Computer system, information processing method, and program

Abstract

To support efficient use of RWD (RealWorldData).SOLUTION: The computer system is accessibly connected to a first database that stores clinical data acquired at a clinical site and including information on a treated patient as an item, and a second database that stores non-clinical data acquired for research purposes and including information on a treated patient group as an item. The computer system selects a predetermined number of pieces of clinical data from the first database, generates comparison data by aggregating the selected pieces of clinical data, executes statistical analysis for analyzing a difference between items using the non-clinical data and the comparison data having the same treatment content, and records a result of the statistical analysis.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to a technology for supporting the efficient use of Real World Data. [Background technology]

[0002] In recent years, in the field of pharmaceutical development, there has been a growing focus on utilizing data from actual clinical settings, rather than just data on specific patients collected in clinical trials and clinical research. Information based on medical treatments obtained in actual clinical settings is called Real World Data (RWD), and research is being conducted on the actual treatment status and effectiveness of treatments for various diseases.

[0003] Patent Document 1 discloses a technology that structures text information such as electronic medical record data, which is one type of RWD, and identifies patients who are candidates for clinical trial subjects using the structured information and information on the clinical trial criteria. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-180080 Summary of the Invention [Problem to be solved by the invention]

[0005] Until now, understanding treatment status and drug efficacy has been conducted on a limited number of patients, such as in clinical trials and clinical research. As modern medicine becomes increasingly personalized, it is difficult to understand treatment status and drug efficacy in actual clinical settings using only data from a select number of patients. Therefore, the use of RWD, which can be analyzed on all patients, is being promoted.

[0006] The difficulty of utilizing RWD varies depending on the type of disease, and the more diverse the treatment patterns, the higher the difficulty. The barriers to utilizing RWD are the enormous amount of data and the fact that it is not structured like clinical trial and clinical research data, which requires a huge amount of time and manpower to analyze.

[0007] The present invention aims to support the efficient use of RWD. [Means for solving the problem]

[0008] A representative example of the invention disclosed in the present application is as follows: That is, a computer system includes a processor, a storage device connected to the processor, and a communication interface connected to the processor, and is accessible to a first database storing clinical data obtained in a clinical setting and including, as items, information about treated patients, and a second database storing non-clinical data obtained for research purposes and including, as items, information about a group of treated patients, wherein the processor selects a predetermined number of the clinical data from the first database, generates comparative data by aggregating the selected clinical data, performs a first statistical analysis using the non-clinical data and the comparative data for which the treatment is the same, to analyze differences in the items, and records the results of the first statistical analysis. [Effects of the Invention]

[0009] According to the present invention, it is possible to present information for extracting clinical data (RWD) to be analyzed, thereby enabling efficient use of RWD. Note that problems, configurations, and effects other than those described above will become clear from the following description of the preferred embodiment of the invention. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an example of a system configuration according to a first embodiment. [Figure 2]FIG. 1 is a diagram illustrating an example of a hardware configuration of a treatment status / drug efficacy investigation system according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of information stored in a regimen management DB according to the first embodiment. [Figure 4] FIG. 2 is a diagram showing an example of information stored in a non-clinical data DB according to the first embodiment. [Figure 5] FIG. 2 is a diagram showing an example of information stored in a clinical data DB according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a statistical analysis method DB according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of an analysis result DB according to the first embodiment. [Figure 8A] FIG. 10 is a diagram illustrating an example of information stored in an extracted information DB according to the first embodiment. [Figure 8B] FIG. 10 is a diagram illustrating an example of information stored in an extracted information DB according to the first embodiment. [Figure 9] 1 is a flowchart illustrating an outline of processing executed by the treatment status / drug efficacy survey system of the first embodiment. [Figure 10] 10 is a flowchart illustrating an example of a pre-statistical analysis process executed by the treatment status / drug efficacy survey system of the first embodiment. [Figure 11] 1 is a flowchart illustrating an example of a statistical analysis process executed by the treatment status / drug efficacy survey system of the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment is an example for explaining the present invention, and appropriate omissions and simplifications have been made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0012] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0013] Examples of various types of information may be described using expressions such as "table," "list," and "queue," but the various types of information may also be expressed using data structures other than these. For example, various types of information such as "XX table," "XX list," and "XX queue" may also be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable.

[0014] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. When there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0015] In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., a CPU or a GPU) and performs processing defined by the program using storage resources (e.g., a memory) and interface devices (e.g., a communication port). Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0016] A program may be installed on a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs. [Example]

[0017] In the examples, a technology for extracting a patient group to be analyzed from RWD will be described as an example of an information system that supports investigation of actual clinical treatment status / drug efficacy using RWD. This information system can, for example, reduce the cost of analyzing drug efficacy and actual treatment status in actual clinical practice, thereby contributing economically.

[0018] First, an example of the system configuration will be described with reference to Fig. 1. Fig. 1 is a diagram illustrating an example of the system configuration of a first embodiment.

[0019] The system includes a treatment status / drug efficacy survey system (information system) 100 and a terminal 101 .

[0020] The treatment status / drug efficacy survey system 100 is a system that reads data, processes the data, performs statistical analysis, extracts patient groups, and outputs the analysis results. The treatment status / drug efficacy survey system 100 includes a data acquisition unit 111, a data processing unit 112, a statistical analysis unit 113, a patient extraction unit 114, and a result output unit 115.

[0021] The data acquisition unit 111 acquires clinical data 130 and non-clinical data 140 .

[0022] The clinical data 130 is data corresponding to RWD obtained at a clinical site, and includes, for example, receipt data 131, patient basic data 132, electronic medical record data 133, and test data 134. In this specification, it is assumed that one piece of clinical data 130 exists for one patient. The data acquisition unit 111 acquires the clinical data 130 of multiple patients from the clinical site.

[0023] The medical receipt data 131 is data including information on a regimen extracted from a patient's medication information, etc. A regimen refers to a combination of treatments, such as a treatment plan that combines multiple medications in the field of cancer. The patient basic data 132 is data including information that characterizes an individual patient, such as the patient's ID, age, sex, place of residence, and disease name. The electronic medical record data 133 is data including daily medical records and treatment records. The test data 134 is data including the results of various tests performed on the patient, such as the results of clinical tests and image data.

[0024] Non-clinical data 140 is data collected for research purposes and includes, for example, article data 141 and clinical trial data 142 .

[0025] The paper data 141 and clinical trial data 142 are data extracted from published papers and research results, such as background information on patients, treatment details, and treatment results. In this example, it is assumed that one nonclinical data 140 exists for one patient group. One or more nonclinical data 140 can be obtained from a paper or research results. Note that data for one patient in a paper or research results can also be treated as one nonclinical data 140.

[0026] The data processing unit 112 processes the clinical data 130 and non-clinical data 140 acquired by the data acquisition unit 111 into a data format that allows statistical analysis. The acquired data and processed data are stored in the non-clinical data DB 121 and the clinical data DB 122.

[0027] The statistical analysis unit 113 performs statistical analysis using the clinical data 130 and non-clinical data 140 processed by the data processing unit 112. The results of the statistical analysis are stored in the analysis result DB 124.

[0028] The patient extraction unit 114 extracts clinical data 130 that meets predetermined extraction conditions based on the results of the statistical analysis.

[0029] The result output unit 115 outputs the clinical data 130 extracted by the patient extraction unit 114 and the like.

[0030] Each function of the treatment status / drug efficacy survey system 100 can be controlled from the terminal 101. Details of each DB held by the treatment status / drug efficacy survey system 100 will be described later.

[0031] The terminal 101 is a terminal operated by a user who uses the treatment status / drug efficacy survey system 100, and is, for example, a general-purpose computer, a smartphone, a tablet terminal, or the like.

[0032] The treatment status / drug efficacy survey system 100 and the terminal 101 are connected by wire or wirelessly. In addition, a plurality of terminals 101 may be connected to the treatment status / drug efficacy survey system 100.

[0033] For example, a fifth generation mobile communication system, so-called 5G (5th Generation), which enables "multiple simultaneous connections" and "ultra-low latency," can be used for communication between the treatment status / drug efficacy survey system 100 and the terminal 101. By utilizing the characteristics of new systems from 5G onwards, communication delays can be suppressed even when a large number of terminals 101 are connected simultaneously.

[0034] The treatment status / drug efficacy survey system 100 may be realized using a cloud system. Furthermore, a new system after 5G may be used for wireless communication between the treatment status / drug efficacy survey system 100 on the cloud system and the terminal 101.

[0035] FIG. 2 is a diagram showing an example of the hardware configuration of the treatment status / drug efficacy survey system 100 according to the first embodiment.

[0036] The treatment status / drug efficacy survey system 100 includes a CPU (Central Processing Unit) 201, a memory 202, a peripheral IF 203, a storage device 204, and a communication IF 205. Each hardware element is communicably connected via a bus 206.

[0037] The CPU 201 is a computing device that executes programs stored in the storage device 204. The memory 202 is a volatile storage device that stores the programs executed by the CPU 201. The memory 202 is also used as a working area and a temporary buffer.

[0038] The storage device 204 is composed of a magnetic disk device, a flash ROM (Read Only Memory), etc., and stores the OS, various drivers, various application programs, and various types of information used by the programs.

[0039] The CPU 201 reads out a program stored in the storage device 204, loads it into the memory 202, and executes it to realize the following functional units: a data acquisition unit 111, a data processing unit 112, a statistical analysis unit 113, a patient extraction unit 114, and a result output unit 115.

[0040] The peripheral IF 203 is an interface for connecting to various peripheral devices such as input / output devices such as a mouse, keyboard, and monitor, and external storage such as a USB (Universal Serial Bus) memory.

[0041] The communication IF 205 is an interface for communicating with the outside world by the treatment status / drug efficacy survey system 100. There may be two or more communication IFs 205.

[0042] FIG. 3 is a diagram showing an example of information stored in the regimen management DB 120 of the first embodiment.

[0043] The regimen management DB 120 stores a table 300, such as that shown in Fig. 3, for each disease. The table 300 shown in Fig. 3 is a table for managing regimens for treating "cancer," and stores records including a regimen ID 301 and a description 302. One record exists for one regimen.

[0044] Regimen ID 301 is a field that stores an ID that represents identification information of a regimen.

[0045] The content 302 is a group of fields that store specific details of the regimen. For example, the content 302 includes fields such as drug name, dosage, administration time, administration date, and one course. Note that the above-mentioned fields are merely examples and are not limiting.

[0046] For example, the "Drug Name" of the record with regimen ID 301 of "R1" indicates that the drug name used is "aaa." Note that Figure 3 shows a case where there are no overlapping drugs used, but if the same drug has different information such as dosage, it will be managed as a different regimen.

[0047] FIG. 4 is a diagram showing an example of information stored in the non-clinical data DB 121 of the first embodiment.

[0048] The non-clinical data DB 121 stores the acquired non-clinical data 140, and also stores a table 400 for each disease, as shown in Fig. 4. The table 400 shown in Fig. 4 is a table related to the "cancer" patient group, and stores records including a regimen ID 401, patient characteristics 402, and treatment results 403. One record exists for one regimen.

[0049] Regimen ID 401 is the same field as Regimen ID 301.

[0050] The patient characteristics 402 are a group of fields that store information about the characteristics of the patients who make up the patient group. The patient characteristics 402 include patient-specific information such as gender and age, and information that indicates the condition of the patient group, such as the name of the disease and the severity of the disease.

[0051] The treatment outcome 403 is a group of fields that stores information about the treatment outcome, including survival time and progression-free survival time.

[0052] For example, the patient group (record) with regimen ID 401 set to "1" has an average age of 65±5 years, a disease severity of "low", a survival period of "180±20 days", and a progression-free survival period of "80±10 days".

[0053] The patient characteristics 402 and treatment outcomes 403 are used in statistical analysis.

[0054] In the following description, the patient characteristics, medical conditions, treatment results, etc. included in the clinical data 130 and non-clinical data 140 will be collectively referred to as items.

[0055] FIG. 5 is a diagram showing an example of information stored in the clinical data DB 122 according to the first embodiment.

[0056] The clinical data DB 122 stores the acquired clinical data 130, and also stores a table 500 as shown in FIG. 5 for each disease. The table 500 shown in FIG. 5 is a table related to patients with "cancer," and stores records including a regimen ID 501, patient characteristics 502, and treatment results 503. One record is comparison data used in statistical analysis to compare with non-clinical data 140. One record exists for one regimen.

[0057] Regimen ID 501 is the same field as Regimen ID 301. Patient characteristics 502 are the same field group as Patient characteristics 402. Treatment outcome 503 is the same field group as Treatment outcome 503.

[0058] For example, the patient group (record) with regimen ID 501 of "1" has an average age of 62±5 years, a disease severity of "high", a survival period of "150±20 days", and a progression-free survival period of "70±15 days".

[0059] The patient characteristics 502 and treatment outcomes 503 are used in statistical analysis.

[0060] If necessary, the data to be stored in the table 500 may be generated by selecting or combining the receipt data 131, the patient basic data 132, the electronic medical record data 133, and the test data 134.

[0061] FIG. 6 is a diagram illustrating an example of the statistical analysis method DB 123 according to the first embodiment.

[0062] The statistical analysis method DB 123 stores a table 600. The table 600 stores records including a file ID 601, a storage directory 602, a file name 603, and a statistical analysis method 604. One record exists for each statistical analysis result.

[0063] File ID 601 is a field that stores an ID that identifies the file in which the analysis results are stored. Storage directory 602 is a field that stores the name of the directory in which the file is stored. File name 603 is a field that stores the name of the file. Statistical analysis method 604 is a field that stores the program used for the statistical analysis.

[0064] For example, the analysis result of file ID 601 "F001" is stored in the directory " / home / user / table / " and the file name is "x_cancer.001_table." It also indicates that the analysis result was obtained by statistical analysis using the program "x_cancer.001_stat."

[0065] FIG. 7 is a diagram illustrating an example of the analysis result DB 124 according to the first embodiment.

[0066] The analysis result DB 124 stores the analysis results in a file format. The analysis results are, for example, a table 700 as shown in Fig. 7. The table 700 stores records including a regimen ID 701, a variable 702, and an analysis result 703.

[0067] Regimen ID 701 is the same field as Regimen ID 301.

[0068] Variable 702 is a field that stores the names of the items to be compared in clinical data 130 and non-clinical data 140. In this embodiment, a statistical analysis is performed to compare the differences between the items.

[0069] The analysis results 703 are a group of fields that store the results of statistical analysis. The analysis results 703 store indices calculated by statistical analysis, such as mean, median, probability values ​​such as p-value, probability distribution, area, etc.

[0070] For example, the first record shows the results of a statistical analysis using data for regimen ID 701 "R1" with "age" as a variable. The p-value is "0.24," which indicates that there is generally no significant difference in age. It also shows that the average age of the patient group corresponding to non-clinical data 140 is "68±3," and the average age of the patient group corresponding to clinical data 130 is "79±4."

[0071] The user can search for and refer to regimens that satisfy the extraction conditions from the analysis results by inputting a conditional expression using variables and index thresholds as extraction conditions using the terminal 101. For example, the user can input "p-value of age is 0.05 or less and p-value of survival time is 0.05 or less" as extraction conditions.

[0072] 8A and 8B are diagrams illustrating an example of information stored in the extracted information DB 125 according to the first embodiment.

[0073] The extracted information DB 125 stores a table 800 and a table 810. The table 800 is a table that stores clinical data 130 of regimens that meet the extraction conditions. The table 810 is a table that stores the results of statistical analysis using the clinical data 130 and non-clinical data 140 of regimens that meet the extraction conditions.

[0074] The table 800 stores records including a regimen ID 801, a patient ID 802, patient characteristics 803, and treatment results 804. One record exists for one patient.

[0075] Regimen ID 801 is the same field as Regimen ID 301. Patient ID 802 is a field that stores an ID that identifies a patient managed in the clinical data 130. Patient characteristics 803 is a field group that stores information about patient characteristics. Treatment results 804 is a field group that stores information about treatment results.

[0076] For example, the first record stores information about the patient characteristics and treatment results of a patient whose patient ID 802 is "P1." Specifically, it indicates that the patient's age is "68 years old," survival time is "160 days," and progression-free survival time is "120 days."

[0077] Table 810 stores records including variables 811 and analysis results 812. There are as many entries as there are analyzed variables. The variables 811 and analysis results 812 are in the same fields as the variables 702 and analysis results 703.

[0078] The user can appropriately select the variables to be acquired by operating the terminal 101. For example, the user can select "age," "gender," "grade of malignancy," and "survival period."

[0079] Next, a description will be given of the processing executed by the treatment status / drug efficacy survey system 100. Fig. 9 is a flowchart illustrating an outline of the processing executed by the treatment status / drug efficacy survey system 100 of the first embodiment.

[0080] The data acquisition unit 111 of the treatment status / drug efficacy research system 100 acquires the clinical data 130 and the non-clinical data 140 (step S901).

[0081] Next, the data processing unit 112 of the treatment status / drug efficacy research system 100 generates the tables 400 and 500 using the clinical data 130 and the non-clinical data 140 (step S902). Here, a method for generating the tables 400 and 500 will be described.

[0082] The data processing unit 112 processes the nonclinical data 140 to match the data structure of the table 400. If the nonclinical data 140 does not include a regimen ID, the data processing unit 112 identifies a regimen based on the information included in the nonclinical data 140 and the regimen management DB 120, and assigns a regimen ID to the nonclinical data 140. The data processing unit 112 generates the table 400 by aggregating the processed nonclinical data 140 for each disease. Note that if the data structure of the nonclinical data 140 is the same as the data structure of the table 400, the data processing unit 112 can generate the table 400 without processing the nonclinical data 140.

[0083] The data processing unit 112 generates a set of clinical data 130 that serves as a population for each disease, and samples representative clinical data 130 from the population. The data processing unit 112 processes the sampled clinical data 130 to match the data structure of the table 500. If the clinical data 130 does not include a regimen ID, the data processing unit 112 identifies the regimen based on the information included in the clinical data 130 and the regimen management DB 120, and assigns a regimen ID to the clinical data 130. For each disease, the data processing unit 112 statistically processes the processed clinical data 130 with the same regimen ID, and generates records for the table 500. The data processing unit 112 generates the table 500 by aggregating the records for each disease.

[0084] The sampling conditions may be set manually using the terminal 101, or may be set in a program that realizes the data processing unit 112. For example, patient characteristics such as age and sex may be set manually as sampling conditions, or an algorithm may be set in the data processing unit 112 that clusters patient groups for each regimen and selects a representative patient from each cluster.

[0085] Next, the treatment status / drug efficacy survey system 100 executes a pre-statistical analysis process using the tables 400 and 500 (step S903). The pre-statistical analysis process will be described in detail later.

[0086] Next, the treatment status / drug efficacy survey system 100 extracts the clinical data 130 of patients that meet the extraction conditions from the clinical data DB 122, and executes statistical analysis processing using the non-clinical data 140 and the extracted clinical data 130 (step S904). The statistical analysis processing will be described in detail later.

[0087] Next, the treatment status / drug efficacy survey system 100 generates output information based on the results of the pre-statistical analysis process and the statistical analysis process, and outputs the output information to the terminal 101 (step S905). For example, output information including tables 700, 800, and 810 is generated.

[0088] The output information can be presented in various formats, such as a table format, a graph format, or a network diagram format.

[0089] Since the amount of data to be processed is small due to sampling, the pre-statistical analysis process can be performed at low cost.

[0090] Detailed statistical analysis is possible by acquiring clinical data 130 that meets the extraction conditions based on the results of the pre-statistical analysis process. For example, if there is a significant difference between the items in the clinical data 130 and the non-clinical data 140, it means that the clinical situation differs from the research results. Therefore, by identifying the items and regimens with significant differences, it is possible to identify a patient group to be investigated to gain new knowledge.

[0091] FIG. 10 is a flowchart illustrating an example of a pre-statistical analysis process executed by the treatment status / drug efficacy survey system 100 according to the first embodiment.

[0092] The statistical analysis unit 113 receives input regarding the statistical analysis method (step S1001). At this time, the type of disease to be analyzed is also received as input. Selectable statistical analysis methods can be selected depending on the purpose, ranging from general epidemiological statistics and medical statistics to mathematical programs. The statistical analysis method in this embodiment is a method for analyzing whether there is a difference between items in two patient groups.

[0093] Next, the statistical analysis unit 113 performs settings for executing statistical analysis based on a statistical analysis method (step S1002). The settings for the statistical analysis method differ depending on the statistical analysis method to be used.

[0094] Next, the statistical analysis unit 113 reads out the tables 400 and 500 to be analyzed from the non-clinical data DB 121 and the clinical data DB 122 (step S1003). At this time, the statistical analysis unit 113 generates a list of regimen IDs that are common to the tables 400 and 500 to be analyzed.

[0095] Next, the statistical analysis unit 113 selects a regimen (step S1004).

[0096] Next, the statistical analysis unit 113 performs statistical analysis using records including the regimen ID selected from the tables 400 and 500 based on the setting of the statistical analysis method (step S1005). For example, the statistical analysis is performed based on a known statistical significance test method, and indices such as the mean, median, probability values ​​such as p-value, probability distribution, area, etc. are calculated.

[0097] Next, the statistical analysis unit 113 records the analysis result in the analysis result DB 124 (step S1006). Specifically, the statistical analysis unit 113 adds a record including the analysis result to the analysis result DB 124.

[0098] Next, the statistical analysis unit 113 records information about the statistical analysis method in the statistical analysis method DB 123 (step S1007). Specifically, the statistical analysis unit 113 adds to the statistical analysis method DB 123 a record including information about the storage destination of the analysis result and the statistical analysis method.

[0099] Next, the statistical analysis unit 113 determines whether or not the processing has been completed for all regimens (step S1008).

[0100] If the processing has not been completed for all regimens, the statistical analysis unit 113 returns to step S1004 and executes the same processing. If the processing has been completed for all regimens, the statistical analysis unit 113 ends the pre-statistical analysis processing.

[0101] FIG. 11 is a flowchart illustrating an example of the statistical analysis process executed by the treatment status / drug efficacy survey system 100 according to the first embodiment.

[0102] The patient extraction unit 114 receives input of extraction conditions (step S1101). The extraction conditions include at least a pair of conditional expressions using a variable and a threshold value of an index. The threshold value may be set arbitrarily or may be set based on an index calculated in the pre-statistical analysis process. The extraction conditions may also include the type of disease.

[0103] The patient extraction unit 114 refers to the analysis result DB 124 for each disease to determine whether or not each regimen satisfies the extraction condition, and identifies the regimen that satisfies the extraction condition (step S1102).

[0104] The patient extraction unit 114 outputs an execution instruction including information on the identified regimen (a list of regimen IDs) to the statistical analysis unit 113.

[0105] The statistical analysis unit 113 selects a regimen (step S1103).

[0106] The statistical analysis unit 113 refers to the statistical analysis method DB 123 based on the file ID of the analysis result corresponding to the identified regimen, identifies the statistical analysis method for the selected regimen, and sets it (step S1104).

[0107] The statistical analysis unit 113 acquires the clinical data 130 of the identified regimen from the clinical data DB 122 (step S1105), and also acquires the non-clinical data 140 of the identified regimen from the non-clinical data DB 121 (step S1106). Here, all of the clinical data 130 that form the population are acquired.

[0108] The statistical analysis unit 113 performs statistical analysis using the acquired clinical data 130 and the acquired non-clinical data 140 (step S1107). The process of step S1107 is similar to the process of step S1005, except that the data to be processed is different.

[0109] Next, the statistical analysis unit 113 records the analysis results in the extracted information DB 125 (step S1108). Specifically, the statistical analysis unit 113 adds the extracted clinical data 130 to table 800 of the extracted information DB 125, and also adds a record including the analysis results to table 810.

[0110] Next, the statistical analysis unit 113 records information about the statistical analysis method in the statistical analysis method DB 123 (step S1109). The process of step S1109 is the same as the process of step S1007.

[0111] Next, the statistical analysis unit 113 determines whether or not the processing has been completed for all regimens (step S1110).

[0112] If the processing has not been completed for all regimens, the statistical analysis unit 113 returns to step S1103 and executes the same processing. If the processing has been completed for all regimens, the statistical analysis unit 113 notifies the patient extraction unit 114 of the completion of the processing. After receiving the notification from the statistical analysis unit 113, the patient extraction unit 114 ends the statistical analysis processing.

[0113] According to the present invention, the treatment status / pharmacologic efficacy survey system 100 can generate information (analysis results) for identifying an analysis target by performing statistical analysis using patient group data generated from sampled clinical data 130 and non-clinical data 140. Based on the information, a user can extract clinical data 130 that meets predetermined extraction conditions and perform detailed statistical analysis using the extracted clinical data 130. In other words, RWD can be used efficiently.

[0114] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments are provided to explain the present invention in detail, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, some of the configurations of each embodiment can be added to, deleted from, or replaced with other configurations.

[0115] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The present invention can also be realized by software program code that implements the functions of the embodiments. In this case, a storage medium on which the program code is recorded is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media for providing such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, solid-state drives (SSDs), optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, and ROMs.

[0116] Furthermore, the program code that realizes the functions described in this embodiment can be implemented in a wide range of program or script languages, such as assembler, C / C++, perl, Shell, PHP, Python, and Java (registered trademark).

[0117] Furthermore, the program code of the software that realizes the functions of the embodiments may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the processor of the computer may read and execute the program code stored in the storage means or storage medium.

[0118] In the above-described embodiment, the control lines and information lines are those that are considered necessary for the explanation, and not all control lines and information lines are necessarily shown in the product. All components may be interconnected. [Explanation of symbols]

[0119] 100 Treatment Status / Drug Efficacy Survey System 101 terminals 111 Data Acquisition Unit 112 Data Processing Department 113 Statistical Analysis Department 114 Patient extraction part 115 Result output section 120 Regimen Management DB 121 Non-clinical Data DB 122 Clinical Data DB 123 Statistical analysis method DB 124 Analysis result DB 125 Extract information DB 130 Clinical Data 131 Receipt data 132 Patient Basic Data 133 Electronic medical record data 134 Test Data 140 Non-clinical data 141 Paper data 142 Clinical trial data 201 CPU 202 memory 203 Peripheral IF 204 Storage device 205 Communication Interface 206 Bus

Claims

1. 1. A computer system comprising: a processor, a storage device connected to the processor, and a communication interface connected to the processor; a first database storing clinical data items obtained in a clinical setting, the items including information about treated patients; and a second database storing non-clinical data items obtained for research purposes, the items including information about treated patient populations; The processor: selecting a predetermined number of the clinical data from the first database, and generating comparative data by aggregating the selected clinical data; A computer system characterized by performing a first statistical analysis to analyze differences in the items using the non-clinical data and the comparative data, which have the same treatment content, and recording the results of the first statistical analysis.

2. 2. The computer system of claim 1, the clinical data and the non-clinical data include the items for identifying treatment content; The processor: A computer system that generates the comparative data by aggregating the selected clinical data for each patient who has received a common treatment.

3. 3. The computer system according to claim 2, The result of the first statistical analysis includes identification information for identifying the treatment content, the type of the item, and a statistical index calculated in the first statistical analysis; The processor: accepting extraction conditions including a conditional expression using the type of the item and the threshold value of the statistical index; Identifying the results of the first statistical analysis that satisfy the extraction condition; Obtaining, from the first database, the clinical data of the target treatment content corresponding to the identification information included in the identified result of the first statistical analysis; A computer system characterized by performing a second statistical analysis to analyze differences in the items using the acquired clinical data and the non-clinical data of the target treatment content, and recording the results of the second statistical analysis.

4. 4. The computer system according to claim 3, A computer system characterized in that the non-clinical data is data obtained from papers and clinical studies.

5. 4. The computer system according to claim 3, A computer system characterized in that the treatment content is a combination of multiple treatments.

6. An information processing method executed by a computer system, comprising: The computer system a processor, a storage device connected to the processor, and a communication interface connected to the processor; a first database storing clinical data items obtained in a clinical setting, the items including information about treated patients; and a second database storing non-clinical data items obtained for research purposes, the items including information about treated patient populations; The information processing method includes: a first step in which the processor selects a predetermined number of the clinical data from the first database and generates comparison data by aggregating the selected clinical data; and a second step of the processor performing a first statistical analysis to analyze differences between the items using the non-clinical data and the comparison data for which the treatment content is the same, and recording the results of the first statistical analysis.

7. 7. The information processing method according to claim 6, the clinical data and the non-clinical data include the items for identifying treatment content; An information processing method characterized in that the first step includes a step in which the processor generates the comparison data by aggregating the selected clinical data for each patient with a common treatment.

8. A program to be executed by a computer, The computer a processor, a storage device connected to the processor, and a communication interface connected to the processor; a first database storing clinical data items obtained in a clinical setting, the items including information about treated patients; and a second database storing non-clinical data items obtained for research purposes, the items including information about treated patient populations; The program a first step of selecting a predetermined number of the clinical data from the first database and generating comparison data by aggregating the selected clinical data; a first statistical analysis for analyzing differences in the items using the non-clinical data and the comparative data for which the treatment content is the same, and a second procedure for recording the results of the first statistical analysis.

9. 9. The program according to claim 8, the clinical data and the non-clinical data include the items for identifying treatment content; the first step includes a step of generating the comparative data by aggregating the selected clinical data for each patient who has received a common treatment; The program, wherein the second step includes a step of performing the first statistical analysis using the non-clinical data and the comparative data having the same treatment content.

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