Medical institution supervision method, device, computer equipment and storage medium

Abstract

The embodiments of the present application belong to the field of big data technology and are applied in the field of smart medical care. They involve a method, apparatus, computer equipment and storage medium for supervising medical institutions, including receiving disease data from various medical institutions, and grouping the disease data to obtain disease groups; obtaining the diagnosis and treatment costs in the disease data, and calculating the diagnosis and treatment weights of the target data set based on the diagnosis and treatment costs, wherein the target data set is a collection of disease data belonging to the same disease group in the medical institution; determining the abnormal indication label of each disease data based on a preset abnormality detection model and a preset standard diagnosis and treatment function library, calculating the total score of the medical institution based on the abnormal indication label and the diagnosis and treatment weight, and determining whether the total score of the medical institution is lower than the score threshold, and if so, sending a supervision abnormality notification to the supervision client. Among them, the disease group can be stored in the blockchain. The present application realizes effective supervision of medical institutions.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to methods, devices, computer equipment and storage media for supervising medical institutions. Background Art

[0002] At present, the supervision of medical institutions mostly adopts methods such as manual review of medical document sampling and video surveillance at the treatment site. These supervision methods have problems such as difficulty in formulating standards, insufficient coverage, unexplainable results and difficult to quantify assessments. Once the scale of medical treatment is slightly expanded, the cost of supervision will rise rapidly, and the supervision effect will be greatly reduced, affecting the scalability and sustainability of standardized diagnosis and treatment supervision. Although there are some intelligent supervision methods, there is still a situation where the supervision of medical institutions is not in place and it is difficult to effectively supervise medical institutions. Summary of the invention

[0003] The purpose of the embodiments of the present application is to propose a medical institution supervision method, apparatus, computer equipment and storage medium to achieve effective supervision of medical institutions.

[0004] In order to solve the above technical problems, the present application embodiment provides a medical institution supervision method, which adopts the following technical solution:

[0005] A medical institution supervision method comprises the following steps:

[0006] Receiving disease data from various medical institutions, and performing grouping operations on the disease data to obtain disease groups;

[0007] Obtaining the diagnosis and treatment costs in the disease data, and calculating the diagnosis and treatment weight of the target data set based on the diagnosis and treatment costs, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution;

[0008] Based on the preset anomaly detection model and the preset standard diagnosis and treatment function library, the abnormal indication label of each disease data is determined, the total score of the medical institution is calculated based on the abnormal indication label and the diagnosis and treatment weight, and it is determined whether the total score of the medical institution is lower than the score threshold. If so, a supervision abnormality notification is sent to the supervision client.

[0009] Furthermore, the step of calculating the total score of the medical institution based on the abnormality indication label and the diagnosis and treatment weight includes:

[0010] Determine the proportion of abnormal diagnosis and treatment times of each of the target data sets based on the abnormal indication label;

[0011] Based on the diagnosis and treatment weights of the target data set of the medical institution, the corresponding proportions of the number of abnormal diagnosis and treatment times are weighted and summed to obtain the total score of the medical institution.

[0012] Furthermore, the step of grouping the disease data to obtain disease groups includes:

[0013] Performing grouping operations on the medical institutions to obtain institution groups;

[0014] The disease data corresponding to each institution group are grouped to obtain the disease group.

[0015] Furthermore, the disease data corresponding to each institution group are grouped to obtain the disease group, including:

[0016] determining whether a disease name exists in the disease data;

[0017] If so, the disease code corresponding to the disease name is determined based on a preset code mapping relationship, and the disease data is grouped based on the disease code to obtain the disease group;

[0018] If not present, the department information in the disease data is obtained, and the disease data is grouped based on the department information to obtain the disease group.

[0019] Furthermore, there are multiple anomaly detection models, and the standard diagnosis and treatment function library includes multiple detection functions. The step of determining the abnormal indication label of each disease data based on the preset anomaly detection model and the preset standard diagnosis and treatment function library includes:

[0020] Determining the anomaly detection model and the detection function associated with the organization group as a target anomaly detection model and a target detection function;

[0021] The disease data corresponding to the corresponding institution group is detected based on the target anomaly detection model and the target detection function to obtain the anomaly indication label.

[0022] Furthermore, the step of determining the abnormal indication label of each disease data based on the preset abnormality detection model and the standard diagnosis and treatment function library includes:

[0023] Inputting the disease data into the abnormality detection model to obtain an output first abnormality indication label;

[0024] Performing abnormality judgment on the disease data based on the standard diagnosis and treatment function library to obtain a second abnormality indication label;

[0025] Determine whether the first abnormality indication label and the second abnormality indication label are the same, and if they are the same, use the first abnormality indication label or the second abnormality indication label as the abnormality indication label.

[0026] Further, after the step of determining whether the first abnormality indication label and the second abnormality indication label are the same, the method further includes:

[0027] If they are different, sending a confirmation signal to the user terminal, the confirmation signal carrying the disease data;

[0028] When a response signal sent by a user terminal is received, a response tag carried by the response signal is acquired as the abnormality indication tag, wherein the response signal corresponds to the confirmation signal.

[0029] In order to solve the above technical problems, the embodiment of the present application also provides a medical institution monitoring device, which adopts the following technical solution:

[0030] A medical institution monitoring device, comprising:

[0031] A receiving module, used for receiving disease data from various medical institutions, and performing grouping operations on the disease data to obtain disease groups;

[0032] a calculation module, used for obtaining the diagnosis and treatment costs in the disease data, and calculating the diagnosis and treatment weight of the target data set based on the diagnosis and treatment costs, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution;

[0033] The detection module is used to determine the abnormal indication label of each disease data based on a preset abnormality detection model and a preset standard diagnosis and treatment function library, calculate the total score of the medical institution based on the abnormal indication label and the diagnosis and treatment weight, and determine whether the total score of the medical institution is lower than the score threshold. If so, a supervision abnormality notification is sent to the supervision client.

[0034] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0035] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned medical institution supervision method when executing the computer-readable instructions.

[0036] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0037] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the above-mentioned medical institution supervision method.

[0038] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0039] This application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, which facilitates subsequent analysis and processing of disease data. Based on the diagnosis and treatment costs of disease data in different disease groups, the share of different types of disease data in each medical institution is determined as the diagnosis and treatment weight of the target data set. Through the anomaly detection model and the standardized diagnosis and treatment function library, the disease data is double-judged for anomalies, so as to determine the anomaly indication label to ensure the accuracy of the anomaly indication label, and then the total score of the medical institution is calculated based on the anomaly indication label and the diagnosis and treatment weight, which facilitates the judgment of the medical institution and improves the accuracy of the judgment of the medical institution's situation. When the total score of the medical institution is lower than the score threshold, a supervision anomaly notification is sent to the supervision client to achieve effective supervision of the medical institution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0042] Figure 2 is a flow chart of an embodiment of a medical institution supervision method according to the present application;

[0043] Figure 3 is a schematic structural diagram of an embodiment of a medical institution monitoring device according to the present application;

[0044] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application.

[0045] Attached figure numbers: 200, computer equipment; 201, memory; 202, processor; 203, network interface; 300, medical institution monitoring device; 301, receiving module; 302, computing module; 303, detection module. DETAILED DESCRIPTION

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0049] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0050] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0051] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0052] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0053] It should be noted that the medical institution supervision method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the medical institution supervision device is generally set in the server / terminal device.

[0054] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0055] Continue to refer Figure 2 , shows a flow chart of an embodiment of a medical institution supervision method according to the present application. The medical institution supervision method comprises the following steps:

[0056] S1: Receive disease data from various medical institutions, and group the disease data to obtain disease groups.

[0057] In this embodiment, the disease data of this application is obtained by associating the medical record table and the settlement record table of the medical insurance database. By grouping the disease data, it is convenient for subsequent processing of the disease data.

[0058] In this embodiment, the electronic device (eg, Figure 1 The server / terminal device shown in the figure can receive the disease data of each medical institution through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0059] Specifically, the step of grouping the disease data to obtain disease groups includes:

[0060] Performing grouping operations on the medical institutions to obtain institution groups;

[0061] The disease data corresponding to each institution group are grouped to obtain the disease group.

[0062] In this embodiment, medical institutions are grouped according to ownership form (public, private), grade (tertiary A, secondary B...) and type (comprehensive, health center, specialist, etc., where specialists are further grouped according to their specific professional types) to obtain institutional groups. Then the disease data under the institutional group is grouped to obtain disease groups, where the disease data mainly refers to the diagnosis name. For example, if there are five tertiary A hospitals, these five tertiary A hospitals are divided into one institutional group; then the disease data of these five hospitals are grouped to obtain disease groups. Each disease group can contain 5 disease members, and each disease member is a medical data record of a certain disease in a hospital. That is, the heart disease of medical institution A and the heart disease of medical institution B are divided into the same disease group. To achieve the comparability of diseases between medical institutions. In summary, this application first groups medical institutions to obtain multiple institutional groups, and then groups the disease data in the same institutional group but from different medical institutions to obtain multiple disease groups. In this way, it is ensured that in one institution group, when disease data of different medical institutions are grouped, the same type of disease data can be grouped into the same group with the same group name. Compared with the method of grouping the disease data of each medical institution separately, which may cause differences in the grouping of the same type of disease data in different medical institutions, this application reduces the differences that may occur when disease data is grouped between medical institutions, and increases the comparability between disease data.

[0063] The steps of grouping the disease data corresponding to each institution group to obtain the disease group include:

[0064] determining whether a disease name exists in the disease data;

[0065] If so, determining the disease code corresponding to the disease name based on a preset code mapping relationship, and performing a grouping operation on the disease data based on the disease code to obtain the disease group;

[0066] If not present, the department information in the disease data is obtained, and the disease data is grouped based on the department information to obtain the disease group.

[0067] In this embodiment, based on the disease name in the disease data, the disease code corresponding to the disease name is determined according to the preset code mapping relationship, wherein the disease code is the standard ICD-10 disease code. The disease data is preferentially grouped according to the disease code. If the disease data does not match the disease code, it is incorporated into the corresponding disease group based on the common name of the disease, the severity label, and the complication label in the disease data. If there is no disease name in the disease data, the disease data is grouped according to the department information it carries.

[0068] S2: Obtaining the medical expenses in the disease data, and calculating the medical weight of the target data set based on the medical expenses, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution.

[0069] In this embodiment, the diagnosis and treatment weight Wd = the proportion of the diagnosis and treatment costs of the same disease group in the medical institution to the total medical costs of the medical institution. For example: the disease group includes a heart disease group, and the weight of the heart disease group of the medical institution = the costs of the heart disease group in the medical institution / the total costs of the medical institution. The disease data in the medical institution is set to a comparable minimum grouping unit (i.e., the target data set), and the diagnosis and treatment weight is calculated and assigned for subsequent calculations.

[0070] S3: Determine the abnormal indication label of each disease data based on the preset abnormality detection model and the preset standard diagnosis and treatment function library, calculate the total score of the medical institution based on the abnormal indication label and the diagnosis and treatment weight, and determine whether the total score of the medical institution is lower than the score threshold. If so, send a supervision abnormality notification to the supervision client.

[0071] In this embodiment, the abnormal indication label is 0 for non-abnormal, and the abnormal indication label is 1 for abnormal. The number of abnormal disease data belonging to the same disease group in the medical institution / the total number of disease data in the medical institution = the proportion of abnormal diagnosis and treatment times. The disease data is double-detected and judged by the abnormal detection model and the preset standard diagnosis and treatment function library, thereby improving the accuracy of the abnormal indication label. Finally, the results are integrated and scored according to the diagnosis and treatment weights. Each supervised medical institution can get a comprehensive score to reflect its overall standard diagnosis and treatment health. Anomaly detection models include Copula-Based Outlier Detection (COPOD, anomaly detection based on statistical probability function), Deviation-based Outlier Detection (LMDD, anomaly detection based on deviation) and Isolation Forest (isolation forest) and other anomaly detection models. In practical applications, any one or more of the above anomaly detection models can be selected according to actual needs, and other anomaly detection models can also be selected, whichever is applicable.

[0072] Specifically, there are multiple anomaly detection models, and the standard diagnosis and treatment function library includes multiple detection functions. The step of determining the abnormal indication label of each disease data based on the preset anomaly detection model and the preset standard diagnosis and treatment function library includes:

[0073] Determining the anomaly detection model and the detection function associated with the organization group as a target anomaly detection model and a target detection function;

[0074] The disease data corresponding to the corresponding institution group is detected based on the target anomaly detection model and the target detection function to obtain the anomaly indication label.

[0075] In this embodiment, when abnormality detection is performed on disease data, according to the institution group or disease group in which the disease data is located, an abnormality detection model associated with the institution group or disease group and a detection function in a standard diagnosis and treatment function library (i.e., diagnosis and treatment rules) are selected to detect the disease data in the institution group or disease group to obtain an abnormality indication label. Different abnormality detection models and detection functions are used for different types of disease data to achieve adaptive adjustment of different disease data detection methods.

[0076] In addition, the step of determining the abnormal indication label of each disease data based on the preset abnormality detection model and the standard diagnosis and treatment function library includes:

[0077] Inputting the disease data into the abnormality detection model to obtain an output first abnormality indication label;

[0078] Performing abnormality judgment on the disease data based on the standard diagnosis and treatment function library to obtain a second abnormality indication label;

[0079] Determine whether the first abnormality indication label and the second abnormality indication label are the same, and if they are the same, use the first abnormality indication label or the second abnormality indication label as the abnormality indication label.

[0080] In this embodiment, the abnormal indication label is mutually verified by the detection function in the abnormal detection model and the standard diagnosis and treatment function library. The disease data is first standardized, and the standardized disease data is input into the called abnormal detection model, and the disease data is preliminarily obtained. The "whether it is abnormal" label F1, that is, the first abnormal indication label; according to the standard diagnosis and treatment function library, the detection function is called to verify the disease data, and a preliminary abnormal indication label F2 (that is, the second abnormal indication label) is obtained. If F1 and F2 are consistent, the value (that is, the value of F1 or F2) is assigned to the final abnormal indication label F. If F1 and F2 are inconsistent, human intervention is required to judge and improve the abnormal detection model and the standard diagnosis and treatment function library. The specific steps are as follows: Run the standard diagnosis and treatment function library, apply it to the full data set being tested, and obtain whether all medical records meet the following abnormal conditions and label them accordingly. Examples are as follows:

[0081]

[0082]

[0083] Wherein, after the step of determining whether the first abnormality indication label and the second abnormality indication label are the same, the method further includes:

[0084] If they are different, sending a confirmation signal to the user terminal, the confirmation signal carrying the disease data;

[0085] When a response signal sent by a user terminal is received, a response tag carried by the response signal is acquired as the abnormality indication tag, wherein the response signal corresponds to the confirmation signal.

[0086] In this embodiment, when the first abnormality indication label and the second abnormality indication label are different, the user sends a confirmation signal to request user assistance to complete the generation of the abnormality indication label.

[0087] In addition, the step of calculating the total score of the medical institution based on the abnormality indication label and the diagnosis and treatment weight includes:

[0088] Determine the proportion of abnormal diagnosis and treatment times of each of the target data sets based on the abnormal indication label;

[0089] Based on the diagnosis and treatment weights of the target data set of the medical institution, the corresponding proportions of the number of abnormal diagnosis and treatment times are weighted and summed to obtain the total score of the medical institution.

[0090] In this embodiment, by multiplying the proportion of abnormal diagnosis and treatment times by the diagnosis and treatment weight, the score of the target data set includes two dimensions: abnormal diagnosis and treatment and medical expenses. Then, the total score of the medical institution is calculated based on the score of the target data set, which can obtain a more comprehensive total score and achieve better evaluation and supervision of the medical institution.

[0091] This application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, which facilitates subsequent analysis and processing of disease data. Based on the diagnosis and treatment costs of disease data in different disease groups, the share of different types of disease data in each medical institution is determined as the diagnosis and treatment weight of the target data set. Through the anomaly detection model and the standardized diagnosis and treatment function library, the disease data is double-judged for anomalies, so as to determine the anomaly indication label to ensure the accuracy of the anomaly indication label, and then the total score of the medical institution is calculated based on the anomaly indication label and the diagnosis and treatment weight, which facilitates the judgment of the medical institution and improves the accuracy of the judgment of the medical institution's situation. When the total score of the medical institution is lower than the score threshold, a supervision anomaly notification is sent to the supervision client to achieve effective supervision of the medical institution.

[0092] It should be emphasized that in order to further ensure the privacy and security of the above disease groups, the above disease groups can also be stored in a blockchain node.

[0093] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0094] This application can be applied in the field of smart medical care, thereby promoting the construction of smart cities.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0096] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0097] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a medical institution monitoring device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0098] like Figure 3As shown, the medical institution supervision device 300 described in this embodiment includes: a receiving module 301, a calculation module 302 and a detection module 303. Among them: the receiving module 301 is used to receive the disease data of each medical institution, and group the disease data to obtain the disease group; the calculation module 302 is used to obtain the diagnosis and treatment costs in the disease data, and calculate the diagnosis and treatment weight of the target data set based on the diagnosis and treatment costs, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution; the detection module 303 is used to determine the abnormal indication label of each disease data based on the preset abnormal detection model and the preset standard diagnosis and treatment function library, calculate the total score of the medical institution based on the abnormal indication label and the diagnosis and treatment weight, and determine whether the total score of the medical institution is lower than the score threshold, and if so, send a supervision abnormality notification to the supervision client.

[0099] In this embodiment, the present application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, which facilitates subsequent analysis and processing of disease data. Based on the diagnosis and treatment costs of disease data in different disease groups, the share of different types of disease data in each medical institution is determined as the diagnosis and treatment weight of the target data set. Through the anomaly detection model and the standardized diagnosis and treatment function library, the disease data is double-abnormally judged to determine the abnormal indication label to ensure the accuracy of the abnormal indication label, and then the total score of the medical institution is calculated based on the abnormal indication label and the diagnosis and treatment weight, which is convenient for the judgment of the medical institution and improves the accuracy of the judgment of the medical institution situation. When the total score of the medical institution is lower than the score threshold, a supervision abnormality notification is sent to the supervision client to achieve effective supervision of the medical institution.

[0100] The receiving module 301 includes a first grouping submodule and a second grouping submodule, wherein the first grouping submodule is used to group the medical institutions to obtain institution groups; the second grouping submodule is used to group the disease data corresponding to each institution group to obtain the disease groups.

[0101] The second grouping submodule includes a determination unit, a first grouping unit and a second grouping unit, wherein the determination unit is used to determine whether there is a disease name in the disease data; the first grouping unit is used to determine the disease code corresponding to the disease name based on a preset coding mapping relationship when the disease name exists, and group the disease data based on the disease code to obtain the disease group; the second grouping unit is used to obtain the department information in the disease data when the disease name does not exist, and group the disease data based on the department information to obtain the disease group.

[0102] There are multiple abnormality detection models, and the standard diagnosis and treatment function library includes multiple detection functions. The detection module 303 includes a determination submodule and a detection submodule, wherein the determination submodule is used to determine the abnormality detection model and the detection function associated with the institution group as the target abnormality detection model and the target detection function; the detection submodule is used to detect the disease data corresponding to the corresponding institution group based on the target abnormality detection model and the target detection function to obtain the abnormality indication label.

[0103] The detection module 303 also includes a model detection submodule, a function library detection submodule and a comparison module, wherein the model detection submodule is used to input the disease data into the abnormality detection model to obtain an output first abnormality indication label; the function library detection submodule is used to perform abnormality judgment on the disease data based on the standard diagnosis and treatment function library to obtain a second abnormality indication label; the comparison module is used to determine whether the first abnormality indication label and the second abnormality indication label are the same. If they are the same, the first abnormality indication label or the second abnormality indication label is used as the abnormality indication label.

[0104] The detection module 303 also includes a signal sending submodule and a signal receiving submodule, wherein the signal sending submodule is used to send a confirmation signal to the user when the first abnormal indication label and the second abnormal indication label are different, and the confirmation signal carries the disease data; the signal receiving submodule is used to obtain the response label carried by the response signal as the abnormal indication label when receiving the response signal sent by the user terminal, wherein the response signal corresponds to the confirmation signal.

[0105] The detection module 303 also includes a proportion determination submodule and a weighting submodule, wherein the proportion determination submodule is used to determine the proportion of abnormal diagnosis and treatment times of each target data set based on the abnormal indication label, and the weighting submodule is used to weighted sum the corresponding proportions of abnormal diagnosis and treatment times based on the diagnosis and treatment weights of the target data set of the medical institution to obtain the total score of the medical institution.

[0106] This application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, which facilitates subsequent analysis and processing of disease data. Based on the diagnosis and treatment costs of disease data in different disease groups, the share of different types of disease data in each medical institution is determined as the diagnosis and treatment weight of the target data set. Through the anomaly detection model and the standardized diagnosis and treatment function library, the disease data is double-judged for anomalies, so as to determine the anomaly indication label to ensure the accuracy of the anomaly indication label, and then the total score of the medical institution is calculated based on the anomaly indication label and the diagnosis and treatment weight, which facilitates the judgment of the medical institution and improves the accuracy of the judgment of the medical institution's situation. When the total score of the medical institution is lower than the score threshold, a supervision anomaly notification is sent to the supervision client to achieve effective supervision of the medical institution.

[0107] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0108] The computer device 200 includes a memory 201, a processor 202, and a network interface 203 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 200 having components 201-203, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0109] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0110] The memory 201 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 201 can be an internal storage unit of the computer device 200, such as a hard disk or memory of the computer device 200. In other embodiments, the memory 201 can also be an external storage device of the computer device 200, such as a plug-in hard disk equipped on the computer device 200, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 201 can also include both the internal storage unit of the computer device 200 and its external storage device. In this embodiment, the memory 201 is generally used to store the operating system and various application software installed on the computer device 200, such as computer-readable instructions of the medical institution supervision method, etc. In addition, the memory 201 can also be used to temporarily store various data that have been output or will be output.

[0111] The processor 202 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 202 is generally used to control the overall operation of the computer device 200. In this embodiment, the processor 202 is used to run computer-readable instructions or process data stored in the memory 201, such as computer-readable instructions for running the medical institution supervision method.

[0112] The network interface 203 may include a wireless network interface or a wired network interface. The network interface 203 is generally used to establish a communication connection between the computer device 200 and other electronic devices.

[0113] In this embodiment, the present application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, performs dual anomaly judgment on disease data through anomaly detection models and standardized diagnosis and treatment function libraries, ensures the accuracy of anomaly indication labels, and calculates the total score of the medical institution, which facilitates the evaluation of the medical institution and realizes effective supervision of the medical institution.

[0114] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the medical institution supervision method as described above.

[0115] In this embodiment, the present application achieves stronger comparability between disease data in various medical institutions by uniformly grouping disease data in medical institutions, performs dual anomaly judgment on disease data through anomaly detection models and standardized diagnosis and treatment function libraries, ensures the accuracy of anomaly indication labels, and calculates the total score of the medical institution, which facilitates the evaluation of the medical institution and realizes effective supervision of the medical institution.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0117] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A medical institution supervision method, characterized in that: The steps include: Receiving disease data from various medical institutions, and performing grouping operations on the disease data to obtain disease groups; Obtaining the diagnosis and treatment costs in the disease data, and calculating the diagnosis and treatment weight of the target data set based on the diagnosis and treatment costs, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution; Determine the abnormality indication label of each piece of the disease data based on the preset abnormality detection model and the preset standard diagnosis and treatment function library, calculate the total score of the medical institution based on the abnormality indication label and the diagnosis and treatment weight, and determine whether the total score of the medical institution is lower than the score threshold, and if so, send a supervision abnormality notification to the supervision client; The step of grouping the disease data to obtain disease groups includes: Performing grouping operations on the medical institutions to obtain institution groups; Performing grouping operations on the disease data corresponding to each institution group to obtain the disease group; The step of grouping the medical institutions to obtain institution groups includes: Medical institutions were grouped according to ownership form and type to obtain institutional groups; The step of calculating the total score of the medical institution based on the abnormality indication label and the diagnosis and treatment weight includes: Determine the proportion of abnormal diagnosis and treatment times of each of the target data sets based on the abnormal indication label; Based on the diagnosis and treatment weights of the target data set of the medical institution, weighted summing up the corresponding proportions of abnormal diagnosis and treatment times to obtain the total score of the medical institution; The step of determining the abnormal indication label of each disease data based on the preset abnormality detection model and the standard diagnosis and treatment function library includes: Inputting the disease data into the abnormality detection model to obtain an output first abnormality indication label; Performing abnormality judgment on the disease data based on the standard diagnosis and treatment function library to obtain a second abnormality indication label; determining whether the first abnormality indication label and the second abnormality indication label are the same, and if they are the same, using the first abnormality indication label or the second abnormality indication label as the abnormality indication label; Wherein, after the step of determining whether the first abnormality indication label and the second abnormality indication label are the same, the method further includes: If they are different, sending a confirmation signal to the user terminal, the confirmation signal carrying the disease data; When a response signal sent by a user terminal is received, a response tag carried by the response signal is acquired as the abnormality indication tag, wherein the response signal corresponds to the confirmation signal.

2. The medical institution supervision method according to claim 1, characterized in that: The disease data corresponding to each institution group are grouped respectively, and the steps of obtaining the disease group include: determining whether a disease name exists in the disease data; If so, determining the disease code corresponding to the disease name based on a preset code mapping relationship, and performing a grouping operation on the disease data based on the disease code to obtain the disease group; If not present, the department information in the disease data is obtained, and the disease data is grouped based on the department information to obtain the disease group.

3. The medical institution supervision method according to claim 1, characterized in that: There are multiple abnormality detection models, and the standard diagnosis and treatment function library includes multiple detection functions. The step of determining the abnormality indication label of each disease data based on the preset abnormality detection model and the preset standard diagnosis and treatment function library includes: Determining the anomaly detection model and the detection function associated with the organization group as a target anomaly detection model and a target detection function; The disease data corresponding to the corresponding institution group is detected based on the target anomaly detection model and the target detection function to obtain the anomaly indication label.

4. A medical institution monitoring device, characterized in that: include: A receiving module, used for receiving disease data from various medical institutions, and performing grouping operations on the disease data to obtain disease groups; a calculation module, used for obtaining the diagnosis and treatment costs in the disease data, and calculating the diagnosis and treatment weight of the target data set based on the diagnosis and treatment costs, wherein the target data set is a set of the disease data belonging to the same disease group in the medical institution; A detection module, used to determine the abnormality indication label of each piece of the disease data based on a preset abnormality detection model and a preset standard diagnosis and treatment function library, calculate the total score of the medical institution based on the abnormality indication label and the diagnosis and treatment weight, and determine whether the total score of the medical institution is lower than the score threshold, and if so, send a supervision abnormality notification to the supervision client; The receiving module includes a first grouping submodule and a second grouping submodule, wherein the first grouping submodule is used to group the medical institutions to obtain the institution groups; the second grouping submodule is used to group the disease data corresponding to each institution group to obtain the disease groups; The first grouping submodule is specifically used to group medical institutions according to ownership form and type to obtain institution groups; The detection module further includes a proportion determination submodule and a weighting submodule, wherein the proportion determination submodule is used to determine the proportion of abnormal diagnosis and treatment times of each target data set based on the abnormal indication label and the weighting submodule is used to perform weighted summation on the corresponding proportion of abnormal diagnosis and treatment times based on the diagnosis and treatment weight of the target data set of the medical institution to obtain the total score of the medical institution; The detection module further includes a model detection submodule, a function library detection submodule and a comparison module, wherein the model detection submodule is used to input the disease data into the abnormality detection model to obtain an output first abnormality indication label; the function library detection submodule is used to perform abnormality judgment on the disease data based on the standard diagnosis and treatment function library to obtain a second abnormality indication label; the comparison module is used to determine whether the first abnormality indication label and the second abnormality indication label are the same, and if they are the same, the first abnormality indication label or the second abnormality indication label is used as the abnormality indication label; Among them, the detection module also includes a signal sending submodule and a signal receiving submodule, wherein the signal sending submodule is used to send a confirmation signal to the user when the first abnormal indication label and the second abnormal indication label are different, and the confirmation signal carries the disease data; the signal receiving submodule is used to obtain the response label carried by the response signal as the abnormal indication label when receiving the response signal sent by the user terminal, wherein the response signal corresponds to the confirmation signal.

5. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the medical institution supervision method as described in any one of claims 1 to 3 when executing the computer-readable instructions.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the medical institution supervision method according to any one of claims 1 to 3.

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