CN116978527A

CN116978527A - DIP-based hospital data analysis method and device

Info

Publication number: CN116978527A
Application number: CN202310985014.2A
Authority: CN
Inventors: 秦晓宏; 刘翠翠; 胡清; 薛程程; 周婷
Original assignee: Shanghai Clinbrain Information Technology Co Ltd
Current assignee: Shanghai Clinbrain Information Technology Co Ltd
Priority date: 2023-08-07
Filing date: 2023-08-07
Publication date: 2023-10-31

Abstract

The application provides a DIP-based hospital data analysis method and a DIP-based hospital data analysis device, wherein the DIP-based hospital data analysis method comprises the steps of merging a history disease group built in a group database with a real-time disease group corresponding to each target hospital, and determining an updated DIP-based target disease group in the group database and at least one corresponding target case under each target disease group; classifying hospitals to be detected according to the preset hospital part attributions based on the updated DIP to be in the target group in the group database, and determining department group values and department group values of the hospitals to be detected under the attributions of different hospital departments; determining whether the cost scores of department groups in the attribution of the hospital department have cost hyperbranched. The application realizes real-time group entering monitoring, can carry out self-adaptive adjustment according to the medical service mode of the hospital to be detected, and further improves the accuracy and efficiency of the payment of the disease component value of the hospital to be detected.

Description

DIP-based hospital data analysis method and device

Technical Field

The application relates to the technical field of big data, in particular to a hospital data analysis method and device based on DIP.

Background

With the development of society and the advancement of science and technology, the payment mode based on big data of paying according to disease score (DIP) has slowly replaced the payment mode of medical service project expense, and the disease data can be objectively classified according to the commonality characteristics among the discovery of disease score payment (DIP), and combined standardized positioning and disease score are formed in the whole sample case data in a certain area range, and the complex state, the resource consumption level and the clinical behavior specification of disease and disease combination are objectively reflected.

Because the medical service capability of different medical institutions in different areas and the medical fields with good treatment are different, the disease score payment (DIP) mode in the prior art cannot analyze and evaluate hospital data of different medical institutions, and further cannot realize the self-adaptive adjustment of the medical service mode of the medical institutions of the patient, so that the accuracy of the disease score payment (DIP) on the classification of the disease component values after combining with each medical institution is lower, and the classification efficiency is lower.

Disclosure of Invention

Accordingly, the present application aims to provide a DIP-based hospital data analysis method and device, which can adaptively adjust according to the medical service mode of the hospital to be detected, thereby improving the accuracy and efficiency of payment of the disease component value of the hospital to be detected.

The embodiment of the application provides a DIP-based hospital data analysis method, which comprises the following steps: inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device, and determining real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources;

combining the constructed historical disease group in the DIP group database with the real-time disease group corresponding to each target hospital, and determining the updated target disease group in the DIP group database and at least one corresponding target case under each target disease group, wherein the target disease group is any disease group data in the constructed DIP group database or each real-time disease group;

classifying hospitals to be detected according to the part attribution of the preset hospitals based on the updated DIP entering the target disease group in the group database, and determining department disease groups and department disease component values of the hospitals to be detected under the control of different hospital departments;

For any department group to which any hospital department in the hospital to be detected belongs, determining whether a cost score of the department group in the hospital department belongs to a cost hyperbranched or not based on the department group value, medical insurance cost of the department group and cost of the department group in the hospital department belongs to so as to complete analysis of the hospital data to be detected.

Further, the hospital department attribution includes a department, the department group includes a department group, the to-be-detected hospitals are classified according to preset hospital department attributions, and the determination of the department group and the department group value of the to-be-detected hospitals under the attribution of different hospital departments includes: classifying hospitals to be detected according to departments, and determining department disease groups and department disease component values of the hospitals to be detected under different departments.

Further, the hospital department attribution further includes a diagnosis and treatment group, the department disease group further includes a diagnosis and treatment group, after the hospitals to be detected are classified according to departments, and the department disease group and the department disease component values of the hospitals to be detected under different departments are determined, the DIP-based hospital data analysis method further includes: aiming at department disease groups of any department of a hospital to be detected, classifying the department disease groups according to diagnosis and treatment groups, and determining different diagnosis and treatment disease groups and diagnosis and treatment disease group values.

Further, the hospital department attribution further includes a doctor type, the department group further includes a doctor group, after the department group for any department of the hospital to be detected classifies the department group according to the diagnosis and treatment group, the DIP-based hospital data analysis method further includes:

and classifying the diagnosis and treatment groups according to the types of doctors aiming at the diagnosis and treatment groups of any diagnosis and treatment group of the hospital to be detected, and determining different doctor groups and doctor group values.

Further, the determining whether the cost score of the department group in the hospital department attribution has a cost hyperbranched based on the department group value, the medical insurance fee, and the cost of the department group in the hospital department attribution includes: determining a cost aggregate score of a department disease group according to the department disease group score of the department disease group, medical insurance cost and cost of the department disease group in the attribution of a hospital department;

and determining whether the cost total score of the department group in the attribution of the hospital department has cost hyperbranched or not based on the cost total score and the hospitalization cost score consumed by the case corresponding to the department group.

Further, the determining whether the cost hyperbranched exists in the cost aggregate score of the department group in the hospital department attribution based on the cost aggregate score and the hospitalization cost score consumed by the case corresponding to the department group, includes: judging whether the hospitalization expense scores consumed by the cases corresponding to the department disease groups are the total hospitalization expense after the cases are completed; if yes, comparing the cost total score with the hospitalization cost score consumed by the case corresponding to the department group, and judging the size of the cost total score and the hospitalization cost score;

if the cost total score is greater than or equal to the hospitalization cost score, determining that the cost total score of the department group in the attribution of the hospital department has cost hyperbranched;

if the cost aggregate score is smaller than the hospitalization cost score, determining that the cost aggregate score of the department group in the hospital department attribution does not have cost hyperbranched.

Further, after determining whether the cost score of the department group in the hospital department attribution has a cost hyperbranched or not based on the department disease component value, the medical insurance cost of the department disease group and the cost of the department disease group in the hospital department attribution for any department disease group in the hospital to be detected, the DIP-based hospital data analysis method further includes: and determining service evaluation results of the hospital to be detected under different classifications according to the different department disease groups, the different diagnosis and treatment disease groups and the different doctor disease groups.

Further, the standard historical case data of the target area, the standard historical disease catalog mapping table of the target area, and the standard historical surgical catalog mapping table of the target area are determined by:

acquiring initial historical case data of a target area, an initial historical disease catalog mapping table of the target area and an initial operation catalog mapping table of the target area;

and respectively cleaning data and checking data according to the initial historical case data, the initial historical disease catalog mapping table and the initial operation catalog mapping table, and determining standard historical case data of the target area, the standard historical disease catalog mapping table of the target area and the standard historical operation catalog mapping table of the target area.

Further, after determining whether the cost score of the department group in the hospital department attribution has a cost hyperbranched or not based on the department disease component value, the medical insurance cost of the department disease group and the cost of the department disease group in the hospital department attribution for any department disease group in the hospital to be detected, the DIP-based hospital data analysis method further includes: and determining a target important disease species according to different department disease groups and department disease component values, different diagnosis and treatment disease groups and diagnosis and treatment disease component values and different doctor disease groups and doctor disease component values so as to realize the important management of the target important disease species.

The embodiment of the application also provides a DIP-based hospital data analysis device, which comprises: the first determining module is used for inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device to determine real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources;

the updating module is used for merging the constructed historical disease group in the DIP group-entering database with the real-time disease group corresponding to each target hospital, and determining the updated target disease group in the DIP group-entering database and at least one corresponding target case under each target disease group, wherein the target disease group is any disease group data in the constructed DIP group-entering database or each real-time disease group;

the second determining module is used for classifying hospitals to be detected according to the preset hospital part attribution based on the updated DIP entering the target disease group in the group database, and determining department disease groups and department disease group values of the hospitals to be detected under the different hospital department attribution;

And a third determining module, configured to determine, for any department group to which any hospital department in the hospitals to be detected belongs, whether a cost score of the department group in the hospital department belongs has a cost hyperbranched based on the department group score, a medical insurance fee of the department group, and a cost fee of the department group in the hospital department belongs, so as to complete analysis of the hospital data to be detected.

The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate through the bus when the electronic device is running, and the machine-readable instructions when executed by the processor perform the steps of the DIP-based hospital data analysis apparatus as described above.

Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a DIP-based hospital data analysis apparatus as described above.

Compared with the application method of DIP data in the prior art, the implementation method and device provided by the embodiment of the application combine the history group of the built DIP in the group database and the real-time group of the corresponding target hospitals based on the DIP, determine the updated DIP in the target group of the group database and at least one corresponding target case under each target group of the target group, classify hospitals to be detected according to the attribution of preset hospital parts, and determine the department group and department group values of the hospitals to be detected under the attribution of different hospital departments; for any department disease group to which any hospital department belongs in the hospital to be detected, whether the cost score of the department disease group in the hospital department belongs is hyperbranched or not is determined based on the department disease component value of the department disease group, the medical insurance cost and the cost of the department disease group in the hospital department belongs, so that disease type analysis of target hospital data under different hospital departments is completed, real-time group entering monitoring is realized, self-adaptive adjustment can be carried out according to the medical service mode of the hospital to be detected, and further the accuracy and efficiency of disease component value payment of the hospital to be detected are improved.

In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 shows one of the flowcharts of the DIP-based hospital data analysis method provided by the embodiment of the application;

FIG. 2 is a block flow diagram of an embodiment of a DIP-based hospital data analysis method according to an embodiment of the present application;

FIG. 3 is a second flowchart of a DIP-based hospital data analysis method according to an embodiment of the present application;

FIG. 4 shows one of the block diagrams of the DIP-based hospital data set provided by the embodiment of the present application;

FIG. 5 shows a second block diagram of a DIP-based hospital data set provided by an embodiment of the present application;

Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

In the figure:

400-DIP-based hospital data analysis device; 410-a first determination module; 420-updating the module; 430-a second determination module; 440-a third determination module; 450-a fourth determination module; 460-a fifth determination module; 600-an electronic device; 610-a processor; 620-memory; 630-bus.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment obtained by a person skilled in the art without making any inventive effort falls within the scope of protection of the present application.

First, an application scenario to which the present application is applicable will be described. The present application can be applied to big data.

According to research, the medical service capability of different medical institutions in different areas and the medical field with good treatment are different, but the disease seed score payment (DIP) mode in the prior art cannot analyze and evaluate hospital data of different medical institutions, so that the medical service mode of the medical institutions cannot be adjusted in a self-adaptive manner, the disease seed score payment (DIP) is led to have lower accuracy and lower division efficiency on the disease component values after being combined with each medical institution.

Based on the above, the embodiment of the application provides the DIP-based hospital data analysis method and device, which realize real-time group entry monitoring and can carry out self-adaptive adjustment according to the medical service mode of the hospital to be detected, thereby improving the accuracy and efficiency of paying for the disease component value of the hospital to be detected.

Referring to fig. 1, fig. 1 is a flowchart of a DIP-based hospital data analysis method according to an embodiment of the present application.

As shown in fig. 1, the DIP-based hospital data analysis method provided by the embodiment of the application includes the following steps:

S101, inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device, and determining real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources.

In the step, after a built DIP (digital information processor) access group database generated based on initial historical case data is determined, initial real-time case data of each target hospital, an initial disease catalog mapping table of each target hospital and an operation catalog mapping table of the target hospital are obtained, then data cleaning and data verification are sequentially carried out on each initial real-time case data, the initial historical disease catalog mapping table and the initial operation catalog mapping table respectively, and standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital, a standard operation catalog mapping table of the target hospital and a standard operation disease catalog mapping table are determined.

After the standard real-time case data of each target hospital, the standard disease catalog mapping table of the target hospital, the standard operation catalog mapping table of the target hospital and the standard operation disease catalog mapping table are determined, the standard real-time case data of each target hospital, the standard disease catalog mapping table of the target hospital and the standard operation catalog mapping table of the target hospital are input into a preset DIP (digital information processor) group entering device to determine real-time disease groups and real-time disease component values of each real-time disease group.

In the above, for the initial real-time case data of each target hospital, different data acquisition modes or acquisition platforms can be adopted according to different application scenarios, and the embodiment provided by the application adopts the CRD platform to acquire the real-time initial real-time case data.

And if the initial real-time case data does not pass the data cleaning and/or the data verification, the initial real-time case data which does not pass the data cleaning and/or the data verification is subjected to the data quality analysis, whether the initial real-time case data needs to be used continuously or not is determined according to the quality analysis result, if the initial real-time case data does not need to be used continuously, the corresponding initial real-time case data is used as the abandoned data to be deleted, and if the initial real-time case data can be used continuously, the data checksum data cleaning is carried out again on the corresponding initial real-time case data. The embodiment provided by the application can be applied to the fields of medical insurance payment, fund supervision, hospital management and the like. And the specific use flows of medical insurance payment, fund supervision and hospital management need to be constructed in combination with the specific corresponding flows in the corresponding fields, and are not described in detail herein.

S102, merging the constructed historical disease group in the DIP group entering database with the real-time disease group corresponding to each target hospital, and determining the updated target disease group in the DIP group entering database and at least one corresponding target case under each target disease group, wherein the target disease group is any disease group data in the constructed DIP group entering database or each real-time disease group.

In the step, the constructed historical disease groups in the DIP group database and the real-time disease groups corresponding to the target hospitals are combined to determine the updated DIP group database, and the updated DIP group database can be used for conveniently acquiring required disease group data of the hospitals to be detected or other target hospitals needing to be detected, so that complicated processes of interfacing various business systems of the hospitals to be detected or other target hospitals needing to be detected are avoided, and further the data acquisition efficiency is improved.

Here, the data of the same disease in the historical disease group and the real-time disease group corresponding to each target hospital are combined, the generated DIP group entering data of each disease in each disease group is determined to be more accurate, rich and comprehensive, and the accuracy of analyzing the data of the hospitals to be detected is greatly improved.

In this way, the constructed DIP group-entering database is based on the economic principles of 'random' and 'average', the internal rule and the association relation of disease types and scores required by corresponding disease types for treatment are determined through real massive historical case data, the characteristics of the historical case data of the disease types are extracted and combined, the average value of each disease and treatment resource consumption in the area is compared with the average value of whole sample resource consumption to form a DIP score, the DIP group-entering database is mainly suitable for settlement of hospitalization medical expenses, and the adaptability and expansibility of the DIP group-entering database can be explored and applied to establishment of clinic payment standards.

The built DIP group-entering database does not reach a total budget index for each target hospital, and under a total budget mechanism, a score point value is calculated according to annual medical insurance payment total, medical insurance payment proportion and total score values of medical institution cases, namely the DIP group-entering database is a disease group database in which medical insurance departments form payment standards based on disease type scores and score point values, and the built DIP group-entering database can realize standardized payment for each case in each target hospital and does not confirm medical resource consumption in a medical service item mode.

S103, classifying hospitals to be detected according to the preset hospital part attributions based on the updated DIP entering the target disease group in the group database, and determining department disease groups and department disease group values of the hospitals to be detected under the different hospital department attributions.

In this step, the assignment of the preset hospital part in the embodiment provided by the application can be adjusted in a self-defined manner according to different application scenes and usage scenes, and the assignment of the preset hospital part in the embodiment provided by the application takes the organization architecture of hospital personnel as an example, and classifies the assignment according to the organization architecture of the hospital personnel of the preset hospital, so as to determine the department disease groups and department disease component values of the hospital to be detected in different hospital departments (namely under different organization architectures).

Here, the preset hospital part attribution in the embodiment provided by the present application may be specifically but not limited to: department-diagnosis and treatment group-doctor type, and the embodiment provided by the application can be used for targeted control aiming at different types of disease types.

Optionally, the hospital department attribution includes a department, the department group includes a department group, and the step S103 includes the following substeps:

and step 1031, classifying the hospitals to be detected, and determining department disease groups and department disease component values of the hospitals to be detected under different departments.

In this step, each department group of diseases exists in the hospital department of at least one department.

Optionally, the hospital department attribution further includes a diagnosis and treatment group, the department disease group further includes a diagnosis and treatment disease group, and the step S103 further includes the following substeps:

Sub-step 1032, classifying the department groups according to diagnosis and treatment groups aiming at the department groups of any department of the hospital to be detected, and determining different diagnosis and treatment groups and diagnosis and treatment group values.

In this step, each treatment group is present in at least one treatment group of at least one department.

Optionally, the hospital department attribution further includes a doctor type, the department group further includes a doctor group, and the step S103 further includes the following substeps:

substep 1033, classifying diagnosis and treatment groups according to doctor types for any diagnosis and treatment group of the hospital to be detected, and determining different doctor groups and doctor group values.

In this step, each physician group is present in at least one physician type of at least one treatment group of at least one department.

Thus, in the embodiments provided by the application, each doctor group may be in a different diagnosis group or department group, and the disease component value of each doctor group or each diagnosis group is the same.

S104, aiming at any department disease group to which any hospital department belongs in the hospital to be detected, determining whether the cost score of the department disease group in the hospital department belongs is in charge of charge hyperbranched based on the department disease component value, medical insurance cost and cost of the department disease group in the hospital department belongs to the department disease group so as to complete analysis of the hospital data to be detected.

In the step, for any department group, a cost score of the department group in the hospital department attribution is determined based on the department group score of the DIP of the department group, a medical insurance fee (the medical insurance fee is determined according to the medical insurance policy under the current time) of the medical institution in the current state and a cost fee (including but not limited to at least one of consumable fee and doctor diagnosis and treatment service fee) of the department group in the hospital department attribution, and whether the cost is hyperbranched is determined based on the cost score and a hospitalization cost score of a proportion corresponding to the department group.

Thus, the consumable fee is a cost fee for medical materials consumed for treating the department group; the classification of the doctor diagnosis and treatment service fees can be customized according to different diagnosis and treatment scenes corresponding to different target hospitals, and the doctor diagnosis and treatment service fees in the embodiment provided by the application can be specifically classified according to the types of doctors and the grades of the doctors.

Optionally, the step S104 includes the following substeps:

substep 1041, determining a cost aggregate score for a department group of diseases based on the department group value, the medical insurance fee, and the cost of the department group of diseases in the hospital department affiliation.

Here, assuming that the component cost of the department disease for treating pneumonia is 1000 yuan, the medical premium for treating pneumonia and the cost of the department disease group in the home of the hospital department are 1000 yuan and 800 yuan respectively in the embodiment provided by the application, the total cost score for treating pneumonia is 2800 yuan.

And a substep 1042 of determining whether the cost hyperbranched exists in the cost aggregate score of the department group in the hospital department attribution based on the hospitalization cost score consumed by the case corresponding to the department group.

Firstly judging whether the hospitalization cost scores consumed by the cases corresponding to the department disease groups are the total hospitalization cost after the cases are completed, and then comparing the cost aggregate scores with the hospitalization cost scores consumed by the cases corresponding to the department disease groups after the hospitalization cost scores consumed by the cases corresponding to the department disease groups are the total hospitalization cost after the cases are completed, and judging the magnitude of the cost aggregate scores and the hospitalization cost scores; if the cost total score is greater than or equal to the hospitalization cost score, determining that the cost total score of the department group in the attribution of the hospital department has cost hyperbranched; if the cost aggregate score is smaller than the hospitalization cost score, determining that the cost aggregate score of the department group in the hospital department attribution does not have cost hyperbranched.

After the analysis of the hospital data to be detected is completed, the hospital data to be detected after the analysis can be displayed under different authorities, such as a management end with the highest authority and a doctor end with the authority of each doctor.

The management end can realize management departments such as hospital, medical insurance department, medical department, performance department and the like, analyze all kinds and groups of diseases of all departments and all hospitals, can transfer and view evaluation details of the departments, diagnosis and treatment groups and doctors, and simultaneously has all system authorities of the doctor end.

The doctor end mainly carries out real-time group entering detection of standard history case data of a target area and analysis of respective organization architecture (service unit) and disease types of the family room, so that prompt of abnormal cases in advance, monitoring of group entering in advance and real-time service evaluation are realized.

Fig. 2 is a flow chart of an embodiment of a DIP-based hospital data analysis method according to an embodiment of the present application, as shown in fig. 2:

in the figure, firstly, data cleaning and data checking are respectively carried out on initial historical case data, an initial historical disease catalog mapping table and an initial operation catalog mapping table in sequence, standard historical case data of a target area, a standard historical disease catalog mapping table of the target area and a standard historical operation catalog mapping table of the target area, which are qualified in both data cleaning and data checking, are input into a preset DIP grouping device, and any unqualified initial historical case data, initial historical disease catalog mapping table and initial operation catalog mapping table are subjected to data quality analysis.

And simultaneously, respectively carrying out data cleaning and data verification on the initial real-time case data of the hospital to be detected, the standard disease catalog mapping table of the initial hospital and the standard operation catalog mapping table of the initial hospital, inputting the standard real-time case data which are qualified in both data cleaning and data verification, the standard disease catalog mapping table of the target hospital and the standard operation catalog mapping table of the target hospital into a preset DIP (digital information processor) group entering device, and carrying out data quality analysis on any unqualified standard real-time case data, the standard disease catalog mapping table of the target hospital and the standard operation catalog mapping table of the target hospital.

And merging the data in the two preset DIP group entering devices, and determining the updated DIP group entering target disease groups in the group database and at least one corresponding target case under each target disease group.

Based on the disease component value of any disease group, the medical insurance cost and the cost of department disease groups in the attribution of the hospital department, the analysis of the hospital data to be detected is completed, and the attribution architecture of the disease group data in the hospital to be detected is determined.

Compared with the prior art, the DIP-based hospital data analysis method provided by the embodiment of the application combines the constructed DIP-based historical disease groups in the group database and the real-time disease groups corresponding to all target hospitals based on the DIP, determines the updated DIP-based target disease groups in the group database and at least one corresponding target case under each target disease group, classifies hospitals to be detected according to the part attribution of the preset hospitals based on the updated DIP-based target disease groups in the group database, and determines department disease groups and department disease group values of the hospitals to be detected under the department attribution of different hospitals; for any department disease group to which any hospital department belongs in the hospital to be detected, whether the cost score of the department disease group in the hospital department belongs is hyperbranched or not is determined based on the department disease component value of the department disease group, the medical insurance cost and the cost of the department disease group in the hospital department belongs, so that disease type analysis of target hospital data under different hospital departments is completed, real-time group entering monitoring is realized, self-adaptive adjustment can be carried out according to the medical service mode of the hospital to be detected, and further the accuracy and efficiency of disease component value payment of the hospital to be detected are improved.

According to the embodiment of the application, through the built DIP group database and the real-time disease group corresponding to the target hospital, a unified standard system and resource allocation mode are established, the transparency and fairness of management are improved, so that the hospitals to be detected and the medical insurance institutions can establish communication channels under the unified standard framework, the analysis of data of the target hospitals is conducted, the treatment kinetic energy of a medical service supply side is stimulated, a plurality of medical institutions including the hospitals to be detected are promoted, social requirements are met by a proper method and reasonable cost, the use efficiency of the medical insurance fund is further improved, the medical insurance expense is reasonably managed and controlled, the standardization, the refinement and the scientization of medical insurance fund are realized, a set of brand-new service evaluation system is provided for a plurality of medical institutions including the hospitals to be detected, the monitoring and the adjustment of the hospitals to be detected are facilitated, and the data support is further provided for the adjustment of the hospitals to be detected.

Referring to fig. 3, fig. 3 is a second flowchart of a DIP-based hospital data analysis method according to an embodiment of the application. As shown in fig. 3, the DIP-based hospital data analysis method provided by the embodiment of the application includes the following steps:

S301, establishing a DIP grouping database according to standard historical case data of a target area, a standard historical disease catalog mapping table of the target area, a standard historical operation catalog mapping table of the target area, a preset DIP grouping device and a DIP medical insurance policy of the target area.

In this step, in step S301, standard history case data of a target area, a standard history disease directory map of the target area, and a standard history surgery directory map of the target area are determined by the following substeps:

and 1, acquiring initial historical case data of a target area, an initial historical disease catalog mapping table of the target area and an initial operation catalog mapping table of the target area.

In the step, initial historical case data of a target area in a preset time node, an initial historical disease catalog mapping table of the target area and an initial operation catalog mapping table of the target area are obtained.

Here, the preset time node in the embodiment provided by the present application may be, but not limited to, three years, because the medical policy will also be adjusted in the preset time node around three years.

And 2, respectively cleaning data and checking data according to the initial historical case data, the initial historical disease catalog mapping table and the initial operation catalog mapping table, and determining the standard historical case data of the target area, the standard historical disease catalog mapping table of the target area and the standard historical operation catalog mapping table of the target area.

In this step, the initial historical case data, the initial historical disease catalog mapping table and the initial surgical catalog mapping table that do not pass the data cleansing and the data verification are subjected to the data quality analysis.

S302, inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device, and determining real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources.

S303, merging the constructed historical disease group in the DIP group-entering database with the real-time disease group corresponding to each target hospital, and determining the updated DIP group-entering database and at least one corresponding target case under each target disease group, wherein the target disease group is any disease group data in the constructed DIP group-entering database or each real-time disease group.

S304, classifying hospitals to be detected according to the part attribution of the preset hospitals based on the updated DIP into the target disease group in the group database, and determining department disease groups and department disease group values of the hospitals to be detected under the attribution of different hospital departments.

S305, determining whether a charge hyperbranched exists in the charge score of the department disease group in the hospital department attribution or not according to the department disease group value, the medical insurance charge and the cost charge of the department disease group in the hospital department attribution of any department disease group in the hospital to be detected, so as to complete analysis of the hospital data to be detected.

S305, determining target important disease types according to different department disease groups and department disease component values, different diagnosis and treatment disease groups and diagnosis and treatment disease component values and different doctor disease groups and doctor disease component values so as to realize the important management of the target important disease types.

S306, determining service evaluation results of the hospital to be detected under different classifications according to different department disease groups, different diagnosis and treatment disease groups and different doctor disease groups.

The descriptions of S302 to S305 may refer to the descriptions of S101 to S104, and the same technical effects can be achieved, which will not be described in detail.

Referring to fig. 4 and 5, fig. 4 is a first block diagram of a DIP-based hospital data analysis according to an embodiment of the present application, and fig. 5 is a second block diagram of a DIP-based hospital data analysis according to an embodiment of the present application. As shown in fig. 4, the DIP-based hospital data analysis apparatus 400 includes:

A first determining module 410, configured to input standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital, and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device, and determine real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, where the real-time disease component values are used to characterize the consumption degree of medical resources by each disease species in the real-time disease groups.

And the updating module 420 is configured to combine the constructed historical disease group in the DIP-in group database with the real-time disease group corresponding to each target hospital, and determine the updated target disease group in the DIP-in group database and at least one corresponding target case under each target disease group, where the target disease group is any disease group data in the constructed DIP-in group database or each real-time disease group.

The second determining module 430 is configured to classify the hospitals to be detected according to the preset hospital part affiliations based on the updated DIP entering the target group in the group database, and determine the department group and the department group values of the hospitals to be detected under the affiliations of different hospital departments.

Optionally, the hospital department attribution in the second determining module 430 includes departments, the department disease groups include department disease groups, the hospitals to be detected are classified according to the preset hospital part attributions, and determining the department disease groups and department disease group values of the hospitals to be detected under the different hospital department attributions includes:

classifying hospitals to be detected according to departments, and determining department disease groups and department disease component values of the hospitals to be detected under different departments.

Optionally, the hospital department assignment in the second determining module 430 further includes a diagnosis and treatment group, and the department group further includes a diagnosis and treatment group.

After classifying the hospitals to be detected according to departments and determining department disease groups and department disease component values of the hospitals to be detected under different departments, the DIP-based hospital data analysis method further comprises the following steps:

aiming at department disease groups of any department of a hospital to be detected, classifying the department disease groups according to diagnosis and treatment groups, and determining different diagnosis and treatment disease groups and diagnosis and treatment disease group values.

Optionally, the hospital department attribution further includes a doctor type, the department group further includes a doctor group, the department group is classified according to diagnosis and treatment groups for any department of the hospital to be detected, and after different diagnosis and treatment groups and diagnosis and treatment component values are determined, the DIP-based hospital data analysis method further includes:

A third determining module 440, configured to determine, for any department group belonging to any hospital department in the hospitals to be detected, whether a cost score of the department group belonging to the hospital department has a cost hyperbranched based on the department group score, a medical insurance fee, and a cost fee of the department group belonging to the hospital department, so as to complete analysis of the hospital data to be detected.

Optionally, the third determining module 440 determines whether there is a charge hyperbranched in the charge score of the department group in the hospital department home based on the department group value of the department group, the medical insurance fee, and the cost of the department group in the hospital department home, including:

and determining the cost total score of the department disease group according to the department disease group score of the department disease group, the medical insurance fee and the cost fee of the department disease group in the attribution of the hospital department.

Optionally, the determining whether the cost of the department group in the hospital department attribution exceeds the cost of the department group based on the cost of the department consumed by the cases corresponding to the department group, includes:

and judging whether the hospitalization expense scores consumed by the cases corresponding to the department disease groups are the total hospitalization expense after the cases are completed.

If yes, comparing the cost total score with the hospitalization cost score consumed by the case corresponding to the department group, and judging the size of the cost total score and the hospitalization cost score.

And if the cost total score is greater than or equal to the hospitalization cost score, determining that the cost total score of the department group in the attribution of the hospital department has cost hyperbranched.

Compared with the prior art, the hospital data analysis device 400 based on the DIP provided by the embodiment of the application combines the history group of the DIP built in the group database and the real-time group corresponding to each target hospital based on the DIP, determines the updated target group of DIP in the group database and at least one corresponding target case under each target group of DIP, classifies hospitals to be detected according to the part attribution of the preset hospitals based on the updated target group of DIP in the group database, and determines the department group and department group values of the hospitals to be detected under the department attribution of different hospitals; for any department disease group to which any hospital department belongs in the hospital to be detected, whether the cost score of the department disease group in the hospital department belongs is hyperbranched or not is determined based on the department disease component value of the department disease group, the medical insurance cost and the cost of the department disease group in the hospital department belongs, so that disease type analysis of target hospital data under different hospital departments is completed, real-time group entering monitoring is realized, self-adaptive adjustment can be carried out according to the medical service mode of the hospital to be detected, and further the accuracy and efficiency of disease component value payment of the hospital to be detected are improved.

Further, fig. 5 is a second block diagram of a DIP-based hospital data analysis according to an embodiment of the present application. As shown in fig. 5, the DIP-based hospital data analysis apparatus 400 includes:

Optionally, according to standard history case data of the target area, a standard history disease catalog mapping table of the target area, a standard history operation catalog mapping table of the target area, a preset DIP grouper and a DIP medical insurance policy of the target area, a DIP group database is built.

Optionally, the standard historical case data of the target area, the standard historical disease catalog mapping table of the target area, and the standard historical surgical catalog mapping table of the target area are determined by:

acquiring initial historical case data of a target area, an initial historical disease catalog mapping table of the target area and an initial operation catalog mapping table of the target area.

The fourth determining module 450 is configured to determine service evaluation results of the hospital to be detected under different classifications according to different department disease groups, different diagnosis and treatment disease groups and different doctor disease groups.

The fifth determining module 460 is configured to determine a target important disease according to different department disease groups and department disease component values, different diagnosis and treatment disease groups and diagnosis and treatment disease component values, and different physician disease groups and physician disease component values, so as to implement important management on the target important disease.

Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown in fig. 6, the electronic device 600 includes a processor 610, a memory 620, and a bus 630.

The memory 620 stores machine-readable instructions executable by the processor 610, and when the electronic device 600 is running, the processor 610 communicates with the memory 620 through the bus 630, and when the machine-readable instructions are executed by the processor 610, the steps of the DIP-based hospital data method in the method embodiments shown in fig. 1 to 3 can be executed, and specific implementation can be referred to method embodiments, which are not repeated herein.

The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor may perform the steps of the DIP-based hospital data method in the method embodiment shown in fig. 1 to 3, and the specific implementation manner may refer to the method embodiment and will not be described herein.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. The DIP-based hospital data analysis method is characterized by comprising the following steps of:

inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device, and determining real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources;

classifying hospitals to be detected according to preset hospital department attributions based on the updated DIP entering the target disease group in a group database, and determining department disease groups and department disease component values of the hospitals to be detected under the attributions of different hospital departments;

2. The DIP-based hospital data analysis method according to claim 1, wherein the hospital department affiliation includes a department, the department group includes a department group, the hospitals to be detected are classified according to preset hospital department affiliation, and determining department group and department group values of the hospitals to be detected under different hospital departments includes:

3. The DIP-based hospital data analysis method according to claim 2, wherein the hospital department affiliation further comprises a diagnosis and treatment group, the department group further comprises a diagnosis and treatment group, and after the hospitals to be detected are classified according to departments, determining department group and department group component values of the hospitals to be detected under different departments, the DIP-based hospital data analysis method further comprises:

4. The DIP-based hospital data analysis method according to claim 3, wherein the hospital department affiliation further includes a doctor type, the department group further includes a doctor group, and after the department group for any department of the hospital to be detected, classifying the department group according to diagnosis and treatment group, determining different diagnosis and treatment group and diagnosis and treatment group values, the DIP-based hospital data analysis method further includes:

5. The DIP-based hospital data analysis method according to claim 4, wherein the determining whether there is a charge hyperbranched of the charge score of the department group in the hospital department home based on the department group value, medical insurance charge, and the cost charge of the department group in the hospital department home, of the department group, comprises:

determining a cost aggregate score of a department disease group according to the department disease group score of the department disease group, medical insurance cost and cost of the department disease group in the attribution of a hospital department;

6. The DIP-based hospital data analysis method according to claim 5, wherein the determining whether there is a charge hyperbranched in the charge aggregate score of the department group in the hospital department attribution based on the charge aggregate score consumed by the cases corresponding to the department group comprises:

Judging whether the hospitalization expense scores consumed by the cases corresponding to the department disease groups are the total hospitalization expense after the cases are completed; if yes, comparing the cost total score with the hospitalization cost score consumed by the case corresponding to the department group, and judging the size of the cost total score and the hospitalization cost score;

7. The DIP-based hospital data analysis method according to claim 1, wherein after any of the department groups to which any of the hospital departments belongs in the hospital to be detected, determining whether there is a charge hyperbranched of the charge scores of the department groups in the hospital department attribution based on the department group value, medical insurance charge, and cost charge of the department groups in the hospital department attribution, the DIP-based hospital data analysis method further comprises:

And determining service evaluation results of the hospital to be detected under different classifications according to the different department disease groups, the different diagnosis and treatment disease groups and the different doctor disease groups.

8. The DIP-based hospital data analysis method according to claim 7, wherein the standard historical case data of the target area, the standard historical disease catalog mapping table of the target area, and the standard historical surgical catalog mapping table of the target area are determined by:

9. The DIP-based hospital data analysis method according to claim 1, wherein after any of the department groups to which any of the hospital departments belongs in the hospital to be detected, determining whether there is a charge hyperbranched of the charge scores of the department groups in the hospital department attribution based on the department group value, medical insurance charge, and cost charge of the department groups in the hospital department attribution, the DIP-based hospital data analysis method further comprises:

And determining a target important disease species according to different department disease groups and department disease component values, different diagnosis and treatment disease groups and diagnosis and treatment disease component values and different doctor disease groups and doctor disease component values so as to realize the important management of the target important disease species.

10. A DIP-based hospital data analysis apparatus, the DIP-based hospital data analysis apparatus comprising:

the first determining module is used for inputting standard real-time case data of each target hospital, a standard disease catalog mapping table of the target hospital and a standard operation catalog mapping table of the target hospital into a preset DIP group entering device to determine real-time disease groups corresponding to all cases in all the target hospitals and real-time disease component values of each real-time disease group, wherein the real-time disease component values are used for representing the consumption degree of each disease species in the real-time disease groups on medical resources;

The second determining module is used for classifying hospitals to be detected according to preset hospital department attributions based on the updated DIP entering the target disease group in the group database, and determining department disease groups and department disease component values of the hospitals to be detected under the attributions of different hospital departments;