifying the patient who underwent the examination. Examination value information 216 includes medical practitioner identification information identifying the medical practitioner who underwent the examination. In this embodiment, the facility-wide anonymization processing unit 601 anonymizes both patient identification information and medical practitioner identification information. Patient identification information is, for example, the patient's name. Medical practitioner identification information is, for example, the medical practitioner's name. That is, through the anonymization processing of the facility-wide anonymization processing unit 601, the patient's name and the medical practitioner's name can be anonymized.

[0140] The facility-wide anonymization processing unit 601 anonymizes patient information 215 and examination value information 216, for example, to create facility-wide anonymized information 604. Here, the anonymization processing is performed in a manner that makes it impossible to determine the medical practitioner (doctor, examination technician, etc.) who performed the examination, or the patient's name. The anonymization processing can be configured to allow for the selection of any processing method that can be combined to match the desired strength of protection, such as k-anonymization or 1-diversity. Regarding the timing of the anonymization processing in the facility-wide anonymization processing unit 601, it can be performed at a time when an update of information that is to be anonymized is detected, at a regular time such as a date and time, or at any time based on the operation of the input unit 105.

[0141] The anonymized information 604 within the facility is stored in the information processing device 101a. The anonymized information 604 can also be retrieved from the information processing device 101a by the inter-facility anonymization processing unit 602 during the scheduled processing time. Alternatively, the anonymized information 604 can also be automatically uploaded to the external shared server 114 after the inter-facility anonymization processing unit 602 has completed its processing.

[0142] The inter-facility anonymization processing unit 602 receives patient information 215 and examination value information 216, which have been anonymized by the intra-facility anonymization processing unit 601, from the information processing devices 101a-101c. It then anonymizes the facility identification information (indicating the facility name) included or attached to the received anonymized patient information 215 and examination value information 216. This allows for the anonymization of information about which facility the patient information 215 and examination value information 216 belong to.

[0143] The inter-facility anonymization processing unit 602, for example, performs anonymization processing on the intra-facility anonymized information 604 output from the information processing device 101a of multiple facilities, and calculates inter-facility anonymized information 605. This anonymization processing is performed in a manner where it is impossible to determine which facilities have undergone inspection. Similar to the intra-facility anonymization processing unit 601, the inter-facility anonymization processing unit 602 can combine any processing method to select anonymization processing. Furthermore, the timing for performing the anonymization processing can be set at any time, matching the anonymization processing of the intra-facility anonymization processing unit 601.

[0144] When outputting patient information 215 and examination value information 216 to output unit 106, permission determination unit 603 determines whether the user has permission to view the patient information 215 and examination value information 216 before anonymization processing by the facility's anonymization processing unit 601, based on permission information 606 indicating whether the user has permission to view the patient information 215 and examination value information 216 before anonymization processing. Furthermore, when outputting patient information 215 and examination value information 216 to output unit 106, permission determination unit 603 determines whether the user has permission to view the facility name of the patient information 215 and examination value information 216 after anonymization processing by the inter-facility anonymization processing unit 602, based on permission information 606.

[0145] The permission determination unit 603 receives operation information 214 and permission information 606 based on input unit 105. For example, the permission determination unit 603 determines whether the user has viewing permissions for patient information 215, examination value information 216, facility-anonymized information 604, and inter-facility anonymized information 605 based on the permission information 606 contained in the operation information 214.

[0146] Operation information 214 includes login information and other data associated with the personal information of users such as the clinic to which the user belongs. This login information includes information indicating whether each user has the authority to view patient information 215, examination value information 216, facility-wide anonymized information 604, and inter-facility anonymized information 605. The permission determination unit 603 determines the user based on the operation information 214, associates the determined user with the permission information 606, and thereby determines whether the user has the authority to view the information that is to be output. Thus, for example, the following settings can be made: The information processing system 11 can set the permission determination unit 603 so that doctors, as users, can view patient information 215 and examination value information 216 before anonymization for patients under their care. On the other hand, the information processing system 11 can set the permission determination unit 603 so that the doctor cannot view facility-wide anonymized information 604 and inter-facility anonymized information 605 before anonymization for patients they are not responsible for. Furthermore, if the operation information 214 does not contain information indicating whether or not the user has permission to view the permission information 606, the output unit 106 may display, for example, a message indicating that viewing is not permitted. By visually recognizing this display on the output unit 106, the user can understand that they do not have permission to view the information before anonymization.

[0147] If the permission determination unit 603 determines that the user has permission to view the patient information 215 and examination value information 216 before anonymization by the facility-internal anonymization processing unit 601, the output unit 106 outputs information based on the patient information 215 and examination value information 216 before anonymization by the facility-internal anonymization processing unit 601. If the permission determination unit 603 determines that the user does not have permission to view the information, the output unit 106 outputs information based on the patient information 215 and examination value information 216 after anonymization by the facility-internal anonymization processing unit 601. Furthermore, if the permission determination unit 603 determines that the user has permission to view the facility name, the output unit 106 outputs information based on the patient information 215 and examination value information 216 before anonymization by the inter-facility anonymization processing unit 602. If the permission determination unit 603 determines that the user does not have permission to view the facility name, the output unit 106 outputs information based on the patient information 215 and examination value information 216 after anonymization by the second anonymization processing unit. Here, the patient information 215 and examination value information 216, which are processed anonymized, are displayed in accordance with the classified groups described in the first embodiment.

[0148] Thus, based on the permission determination result of the permission determination unit 603, the output unit 106 outputs the information before anonymization (patient information 215, examination value information 216), the facility-anonymized information 604 after anonymization by the facility-anonymization processing unit 601, or the inter-facility anonymized information 605 after anonymization by the inter-facility anonymization processing unit 602. Additionally, the output unit 106 may also output patient classification information 217, examination value representative value 218, and examination implementation rate 219, which are associated with the patient information 215 and examination value information 216 for which the user has viewing permissions.

[0149] <Example of output screen>

[0150] Figure 10 This is a diagram showing the fifth display screen example 701 of the output unit 106. Figure 11 This diagram shows an example of the sixth display screen 705 of the output unit 106. Hereinafter, we will describe an example where the output unit 106 of the information processing device 101a displays information on the display screen, but the information displayed on the display screen may also be printed on paper or stored as data in a storage device. The fifth display screen example 701 and the sixth display screen example 705 may be displayed on the same screen, or one screen may be displayed on one screen, and the display screens may be switched.

[0151] like Figure 10 As shown, the fifth display screen example 701 includes: a patient information selection unit 702, a group addition button 703, an update button 704, and a facility selection button 705a. The patient information selection unit 702 displays the patient information for each group being compared. Figure 10 In this configuration, the patient information designation unit 702 can designate patient information for two groups: Group A patient information designation unit 702a and Group B patient information designation unit 702b. The group addition button 703 adds an area to be displayed as a comparison object. The update button 704 performs processing based on the new patient information 215 for each group when the designations for Group A patient information designation unit 702a and Group B patient information designation unit 702b are updated. The facility designation button 705a selects whether the user's medical facility 101a is included, or whether all medical facilities 100a, 100b, and 100c, including the user's facility, are included.

[0152] The patient information designation unit 702 allows users to specify the extraction criteria for the patient information 215 to be compared. The patient information designation unit 702 includes fields for patient information designation unit 702a (Group A) and patient information designation unit 702b (Group B), covering fields for period, admission / exit category, department, diagnosis, age, and attending physician. Users can operate the input unit 105 to specify the period, admission / exit category, department, diagnosis, age, and attending physician.

[0153] For example, in the patient information designation section 702a for Group A, patient information 215 is designated as Group A patient information for patients whose examination dates fall within the period "January 1, 20XX to December 31, 20XX" and whose diagnosed disease is "Disease A" within "this facility". On the other hand, in the patient information designation section 702b for Group B, patient information 215 is designated as Group B patient information for patients whose examination dates fall within the period "January 1, 20XX to December 31, 20XX" and whose diagnosed disease is "Disease A" within "all facilities". Neither the Group A patient information nor the Group B patient information specifies the admission category, department, diagnosed disease, age, or attending physician. In the fifth display screen example 701, the designation of patient information 215 is the same, specifying the conditions for comparing this facility (medical facility 100a) and all facilities (medical facilities 100a~100c).

[0154] Additionally, when the user operates the input unit 105 and clicks the group addition button 703, the patient information specification unit 702 displays, for example, a C-group patient information specification unit (illustration omitted), which specifies the patient information of group C. The user can specify the extraction conditions for new patient information to be compared in the C-group patient information specification unit.

[0155] When the user enters comparison conditions for the patient information designation section 702a for group A and the patient information designation section 702b for group B in the fifth display screen (example 701) and clicks the update button 704, the previously described usage is executed. Figure 9 The permission determination unit 603 describes the permission determination process. The output unit 106 outputs information that matches the extraction conditions specified in the fifth display screen example 701. As a result, the sixth display screen example 705 is displayed on the output unit 106.

[0156] Figure 11 This is a diagram representing the sixth display screen, example 705, of the output unit 106. (See diagram 705.) Figure 11 As shown, the sixth display screen example 705 displays an inspection item display unit 706, an inspection value representative value display unit 707, an inspection implementation rate display unit 708, and an inspection value information display unit 709. The inspection item display unit 706 displays the types of inspections extracted under specified conditions. The inspection value representative value display unit 707 and the inspection implementation rate display unit 708, as described above, display the inspection value representative value 218 and the inspection implementation rate 219 obtained according to the inspection. The inspection value information display unit 709 displays a graph representing statistical information about the inspection value.

[0157] In the sixth display screen example 705, there is no display unit that displays the patient's name, medical practitioner's name, and facility name. However, when the screen switches to a screen that displays the patient's name, medical practitioner's name, and facility name, the patient's name, medical practitioner's name, and facility name are displayed anonymously based on the user's permission information.

[0158] Furthermore, in the sixth display screen example 705, it can also be set to display information that the user has viewing permission for. In this case, if no information that the user has viewing permission for is found in the information extracted from the extraction conditions, although the illustration is omitted, the meaning can still be displayed in the sixth display screen example 705, such as a message like "No information to display".

[0159] The examination item display unit 706 displays examinations A through D as examination items. Corresponding to each examination A through D, the representative value of the examination calculated based on patient information from group A and group B, 218, and the examination implementation rate, 219, are displayed. Figure 11 In the diagram, Group A displays patient information for Group A, and Group B displays patient information for Group B. For example, the representative value of examination A, 218, is displayed as 92 points in Group A patient information and 88 points in Group B patient information. Furthermore, the examination implementation rate for examination A is displayed as 90% in Group A patient information and 70% in Group B patient information.

[0160] Furthermore, corresponding to each of examinations A through D, the examination value information display unit 709 displays statistical information of the examination value information 216 calculated based on the patient information of group A and group B using box plots 713 and 714. Group A display 711 indicates statistical information calculated based on the patient information of group A, and therefore displays the same content as box plot 713. Group B display 712 indicates statistical information calculated based on the patient information of group B, and therefore displays the same content as box plot 714. Figure 11 In the display, group A (711) and box plot 713 are shaded, while group B (712) and box plot 714 are blank, clearly distinguishing between group A and group B patient information. Statistical information is displayed using box plots 713 and 714, allowing users to easily visually identify the distribution of patient information in groups A and B. Examination value information 216 is represented by box plots 713 and 714, but is not limited to these; it can also be displayed in other forms such as charts, histograms, distribution plots, or scatter plots. Alternatively, the distribution information of examination result values ​​can be displayed instead of the examination value information 216 displayed in the examination value information display unit 709.

[0161] Additionally, in the information processing system 11, it is also possible to... Figure 10 , Figure 11The fifth display screen example 701 and the sixth display screen example 705 shown are displayed together with the first display screen example 401, the second display screen example 405, the third display screen example 401a or the fourth display screen example 501 described above in the same screen.

[0162] As described above, the information processing system 11 according to this second embodiment can maintain the anonymity of the patient who is the subject of the examination, the medical facility that performs the examination, and the medical practitioner, and output examination value information 216 including other facilities to the output unit 106 in a manner that can be compared according to any conditions.

[0163] For example, in medical facility 100a, if a user with the authority to identify the patient and medical practitioner wants to view patient information 215 and examination value information 216 related to an examination within medical facility 100a via information processing device 101a, information processing system 11 displays the patient information 215 and examination value information 216, etc., at output unit 106 in a manner that allows identification of the patient's name and the medical practitioner's name. On the other hand, in medical facility 100a, if a user without the authority to identify the patient and medical practitioner wants to view patient information 215 and examination value information 216 related to an examination within medical facility 100a, information processing system 11 displays the patient information 215 and examination value information 216, etc., at output unit 106 in a manner that prevents identification of the patient's name and the medical practitioner's name.

[0164] Furthermore, for example, if a user of medical facility 100a wants to view patient information 215 and examination value information 216 related to examinations in medical facilities 100a-100c via information processing device 101a, information processing system 11 displays the patient information 215 and examination value information 216 related to examinations in medical facility 100a on output unit 106 in a manner that allows identification of the facility name. On the other hand, information processing system 11 displays the patient information 215 and examination value information 216 related to examinations in medical facilities 100b and 100c on output unit 106 in a manner that does not allow identification of the patient name, medical practitioner name, or facility name.

[0165] According to the information processing system 11, users can compare and examine value information 216 based on their desired conditions. At this time, the information processing system 11 can guarantee the anonymity of patients, medical facilities, and medical practitioners.

[0166] Furthermore, in this embodiment, the comparison between two groups of patients matching the specified conditions was described by the patient information designation unit 702. However, the information processing system 11 can also compare a specific patient with a group of patients matching the specified conditions. Therefore, the information processing system 11 can compare the examination result values ​​of a specific patient with, for example, the distribution of examination result values ​​of a group of patients with similar conditions, and output the comparison result to the output unit 106. Thus, the user can, for example, visually assess whether the examination result values ​​of a specific patient statistically converge to an appropriate range.

[0167] Furthermore, the techniques described in this embodiment are not limited to the embodiments described above, and include various modifications. In the embodiments described above, an examination in medical facilities 100a-100c was used as an example, but for example, within a medical facility, the information processing system 10 and information processing system 11 can also perform the same processing on prescription value information, which represents indicators related to the effects obtained through medication on patients, as they do on examination value information 216. Therefore, the techniques described in this embodiment can be applied to the evaluation of prescription value and prescription efficiency.

[0168] Furthermore, for example, the embodiments described above are detailed embodiments for the purpose of easily understanding and illustrating the technology of this disclosure, and are not limited to having all the structures described. Additionally, a portion of the structure of one embodiment can be replaced with the structure of another embodiment, and it is also possible to add structures of other embodiments to the structure of one embodiment. Furthermore, for a portion of the structure of each embodiment, it is possible to add, delete, or replace other structures.

[0169] Explanation of reference numerals in the attached figures

[0170] 10, 11…Information processing system; 101a~101c…Information processing device; 102…Arithmetic unit; 103…Main storage device; 104…Communication unit; 105…Input unit; 106…Output unit; 107…Auxiliary storage device; 108a~108c…Internal network; 109a~109c…External storage device; 110a~110c…Medical information server; 111a~111c…Diagnostic analysis device; 112…External network; 113…Supplier; 114…External shared server 120…Inspection Value Information Calculation System, 121…Learning Department, 122…Calculation Department, 201…Information Acquisition Department, 202…Classification Department, 203…Calculation Department, 204…Inspection Value Representative Value Calculation Department, 205…Inspection Implementation Rate Calculation Department, 206…Improvement Inspection Candidate Calculation Department, 207…Estimated Loss Calculation Department, 208…Input Information, 209…Electronic Medical Record Information, 210…Inspection Command Information, 211…Medical Service Bill Information, 212…Hospital Information, 213…Guideline Information, 214…Operational Information 215…Patient Information, 216…Examination Value Information, 217…Patient Classification Information, 218…Representative Value of Examination, 219…Examination Implementation Rate, 220…Presumed Loss, 221…Improvement Threshold, 222…Improvement Examination Candidates, 223…Treatment Remuneration Information, 224…Examination Cost Information, 401…Example of First Display Screen, 401a…Example of Third Display Screen, 403…Improvement Threshold Setting Unit, 404…Patient Information Setting Unit, 405…Example of Second Display Screen, 409…Improvement Examination Candidate Display, 501 …Example of the fourth display screen, 603…Permission determination section, 604…Anonymized information within the facility, 605…Anonymized information between facilities, 606…Permission information, 701…Example of the fifth display screen, 702a…Patient information designation section for Group A, 702b…Patient information designation section for Group B, 703…Add button for groups, 704…Update button, 705…Example of the sixth display screen, 706…Examination item display section, 707…Examination value representative value display section, 708…Examination implementation rate display section, 709…Examination value information display section.

Claims

1. An information processing system for evaluating the value of multiple inspection items, characterized in that, The information processing system has the following features: The information acquisition unit acquires patient information related to multiple patients who are the subjects of the examination, and examination value information related to the effect obtained by performing the examination on each of the multiple patients for each of the examination items. The classification department classifies the multiple patients into multiple groups based on the patient information obtained by the information acquisition department and the examination value information; The representative value calculation unit calculates the representative value, which is an indicator value statistically derived from multiple inspection value information, according to the groups classified by the classification section.

2. The information processing system according to claim 1, characterized in that, The information processing system further includes an output unit that outputs the representative value calculated by the representative value calculation unit.

3. The information processing system according to claim 1, characterized in that, The information processing system also has: The inspection implementation rate calculation unit calculates the inspection implementation rate, which represents the proportion of the inspection items that have been implemented, according to the groups categorized by the classification section. The improvement inspection candidate calculation unit calculates the inspection efficiency of each inspection item in the classified group based on the inspection implementation rate calculated by the inspection implementation rate calculation unit and the representative value calculated by the representative value calculation unit, and calculates the improvement inspection candidates for the inspection items in the group based on the calculated inspection efficiency and a specified improvement efficiency threshold.

4. The information processing system according to claim 3, characterized in that, The information processing system further includes an output unit that highlights and outputs the improvement inspection candidates calculated by the improvement inspection candidate calculation unit.

5. The information processing system according to claim 3, characterized in that, The information acquisition unit acquires treatment remuneration information and examination cost information. The treatment remuneration information represents the individual examination remuneration for each of the multiple examination items, and the examination cost information represents the examination cost incurred by performing the multiple examination items separately. The information processing system further includes: an estimated loss calculation unit, which calculates, based on the examination implementation rate, the representative value, the treatment remuneration information, and the examination cost information, an estimated loss related to the cost of the examination item estimated by performing the examination item. The improvement inspection candidate calculation unit calculates the improvement inspection candidates based on the representative value, the inspection implementation rate, and the estimated loss.

6. The information processing system according to claim 1, characterized in that, The information processing system also has: The learning unit uses a dataset that takes the patient information and examination result information related to the examination results obtained by performing the examination on the patient as input and the examination value information as output to learn a model for calculating the value of the examination items. The calculation unit calculates the examination value information of the examination item based on the patient information, the examination result information, and the model input each time the examination is performed.

7. The information processing system according to claim 6, characterized in that, The information processing system also includes: an inspection value information storage unit, which stores the inspection value information. The calculation unit outputs the inspection value information to the inspection value information storage unit each time it calculates the inspection value information.

8. The information processing system according to claim 1, characterized in that, The information processing system also has: A first anonymization processing unit anonymizes the patient information and the examination value information; and The permission determination unit, when outputting the patient information and the examination value information, determines whether the user has permission to view the patient information and examination value information before anonymization by the first anonymization processing unit, based on the patient information before anonymization and permission information indicating whether the user has permission to view the examination value information.

9. The information processing system according to claim 8, characterized in that, The information processing system further includes an output unit, which outputs the patient information before anonymization by the first anonymization processing unit and information based on the examination value information when the permission determination unit determines that the patient has viewing permission, and outputs the patient information after anonymization by the first anonymization processing unit and information based on the examination value information when the permission determination unit determines that the patient does not have viewing permission.

10. The information processing system according to claim 9, characterized in that, The patient information includes patient identification information that identifies the patient who underwent the examination. The examination value information includes medical practitioner identification information that identifies the medical practitioner who underwent the examination. The first anonymization processing unit anonymizes the patient identification information and the medical practitioner identification information.

11. The information processing system according to claim 10, characterized in that, The information processing system includes: Information processing devices are installed in multiple facilities; The server is communicatively connected to the information processing device. The permission information also includes information indicating whether there is permission to view the facility name where the information processing device is installed. The information processing device includes: an information acquisition unit, a classification unit, a representative value calculation unit, a first anonymity processing unit, a permission determination unit, and an output unit. The server includes a second anonymization processing unit, which receives the patient information and the examination value information anonymized by the first anonymization processing unit from the information processing device, and anonymizes the facility identification information representing the facility name included or attached to the received anonymized patient information and examination value information. When the permission determination unit outputs the patient information and the examination value information to the output unit, it also determines, based on the permission information, whether it has permission to view the facility name that has been anonymized by the second anonymization processing unit and the examination value information. If the permission determination unit determines that the user has permission to view the facility name, the output unit outputs information based on the patient information and the examination value information before anonymization by the second anonymization processing unit. If the permission determination unit determines that the user does not have permission to view the facility name, the output unit outputs information based on the patient information and the examination value information after anonymization by the second anonymization processing unit.

12. An information processing method for evaluating the value of multiple inspection items separately, characterized in that, Obtain patient information related to multiple patients who are the subjects of the examination, and examination value information related to the effects obtained by performing the examinations on the multiple patients for each of the aforementioned examination items. Based on the obtained patient information and the examination value information, the multiple patients are classified into multiple groups. The representative value is calculated based on the statistical results of multiple inspection value information for each group classified.

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