Rehabilitation bed daily payment grouping method and equipment based on big data

Abstract

The invention provides a rehabilitation bed day payment grouping method and equipment based on big data. The rehabilitation bed day payment grouping method comprises the following steps: creating a rehabilitation bed day knowledge base; and based on the rehabilitation bed day knowledge base, formulating a rehabilitation disease category payment grouping directory to group rehabilitation bed day payment of the patients. According to the method, the rehabilitation bed day knowledge base is established based on big data, and meanwhile, the rehabilitation disease category payment grouping catalog is automatically divided and calculated based on the rehabilitation bed day knowledge base, so that the standard cost of the rehabilitation disease category is formulated, and the scientificity and rationality of the rehabilitation bed day payment standard are ensured.

Description

Technical Field

[0001] The present invention relates to the field of medical big data technology, and in particular to a method and device for grouping daily payment of rehabilitation beds based on big data. Background Art

[0002] At present, the domestic payment for rehabilitation bed days mainly adopts the method of calculating the bed day standard according to the level of medical institutions. This method has the following problems: it does not distinguish between diseases, and all rehabilitation diseases are only classified and paid according to the third, second and first levels. The final classification payment results only correspond to the third, second and first level classifications, and cannot intuitively reflect the payment situation between different diseases. Summary of the invention

[0003] In order to overcome the above technical defects, the present invention provides a method and device for grouping daily payment of rehabilitation beds based on big data, which can group daily payment of rehabilitation beds according to the type of rehabilitation disease.

[0004] In order to solve the above problems, the present invention provides a method and device for daily payment grouping of rehabilitation beds based on big data, comprising: A method for grouping daily payment of rehabilitation beds based on big data, comprising the following steps: Create a knowledge base of rehabilitation bed days; Based on the rehabilitation bed-day knowledge base, a rehabilitation disease payment grouping catalog is developed to group patients' rehabilitation bed-day payments.

[0005] As a further improvement of the present invention, the step of creating a rehabilitation bed daily knowledge base includes: Create a clinical rehabilitation disease table; Create large clinical diagnosis and treatment data sets; The clinical rehabilitation disease table and clinical diagnosis and treatment big data set are measured to obtain the rehabilitation bed day knowledge base.

[0006] As a further improvement of the present invention, the step of creating a clinical rehabilitation disease table includes: The ICD-10 diagnostic codes of rehabilitation diseases are obtained and saved in the database to form a clinical rehabilitation disease table.

[0007] As a further improvement of the present invention, the step of creating a clinical diagnosis and treatment big data set includes: Obtain rehabilitation medical data; Clean and integrate rehabilitation medical data to obtain a large clinical diagnosis and treatment data set.

[0008] As a further improvement of the present invention, the step of calculating the clinical rehabilitation disease table and the clinical diagnosis and treatment big data set to obtain the rehabilitation bed day knowledge base includes: The rehabilitation medical data were statistically analyzed to obtain descriptive statistical analysis results and cost composition analysis results; A knowledge base of rehabilitation bed days is constructed based on the analysis results and cost structure analysis results.

[0009] As a further improvement of the present invention, the step of formulating a rehabilitation disease payment grouping catalog based on the rehabilitation bed day knowledge base includes: Determine the patient's rehabilitation disease group based on a large clinical diagnosis and treatment data set; Classification and grouping measurement; Based on the results of classification and grouping calculations, standard fees for rehabilitation diseases are formulated.

[0010] As a further improvement of the present invention, the step of determining the rehabilitation disease group based on the clinical diagnosis and treatment big data set includes: The main diagnoses in the medical insurance settlement list in the clinical diagnosis and treatment big data set are compared with the diagnostic criteria in the rehabilitation bed day knowledge base to determine the rehabilitation disease group to which the patient belongs.

[0011] As a further improvement of the present invention, the step of classification, grouping and calculation includes: Patients are divided into different classification groups according to their rehabilitation disease group and number of hospitalization days. The cost of each classification group is calculated to obtain the range of unified payment amounts for different classification groups.

[0012] As a further improvement of the present invention, the step of formulating the standard cost of rehabilitation diseases according to the classification and grouping calculation results includes: After eliminating unreasonable records in the unified payment range, the weighted average of the unified payment range is calculated to obtain the standard fee for each classification group.

[0013] The present invention also provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the rehabilitation bed daily payment grouping method.

[0014] The present invention has the following beneficial effects: the present invention builds a rehabilitation bed day knowledge base based on big data, and at the same time, automatically divides the rehabilitation disease payment grouping directory based on the rehabilitation bed day knowledge base, thereby grouping the patient's rehabilitation bed day payment. On this basis, the payment situation of different diseases can be statistically analyzed, providing an intuitive comparison of medical resource consumption between different diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the method for grouping daily payment for rehabilitation beds of the present invention; Figure 2 This is a flow chart of step S1 of the present invention; Figure 3 This is a flow chart of step S2 of the present invention. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0017] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0018] The present invention provides a rehabilitation bed day payment grouping method based on big data. Before implementing the rehabilitation bed day payment grouping method of the present invention, considering that no access condition constraints are set for rehabilitation bed day payment in the prior art, some unnecessary rehabilitation treatments are included in the medical insurance payment scope, which increases the expenditure pressure of the medical insurance fund and affects the safety and effective application of the medical insurance fund. It is necessary to formulate a rehabilitation bed day access condition review mechanism: 1. Qualification review of rehabilitation medical institutions: Relying on the system built by the rehabilitation bed-day payment grouping method, the "rehabilitation medical institution qualifications review" is carried out on the medical insurance settlement list of "payment by bed-day" initiated by the medical institution. The "medical institution practice license" table of the "hospital department qualification library" in the system's database is searched for "diagnosis and treatment subject" information, and the search results are checked to see whether there are keywords such as "mental rehabilitation major" or "rehabilitation medicine department". If so, it means that the medical institution has the rehabilitation diagnosis and treatment qualifications and proceeds to the next step; otherwise, it means that the medical institution does not have the rehabilitation diagnosis and treatment qualifications and exits the rehabilitation bed-day access conditions review, and the review fails.

[0019] 2. Review of staffing in rehabilitation medical institutions: The system conducts a "review of staffing in rehabilitation medical institutions" on the medical insurance settlement list of "payment by bed day" initiated by the medical institution. The system automatically calls the "medical personnel information query" interface of the current provincial medical insurance information platform, and queries the number of physicians, rehabilitation treatment rooms, and nurses in the hospital to be supervised, and compares them with the "rehabilitation medical institution level staffing table" of this system to determine whether the hospital's staffing meets the corresponding level requirements. If it meets the level requirements, it means that the medical institution's staffing is compliant and proceeds to the next step; otherwise, it means that the medical institution's staffing is compliant and exits the rehabilitation bed day access conditions review, and the review fails.

[0020] 3. Patient rehabilitation assessment: The system conducts "patient rehabilitation assessment" on the patients in the medical insurance settlement list of "payment by bed day" initiated by the medical institution. First, the system automatically determines whether the medical institution has uploaded "admission information" and "discharge summary", and at the same time determines whether there is an "assessment report". If so, it will proceed to the next step; otherwise, it will exit the rehabilitation bed day access condition review and the review will fail. Secondly, the system will use natural language recognition technology to identify the "scoring results" and "rehabilitation value". If both the "scoring results" and "rehabilitation value" meet the requirements, it will proceed to the next step; otherwise, it will exit the rehabilitation bed day access condition review and the review will fail. Finally, the system automatically determines the "discharge summary" uploaded by the medical institution, and uses natural language recognition technology to identify "rehabilitation effectiveness". If there are keywords such as "relief", "rehabilitation", and "recovery", it is determined that the patient meets the rehabilitation bed day payment access conditions and the review is successful; otherwise, the review fails.

[0021] Through the above steps, the access conditions for rehabilitation bed days are determined, including rehabilitation medical institutions, practicing physicians and rehabilitation access standards, which effectively constrains the implementation of rehabilitation bed days. This advantage is achieved by adopting a scientific access condition review mechanism, ensuring that only qualified patients can enjoy rehabilitation bed day services, thereby improving the security and effective application of medical insurance funds.

[0022] like Figure 1 As shown, the rehabilitation bed daily payment grouping method of the present invention comprises the steps of: S1. Create a knowledge base of rehabilitation bed days, such as Figure 2 As shown, step S1 includes the following steps: S11. Create a clinical rehabilitation disease table, obtain ICD-10 diagnostic codes related to rehabilitation diseases guided and summarized by clinical experts, save them in the database, and form a "clinical rehabilitation disease table", which specifically includes neurological diseases, mental and behavioral disorders, cerebrovascular diseases, functional intestinal diseases, musculoskeletal system and other rehabilitation diseases. As the basis for subsequent measurements, in the specific implementation process, the diagnostic codes can be entered manually or by file import.

[0023] S12. Create a large clinical diagnosis and treatment data set; Obtain rehabilitation medical data; rehabilitation medical data can be imported through csv file import. Rehabilitation medical data includes basic patient information (such as age, gender, medical history, etc.), medical insurance settlement list information, diagnosis information (such as diagnosis code, diagnosis name, etc.), treatment information (such as treatment date, project code, project name, etc.) and cost information (such as cost amount, pooling amount, quantity, etc.).

[0024] Clean and integrate the rehabilitation medical data, check and correct errors or inconsistencies in the data, and use data cleaning tools for automated processing, such as deleting duplicate records, filling missing values, converting data types, etc. Next, integrate these data into the clinical diagnosis and treatment big data set to ensure data consistency and integrity, and finally obtain the clinical diagnosis and treatment big data set.

[0025] S13. Calculate the clinical rehabilitation disease table and clinical diagnosis and treatment big data set to obtain the rehabilitation bed day knowledge base.

[0026] Specifically, step S13 includes the following steps: The rehabilitation medical data were statistically analyzed to obtain the descriptive statistical analysis results and the cost composition analysis results: First, the statistics of the basic information of different rehabilitation diseases were calculated, including the average age, gender ratio, etc. Then, the distribution of different types of diseases was calculated, and an overview of the treatment information was statistically analyzed, including the average length of stay, the average amount of overall planning, the value of admission examination results, the value of discharge examination results, the value of admission test results, and the value of discharge test results. Finally, the composition of the costs of different rehabilitation diseases was calculated, including the proportion of drug costs, the proportion of treatment costs, the proportion of examination costs, the proportion of medical consumables costs, etc. At the same time, a list of costs for inspections, tests, drugs, treatments, etc. related to different rehabilitation diseases was processed, and records with more than 50 samples and a usage rate of more than 40% were extracted to form a "rehabilitation disease diagnosis and treatment path."

[0027] According to the above analysis results, 15 types of rehabilitation disease groups and related diagnostic standards, treatment plans, rehabilitation goals, rehabilitation values ​​and other information were automatically constructed to form a rehabilitation bed-day knowledge base. Among them, "diagnostic standards" refer to the values ​​defined in the clinical rehabilitation disease table, such as "F20.301 / atypical schizophrenia"; "rehabilitation goals and rehabilitation values" are the preliminary basis calculated by the system through the patient's "admission examination result value, discharge examination result value, admission test result value and discharge test result value", and a comprehensive evaluation is made based on the "rehabilitation effectiveness" identification in the "discharge summary" text (see the "Formulation of the rehabilitation bed day access conditions review mechanism" section for details); "treatment plan" is constructed based on the "rehabilitation disease diagnosis and treatment pathway", and different examinations, tests, drugs and treatments are used for different diseases.

[0028] For example, the rehabilitation bed day knowledge base is illustrated as follows: "F20.x / Schizophrenia" rehabilitation disease group, the relevant "diagnostic criteria" include "F20.900 / Schizophrenia", "F20.301 / Atypical Schizophrenia", "F20.000 / Paranoid Schizophrenia", "F20.100 / Adolescent Schizophrenia" or "F20.200 / Catonic Schizophrenia", etc. Its "treatment plan" is "antipsychotic drug treatment is mainly single drug treatment", and related drugs include "chlorpromazine", "perphenazine", "fluphenazine", "trifluoperazine", "haloperidine" and "pentafluidol", etc.; its average hospital stay in cities and prefectures is 87.55 days; its examination items include "neuroimaging examination", "electroencephalogram (EEG) examination", "laboratory examination" and "psychological assessment", etc.

[0029] As shown in Table 1, fragment information of a rehabilitation bed day knowledge base.

[0030] Table 1: Rehabilitation bed day knowledge base (fragment)

[0031] S2. Based on the rehabilitation bed-day knowledge base, formulate a rehabilitation disease payment grouping directory to group patients' rehabilitation bed-day payments, such as Figure 3 As shown: S21. Determine the patient's rehabilitation disease group based on the clinical diagnosis and treatment big data set; specifically, compare the main diagnoses in the medical insurance settlement list in the clinical diagnosis and treatment big data set with the diagnostic standards in the rehabilitation bed day knowledge base to determine the rehabilitation disease group to which the patient belongs.

[0032] For example, the main diagnosis code in the medical insurance settlement list of patient A is "F20.400 / Post-depression due to schizophrenia". The diagnosis code "F20.400" is used as the query keyword to search in the "Rehabilitation Bed Day Knowledge Base". The search results successfully obtain relevant information of "F20.400 / Post-depression due to schizophrenia", including the average hospitalization days corresponding to the diagnosis, the scope of unified payment, treatment plan, rehabilitation goals and rehabilitation value.

[0033] S22. Calculation by classification and grouping: Patients are divided into different classification groups according to their rehabilitation disease group and number of hospitalization days. The cost of each classification group is calculated to obtain the range of unified payment amounts for different classification groups.

[0034] S23. According to the results of classification and grouping, formulate standard fees for rehabilitation diseases. After eliminating unreasonable records in the scope of unified payment, perform weighted average calculation on the scope of unified payment to obtain the standard fee for each classification group. This will serve as the basis for the medical insurance department to pay for rehabilitation bed-days.

[0035] As shown in Table 2, a fragment of the standard cost of a rehabilitation bed day: Table 2: Standard cost of a rehabilitation bed per day (fragment)

[0036] After obtaining the standard fee for each classification group through the above rehabilitation bed daily payment grouping method, the grouping catalog can be further optimized, such as: 1) Expert argumentation optimization: The system built based on the rehabilitation bed day payment grouping method provides a page for human-computer interaction. The argumentation results of rehabilitation medicine experts, clinical diagnosis and treatment experts, and medical insurance management experts on the rehabilitation bed day payment grouping method are maintained through the page to make appropriate adjustments to the "Rehabilitation Bed Day Knowledge Base" and "Standard Fees for Rehabilitation Diseases" to ensure the scientificity and rationality of the rehabilitation disease payment grouping catalog.

[0037] 2) Optimization of medical institution consultation: The system will collect the feedback results of medical institutions at all levels on the rehabilitation bed day payment grouping method, and after review by relevant experts, make appropriate adjustments to the "Rehabilitation Bed Day Knowledge Base" and "Standard Fees for Rehabilitation Diseases" to ensure the rationality and applicability of the rehabilitation disease payment grouping catalog.

[0038] The above steps, based on the rehabilitation bed daily payment grouping method of the present invention, ensure the scientificity and rationality of the rehabilitation bed daily payment grouping method by organizing expert discussions and soliciting opinions from medical institutions, and improve the scientificity and rationality of the rehabilitation bed daily payment grouping method.

[0039] In summary, the present invention uses clinical rehabilitation disease tables and clinical diagnosis and treatment big data sets for calculation, formulates a rehabilitation bed day knowledge base, and formulates a rehabilitation disease payment grouping directory to provide conditions for the refined management of rehabilitation bed days. After the present invention realizes its function by using big data analysis, it is combined with expert demonstration to ensure the scientificity and rationality of the rehabilitation bed day payment standard, while also improving the quality and efficiency of rehabilitation medical services.

[0040] The present invention also provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the aforementioned monitoring method.

[0041] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0042] The memory can be used to store the computer program or module, and the processor implements various functions of the charging pile identification method by running or executing the computer program or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0043] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for grouping daily payment for rehabilitation beds based on big data, characterized in that: Includes steps: Create a knowledge base of rehabilitation bed days; Based on the rehabilitation bed-day knowledge base, a rehabilitation disease payment grouping catalog is developed to group patients' rehabilitation bed-day payments.

2. The method for grouping daily payment for rehabilitation beds according to claim 1, characterized in that: The steps of creating a rehabilitation bed daily knowledge base include: Create a clinical rehabilitation disease table; Create large clinical diagnosis and treatment data sets; The clinical rehabilitation disease table and clinical diagnosis and treatment big data set are measured to obtain the rehabilitation bed day knowledge base.

3. The method for grouping daily payment for rehabilitation beds according to claim 2, characterized in that: The step of creating a clinical rehabilitation disease table includes: The ICD-10 diagnostic codes of rehabilitation diseases are obtained and saved in the database to form a clinical rehabilitation disease table.

4. The method for grouping daily payment for rehabilitation beds according to claim 2, characterized in that: The step of creating a clinical diagnosis and treatment big data set includes: Obtain rehabilitation medical data; Clean and integrate rehabilitation medical data to obtain a large clinical diagnosis and treatment data set.

5. The method for grouping daily payment for rehabilitation beds according to claim 4, characterized in that: The step of calculating the clinical rehabilitation disease table and the clinical diagnosis and treatment big data set to obtain the rehabilitation bed day knowledge base includes: The rehabilitation medical data were statistically analyzed to obtain descriptive statistical analysis results and cost composition analysis results; A knowledge base of rehabilitation bed days is constructed based on the analysis results and cost structure analysis results.

6. The method for grouping daily payment for rehabilitation beds according to claim 2, characterized in that: The steps of formulating a rehabilitation disease payment grouping catalog based on the rehabilitation bed-day knowledge base include: Determine the patient's rehabilitation disease group based on a large clinical diagnosis and treatment data set; Classification and grouping measurement; Based on the results of classification and grouping calculations, standard fees for rehabilitation diseases are formulated.

7. The method for grouping daily payment for rehabilitation beds according to claim 6, characterized in that: The steps of determining the recovery disease group based on the clinical diagnosis and treatment big data set include: The main diagnoses in the medical insurance settlement list in the clinical diagnosis and treatment big data set are compared with the diagnostic criteria in the rehabilitation bed day knowledge base to determine the rehabilitation disease group to which the patient belongs.

8. The method for grouping daily payment for rehabilitation beds according to claim 6, characterized in that: The steps of classification, grouping and calculation include: Patients are divided into different classification groups according to their rehabilitation disease group and number of hospitalization days. The cost of each classification group is calculated to obtain the range of unified payment amounts for different classification groups.

9. The method for grouping daily payment for rehabilitation beds according to claim 8, characterized in that: According to the results of classification and grouping, the steps to formulate standard fees for rehabilitation diseases include: After eliminating unreasonable records in the unified payment range, the weighted average of the unified payment range is calculated to obtain the standard fee for each classification group.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the rehabilitation bed daily payment grouping method as described in any one of claims 1 to 9.

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