DRG-based profit and loss analysis method and system

Abstract

The invention relates to the technical field of intelligent management, and particularly discloses a DRG-based profit and loss analysis method and system, and the method comprises the steps: S1, obtaining the case treatment information of a hospital, carrying out the grouping of the case treatment information through a disease diagnosis related group DRG, obtaining the DRG group information corresponding to each case, and enabling the case treatment information to comprise the treatment cost information; s2, determining profit and loss information of each case based on the treatment cost information of each case and a DRG preset payment standard of a case diagnosis type corresponding to the case; s3, based on the DRG group information and the profit and loss information corresponding to each case, obtaining a profit and loss analysis result of each case, and integrating the profit and loss analysis result with the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital to obtain a comprehensive profit and loss analysis result of the hospital; the method is used for efficiently carrying out profit and loss analysis on different cases in hospitals.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent management, and particularly relates to a DRG-based profit and loss analysis method and system. BACKGROUND

[0002] At present, with the continuous deepening of the layout of national public medical treatment and medical treatment reform, payment system reform is a system engineering, which is a manifestation of the major change of medical insurance management concept and the role of medical insurance agencies. Implementing payment according to disease types has become a problem that needs to be solved in comprehensive medical reform.

[0003] Payment according to disease types means that first, through a unified and standardized disease diagnosis classification system, various diseases are accurately classified, and on this basis, a standard payment standard corresponding to each disease is scientifically and reasonably formulated. The social security agency pays the corresponding hospitalization expenses to the designated medical institutions according to the established standard combined with the actual number of hospitalizations. Through this way, the utilization of medical resources can be standardized, and the consumption of medical resources is ensured to be proportional to the number of hospitalized patients, the complexity of the disease and the service intensity. From the actual effect, payment according to disease types is of great significance.

[0004] However, in the prior art, the flexibility of payment according to disease types is not high, and there are limitations in accounting for the medical cost of each DRG group, identifying the key points of cost control, and optimizing the allocation of resources.

[0005] Therefore, the present application provides a more comprehensive, detailed and accurate DRG-based profit and loss analysis method and system. SUMMARY

[0006] The present application provides a DRG-based profit and loss analysis method and system, which acquires hospital case treatment information, accurately groups the case treatment information using DRG grouping technology, and determines the profit and loss of each case based on the treatment cost information of the grouped case and the DRG preset payment standard. The profit and loss of each case can be more accurately mastered, and the comprehensive profit and loss analysis result of the hospital can be determined by combining the corresponding profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital. The comprehensive, detailed and accurate profit and loss analysis result of the hospital can provide more effective support for hospital management.

[0007] The present application provides a DRG-based profit and loss analysis method, comprising:

[0008] S1, acquiring hospital case treatment information, and grouping the case treatment information using disease diagnosis related grouping DRG to obtain DRG group information corresponding to each case, wherein the case treatment information includes treatment cost information;

[0009] S2, determining profit and loss information of each case based on treatment cost information of each case and DRG preset payment standard of case diagnosis type corresponding to the case;

[0010] S3, obtaining profit and loss analysis result of each case based on DRG group information corresponding to each case and profit and loss information, and integrating the profit and loss analysis result with profit and loss analysis result of comprehensive profit and loss analysis type of the hospital to obtain comprehensive profit and loss analysis result of the hospital.

[0011] As an implementable manner, S1, obtaining case treatment information of the hospital, and grouping the case treatment information by using disease diagnosis related grouping DRG to obtain DRG group information corresponding to each case, including:

[0012] Obtaining initial case treatment information of the hospital, and extracting key information from the initial case treatment information according to diagnosis requirements of the DRG to obtain the case treatment information, the case treatment information including clinical diagnosis information, treatment cost information and treatment resource consumption information;

[0013] Judging information consistency of information corresponding in the clinical diagnosis information and the treatment resource consumption information, if the information consistency is higher than preset information consistency, judging that the case treatment information is valid case treatment information;

[0014] Converting the valid case treatment information by information coding, and grouping the case treatment information after coding conversion by using the disease diagnosis related grouping DRG;

[0015] Obtaining case treatment set of the hospital according to the grouping result, wherein each subset in the case treatment set corresponds to a case diagnosis type;

[0016] Extracting corresponding DRG group information based on each case diagnosis type, wherein the DRG group information includes DRG group coding and weight information.

[0017] As an implementable manner, extracting corresponding DRG group information based on each case diagnosis type, including:

[0018] Extracting DRG group coding corresponding to each case diagnosis type from a preset DRG database;

[0019] And, obtaining cases of each case diagnosis type of the hospital within a preset time period, and obtaining ratio of the cases of each case diagnosis type to total cases of the hospital as weight information corresponding to each case diagnosis type.

[0020] As an implementable manner, S2, determining profit and loss information of each case based on treatment cost information of each case and DRG preset payment standard of case diagnosis type corresponding to the case, including:

[0021] Obtaining standard payment information of a DRG corresponding to each case diagnosis type, hospital level and regional information corresponding to the hospital;

[0022] Determining a first level coefficient of the standard payment information of the DRG based on the hospital level, and determining a second level coefficient of the standard payment information of the DRG based on the regional information corresponding to the hospital;

[0023] Optimizing the standard payment information of the DRG of the corresponding case diagnosis type according to the first level coefficient and the second level coefficient, to obtain a preset payment standard of the DRG corresponding to the case diagnosis type of the current case;

[0024] Determining the profit and loss information of the current case according to the preset payment standard of the DRG corresponding to the current case.

[0025] As an implementable manner, determining the profit and loss information of the current case based on the preset payment standard of the DRG corresponding to the current case, comprises:

[0026] Judging the profit and loss type of the current case based on the treatment cost information of the current case and the preset payment standard of the DRG corresponding to the case diagnosis type of the current case;

[0027] Combining the weight information and the profit and loss type in the DRG group information corresponding to the current case to obtain the profit and loss information of the current case.

[0028] As an implementable manner, S3, based on the DRG group information and the profit and loss information corresponding to each case, obtaining the profit and loss analysis result of each case, and combining the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital to obtain the comprehensive profit and loss analysis result of the hospital, comprising:

[0029] Aggregating the DRG group information and the profit and loss information of each case based on each information aggregation manner in the preset information aggregation set to obtain the profit and loss analysis result corresponding to each information aggregation manner, and the information aggregation manner comprises time sequence aggregation and MDC aggregation;

[0030] Obtaining the first profit and loss analysis result of the hospital according to the profit and loss analysis result corresponding to each information aggregation manner in the preset information aggregation set;

[0031] Obtaining the comprehensive profit and loss type of the hospital, and obtaining the profit and loss analysis result of each comprehensive profit and loss type of the hospital based on the DRG group information and the profit and loss information corresponding to each case of the hospital, to obtain the second profit and loss analysis result of the hospital;

[0032] Obtaining the comprehensive profit and loss analysis result of the hospital according to the first profit and loss analysis result and the second profit and loss analysis result of the hospital.

[0033] As an implementable manner, the first profit and loss analysis result of the hospital is obtained according to a profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set, and specifically includes:

[0034] A case complexity coefficient of each case diagnosis type is obtained according to a diagnosis complexity of each case diagnosis type in the hospital;

[0035] Personnel attribute information and clinical diagnosis information in each case of each case diagnosis type are obtained, and a case diagnosis influence coefficient of each case in the case diagnosis type is determined based on a matching result of the personnel attribute information and the clinical diagnosis information;

[0036] A mean value of the diagnosis influence coefficient of each case in the case diagnosis type is taken as a diagnosis influence coefficient of the current case diagnosis type;

[0037] A profit and loss analysis result of each case diagnosis type is obtained according to the time sequence aggregation mode, as first profit and loss analysis information of the current case diagnosis type;

[0038] The first profit and loss analysis information of the corresponding case diagnosis type is optimized based on the case complexity coefficient and the diagnosis influence coefficient of each case diagnosis type, to obtain first optimized profit and loss analysis information of the case diagnosis type;

[0039] A first profit and loss analysis information set is obtained based on the first optimized profit and loss analysis information of each case diagnosis type;

[0040] A profit and loss analysis result of each case diagnosis type of the same disease is obtained based on the MDC aggregation mode, as second profit and loss analysis information of the current disease;

[0041] A disease complexity coefficient of the current disease is obtained based on a case complexity coefficient of each case diagnosis type of the same disease;

[0042] Meanwhile, a disease diagnosis influence coefficient of the current disease is obtained based on a diagnosis influence coefficient of each case diagnosis type of the same disease;

[0043] The second profit and loss analysis information of the corresponding disease is optimized based on the disease complexity coefficient and the disease diagnosis influence coefficient of each disease, to obtain second optimized profit and loss analysis information of each disease;

[0044] A second profit and loss analysis information set is obtained based on the second optimized profit and loss analysis information of each disease;

[0045] The first profit and loss analysis result of the hospital is obtained according to the first profit and loss analysis information set and the second profit and loss analysis information set.

[0046] As an implementable manner, the DRG-based profit and loss analysis method further includes: performing case diagnosis optimization according to the comprehensive profit and loss analysis result, specifically including:

[0047] extracting a case diagnosis type corresponding to a case of loss in the comprehensive profit and loss analysis result, to obtain a case diagnosis type set;

[0048] obtaining weight information corresponding to each case diagnosis type in the case diagnosis type set;

[0049] comparing the weight information with a preset weight threshold, so as to determine different diagnosis optimization schemes based on different comparison results, to perform diagnosis optimization on each case diagnosis type.

[0050] The application provides a DRG-based profit and loss analysis system, characterized in that it comprises:

[0051] a data acquisition module, configured to acquire case treatment information of a hospital, and group the case treatment information by using disease diagnosis related grouping (DRG), to obtain DRG group information corresponding to each case, wherein the case treatment information comprises treatment cost information;

[0052] an information determination module, configured to determine profit and loss information of each case based on the treatment cost information of each case and DRG preset payment standards of a case diagnosis type corresponding to the case;

[0053] a profit and loss analysis module, configured to obtain profit and loss analysis results of each case based on the DRG group information corresponding to each case and the profit and loss information, and combine the profit and loss analysis results with profit and loss analysis results of a comprehensive profit and loss analysis type of the hospital, to obtain comprehensive profit and loss analysis results of the hospital.

[0054] The application has the beneficial effects that, by acquiring case treatment information of a hospital, and precisely grouping the case treatment information by using DRG grouping technology, and determining the profit and loss of each case based on the treatment cost information of each case after grouping and DRG preset payment standards, the income and expenditure of each case can be more accurately mastered, and the comprehensive profit and loss analysis results of the hospital can be determined by combining the profit and loss analysis results corresponding to the comprehensive profit and loss analysis type of the hospital, so that the profit and loss analysis results of the hospital can be more comprehensively, meticulously and accurately presented, and more effective support can be provided for hospital management.

[0055] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by means of the instrumentalities particularly pointed out in the specification.

[0056] The technical solutions of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the application, and do not limit the application. In the drawings:

[0058] Figure 1 A flow chart of a DRG-based profit and loss analysis method in an embodiment of the application;

[0059] Figure 2 A structural diagram of a DRG-based profit and loss analysis system in an embodiment of the application. DETAILED DESCRIPTION

[0060] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described here are only used to explain and illustrate the application, and do not limit the application.

[0061] Embodiment 1:

[0062] The application provides a DRG-based profit and loss analysis method, referring to Figure 1 , comprising:

[0063] S1, obtaining case treatment information of a hospital, and grouping the case treatment information by using disease diagnosis related grouping DRG to obtain DRG group information corresponding to each case, wherein the case treatment information comprises treatment cost information;

[0064] S2, determining profit and loss information of each case based on the treatment cost information of each case and a DRG preset payment standard of a case diagnosis type corresponding to the case;

[0065] S3, obtaining a profit and loss analysis result of each case based on the DRG group information corresponding to each case and the profit and loss information, and integrating the profit and loss analysis result with a comprehensive profit and loss analysis result of the hospital to obtain a comprehensive profit and loss analysis result of the hospital.

[0066] In this embodiment, the case treatment information refers to various information recorded by the hospital in the process of patient treatment, including clinical diagnosis information, treatment cost information, treatment resource consumption information, treatment scheme, treatment duration, etc., which can comprehensively reflect the specific situation of patient treatment.

[0067] In this embodiment, DRG is a diagnosis grouping payment method for grouping similar medical cases into several disease groups, determining similar medical expenses for patients in the same group, and paying medical institutions by medical insurance departments, wherein the grouping method is related to factors such as age, diagnosis information, comorbidities and complications, and treatment methods of patients.

[0068] In this embodiment, the DRG group information includes, but is not limited to, group code, name, feature, weight information, etc. For example, a certain DRG group code is "AB123", and the name is "lower limb fracture internal fixation".

[0069] In this embodiment, the DRG preset payment standard is a payment standard preset by the medical insurance department or other payment channels to the hospital in advance, and the determination of the DRG preset payment standard is related to the average cost of disease treatment, the consumption of medical resources, the level and regional information of the hospital, etc. For example, the preset payment standard of the DRG group corresponding to the A type case diagnosis type is 5000 yuan.

[0070] In this embodiment, the profit and loss information is determined by comparing the actual treatment cost information of each case with the corresponding DRG preset payment standard to determine the profit or loss of the case in the treatment process. If the treatment cost information is lower than the DRG preset payment standard, it is a profit, and if the treatment cost information is higher than the DRG preset payment standard, it is a loss. For example, the treatment cost information of B case is 8000 yuan, and the preset payment standard is 10000 yuan, so B case makes a profit of 2000 yuan.

[0071] In this embodiment, the profit and loss analysis result is the type and amount of profit and loss of each case diagnosis type after sorting the profit and loss information of each case diagnosis type.

[0072] In this embodiment, the comprehensive profit and loss analysis result is the profit or loss of the hospital in the preset time period after summarizing the profit and loss analysis results of all case diagnosis types corresponding to all cases. It can reflect the operating efficiency of the hospital. For example, the comprehensive profit and loss analysis result of the hospital in a quarter is a loss of 5%. The preset time period can be a month, a quarter, half a year, a year, or even the entire life cycle of the hospital.

[0073] The beneficial effects of the above technology are: by obtaining the case treatment information of the hospital, using the DRG grouping technology to accurately group the case treatment information, and determining the profit and loss of each case by the treatment cost information and the DRG preset payment standard of each grouped case, the income and expenditure of each case can be more accurately mastered. Combined with the comprehensive profit and loss analysis result of the hospital corresponding to the comprehensive profit and loss analysis type, the comprehensive profit and loss analysis result of the hospital can be determined, and the profit and loss analysis result of the hospital can be more comprehensively, meticulously and accurately presented, providing more effective support for hospital management.

[0074] Embodiment 2:

[0075] On the basis of embodiment 1, a DRG-based profit and loss analysis method S1 acquires hospital case treatment information and groups the case treatment information using disease diagnosis related groups (DRGs) to obtain DRG group information corresponding to each case, including:

[0076] Acquire initial case treatment information of a hospital, and extract key information from the initial case treatment information according to the diagnosis requirements of DRGs to obtain case treatment information, which includes clinical diagnosis information, treatment cost information, and treatment resource consumption information.

[0077] Determine the information consistency of the information corresponding to the clinical diagnosis information and the treatment resource consumption information. If the information consistency is higher than the preset information consistency, the case treatment information is determined to be valid case treatment information.

[0078] Transform the valid case treatment information into information coding, and group the coded case treatment information based on DRGs.

[0079] Obtain a case treatment set of the hospital according to the grouping result, wherein each subset of the case treatment set corresponds to a case diagnosis type.

[0080] Extract the corresponding DRG group information based on each case diagnosis type, wherein the DRG group information includes DRG group coding and weight information.

[0081] In this embodiment, the initial case treatment information refers to all treatment information related to a patient during the treatment process of the patient, for example, the initial case treatment information can include personnel basic information, symptom description, various examination results, doctor's clinical diagnosis information, treatment process drug name, treatment resource consumption information, nursing condition, recovery condition, daily examination information, etc.

[0082] In this embodiment, the diagnosis requirements of DRGs refer to specific requirements proposed by disease diagnosis related groups (DRGs) for case information in order to achieve reasonable grouping and resource management. The diagnosis requirements of DRGs are to ensure that the grouping can accurately reflect the disease severity, treatment complexity, and resource consumption of patients, etc.

[0083] In this embodiment, the case treatment information is a set of key information extracted from the initial case treatment information according to the diagnosis requirements of the DRG, such as the doctor's clinical diagnosis information in the initial case treatment information, the name of the drug used in the treatment process, the treatment resource consumption information, etc. For example, for a patient with heart disease, the extracted case treatment information may be that the clinical diagnosis information is "coronary heart disease, unstable angina, combined with hypertension 2"; the treatment cost information includes drug cost 5000 yuan, examination cost 2000 yuan, operation cost 30000 yuan, etc.; the treatment resource consumption information is 15 days of hospitalization, 3 units of blood products are used, and 1 coronary angiography is performed.

[0084] In this embodiment, the clinical diagnosis information is the professional judgment and diagnosis conclusion made by the doctor according to the patient's symptoms, signs, examination results, etc.

[0085] In this embodiment, the treatment resource consumption information refers to the record of medical resources consumed by the patient during the treatment process, including manpower, material resources and time, etc. For example, the treatment resource consumption information can include the number of days of hospitalization (such as 10 days), the number and types of drugs used (such as 5 injections of antibiotics, 30 tablets of oral antihypertensive drugs), the examination items and times performed (such as 1 chest X-ray examination, 3 blood routine examinations), the surgical instruments and materials used (such as 1 heart stent, a certain amount of suture line), etc.

[0086] In this embodiment, the information consistency refers to the consistency between the clinical diagnosis information and the treatment resource consumption information. If the clinical diagnosis information indicates that the patient's condition is severe, then the treatment resource consumption information should also be relatively high; on the contrary, if the clinical diagnosis information diagnoses a lighter condition, the treatment resource consumption information should also be relatively low. For example, the clinical diagnosis is "mild pneumonia", but the treatment resource consumption shows 15 days of hospitalization, a large amount of expensive imported antibiotics are used, and a large number of unnecessary examinations are performed, so the information consistency is low.

[0087] In this embodiment, the preset information consistency is a threshold standard for judging whether the clinical diagnosis information and the treatment resource consumption information are consistent, which is preset according to the profit and loss analysis accuracy of the hospital. When the information consistency of the treatment resource consumption information and the clinical diagnosis information is higher than this preset value, it is considered that the case treatment information is reasonable and reliable, and can be used as effective case treatment information. For example, the hospital sets the preset information consistency to be 80%, that is, if the matching degree of the corresponding contents in the clinical diagnosis information and the treatment resource consumption information reaches 80% or above, it is judged that the case treatment information is effective.

[0088] In this embodiment, the effective case treatment information refers to case treatment information whose consistency between clinical diagnosis information and treatment resource consumption information is higher than the preset information consistency after information consistency judgment, and the information quality of the effective case treatment information is higher.

[0089] In this embodiment, the information coding conversion is to convert various contents in the effective case treatment information, such as clinical diagnosis and treatment method, according to the coding rules specified by the DRG system, so as to make it into a standard coding form that can be recognized and processed by a computer, for example, converting the "type 2 diabetes" diagnosis in the patient medical record into "E11.901".

[0090] In this embodiment, the case treatment set is a set formed by collecting cases with the same case diagnosis type together according to the DRG grouping result. Each sub-set corresponds to a specific case diagnosis type, which facilitates centralized analysis of cases of the same type.

[0091] In this embodiment, the DRG group information includes but is not limited to group coding, name, characteristics, weight information, etc. For example, a certain DRG group coding is "AB123", and the name is "lower limb fracture internal fixation".

[0092] The beneficial effects of the above technology are: through information extraction and consistency judgment on the initial case treatment information, the data quality of the case treatment information can be effectively improved, so that the case treatment information is grouped according to the DRG, which is helpful to more clearly and efficiently analyze each case diagnosis type and improve the profit and loss analysis efficiency.

[0093] Embodiment 3:

[0094] Based on embodiment 2, a DRG-based profit and loss analysis method based on each case diagnosis type extracts corresponding DRG group information, including:

[0095] extracting the DRG group coding corresponding to each case diagnosis type from the preset DRG database;

[0096] and obtaining the cases of each case diagnosis type in the hospital within the preset time period, and obtaining the ratio of the cases of each case diagnosis type to the total cases of the hospital as the weight information corresponding to each case diagnosis type.

[0097] In this embodiment, the preset DRG database is a database that is constructed in advance and stores disease diagnosis related grouping (DRG) related information. It contains various case diagnosis types and corresponding DRG group coding, etc.

[0098] In this embodiment, the DRG group code is a unique code assigned to each specific DRG group in the disease diagnosis-related grouping system. Through this coding, different DRG groups can be quickly and accurately identified and distinguished, such as for "type 2 diabetes with complications", the corresponding DRG group code is "EB005".

[0099] In this embodiment, the case diagnosis type refers to the classification of diseases according to the patient's disease characteristics, symptoms, test results, and other factors. Different case diagnosis types reflect different disease conditions and treatment needs, and are the basis for DRG grouping.

[0100] In this embodiment, the weight information is an index for measuring the relative importance of each case diagnosis type in the hospital cases. By calculating the ratio of the number of cases of a certain case diagnosis type to the total number of hospital cases, the proportion of that type of case in the hospital can be understood, such as in a certain hospital in June 2025, the total number of cases in the hospital is 500. Among them, the number of cases of case diagnosis type "acute bronchitis" is 50, and the weight information corresponding to the case diagnosis type "acute bronchitis" is 50÷500=0.1, i.e. 10%.

[0101] The beneficial effects of the above technology are: by obtaining the DRG group code and weight information corresponding to each case diagnosis type, the distribution weight of each case diagnosis type can be more clearly and intuitively understood, providing more effective support for hospital resource management.

[0102] Embodiment 4:

[0103] Based on embodiment 1, a DRG-based profit and loss analysis method S2 determines the profit and loss information of each case based on the treatment cost information of each case and the DRG preset payment standard of the case diagnosis type corresponding to the case, including:

[0104] Obtain the standard payment information of the DRG corresponding to each case diagnosis type, the hospital level and the regional information corresponding to the hospital;

[0105] Determine the first level coefficient of the standard payment information of the DRG based on the hospital level, and determine the second level coefficient of the standard payment information of the DRG based on the regional information corresponding to the hospital;

[0106] Optimize the standard payment information of the DRG corresponding to the corresponding case diagnosis type according to the first level coefficient and the second level coefficient, to obtain the DRG preset payment standard corresponding to the case diagnosis type of the current case;

[0107] Determine the profit and loss information of the current case according to the DRG preset payment standard corresponding to the current case.

[0108] In this embodiment, the standard payment information of DRG is a pre-set payment amount standard for each specific DRG group. The standard payment information of DRG comprehensively considers the average treatment cost of each case diagnosis type, disease severity, treatment complexity, required medical resources (such as drugs, examinations, operations, etc.), and expected treatment effect, and other factors.

[0109] In this embodiment, the hospital level is a level divided according to the scale of the hospital, the level of medical technology, the equipment condition, the management level, the quality of medical service, and the scientific research ability, and other factors. Common hospital levels are divided into first level, second level, and third level. The third level hospital is a large-scale comprehensive hospital or a specialized hospital, which has a high level of medical technology and advanced equipment, such as a provincial hospital or a well-known large specialized hospital, which can perform complex operations and diagnose and treat difficult diseases.

[0110] In this embodiment, the regional information refers to the specific geographical area where the hospital is located, including province, city, urban area, and other different levels of information. The economic development level, population structure, medical resource distribution, price level, and medical insurance policy of different regions are different, which will affect the operation cost and medical service price of the hospital, and thus affect the payment standard of DRG.

[0111] In this embodiment, the first level coefficient is a coefficient determined based on the hospital level for adjusting the standard payment information of DRG. For example, the first level coefficient of a first level hospital is 0.8. For a DRG group with a standard payment information of 15000 yuan, the adjusted payment information is 12000 yuan (15000x0.8).

[0112] In this embodiment, the second level coefficient is a coefficient determined according to the regional information corresponding to the hospital for further adjusting the standard payment information of DRG. For example, the second level coefficient of a hospital in the eastern developed area may be 1.1. For a DRG group with a standard payment information of 15000 yuan, the adjusted payment information is 16500 yuan (15000x1.1).

[0113] In this embodiment, the DRG standard payment information adjusted by the first level coefficient and the second level coefficient is the final payment standard determined according to the actual situation of the current hospital and the region. For example, the original standard payment information of a DRG group is 15000 yuan, the hospital is a third level hospital (the first level coefficient is 1.2), and it is located in the eastern developed area (the second level coefficient is 1.1). Therefore, the DRG pre-set payment standard is 15000x1.2x1.1=19800 yuan.

[0114] The beneficial effect of the above technology is that by combining the hospital level and regional information to optimize the standard payment information of DRG, the optimized DRG preset payment standard can be obtained, so that the determination of the profit and loss information of the current case is more accurate.

[0115] Embodiment 5:

[0116] Based on embodiment 4, a DRG-based profit and loss analysis method determines the profit and loss information of the current case based on the DRG preset payment standard corresponding to the current case, including:

[0117] Based on the treatment cost information of the current case and the DRG preset payment standard of the case diagnosis type corresponding to the current case, the profit and loss type of the current case is determined.

[0118] The weight information and the profit and loss type in the DRG group information corresponding to the current case are combined to obtain the profit and loss information of the current case.

[0119] In this embodiment, the profit and loss type is determined by comparing the treatment cost information of the current case with the DRG preset payment standard of the case diagnosis type corresponding to the current case to determine whether the case is in profit or loss state. If the treatment cost is lower than the DRG preset payment standard, the case is of profit type; if the treatment cost is higher than the DRG preset payment standard, the case is of loss type.

[0120] In this embodiment, the profit and loss information is obtained by combining the weight information in the DRG group information corresponding to the current case with the profit and loss type. It not only reflects the profit or loss state of the case relative to the DRG preset payment standard, but also considers the resource consumption degree and disease severity of the case in the DRG grouping system. For example, the DRG group weight information corresponding to a case is 2.0, the profit and loss type is profit, and the profit amount is 500 yuan.

[0121] The beneficial effect of the above technology is that by combining the profit and loss type and the weight information of the current case, the determined profit and loss information of the case is more comprehensive and effective.

[0122] Embodiment 6:

[0123] Based on embodiment 1, a DRG-based profit and loss analysis method S3 obtains the profit and loss analysis result of each case based on the DRG group information and the profit and loss information corresponding to each case, and combines the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital to obtain the comprehensive profit and loss analysis result of the hospital, including:

[0124] Each information aggregation mode in the preset information aggregation set is aggregated based on the DRG group information and the profit and loss information of each case to obtain the profit and loss analysis result corresponding to each information aggregation mode. The information aggregation mode includes time sequence aggregation and MDC aggregation.

[0125] According to the profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set, a first profit and loss analysis result of the hospital is obtained;

[0126] An overall profit and loss type of the hospital is obtained, and a profit and loss analysis result of each overall profit and loss type of the hospital is obtained based on the DRG group information and the profit and loss information corresponding to each case of the hospital, so as to obtain a second profit and loss analysis result of the hospital;

[0127] According to the first profit and loss analysis result and the second profit and loss analysis result of the hospital, an overall profit and loss analysis result of the hospital is obtained.

[0128] In this embodiment, the preset information aggregation set is a set of multiple methods for integrating data set in advance. The information aggregation mode aims to summarize and aggregate the DRG group information and the profit and loss information of the cases, so as to analyze the business status of the hospital from different angles. For example, common information aggregation modes include time sequence aggregation and MDC aggregation.

[0129] In this embodiment, the time sequence aggregation is to aggregate the DRG group information and the profit and loss information of the cases according to time sequence. The case data in the same time period can be analyzed by aggregating according to different time dimensions such as day, week, month, quarter and year, so as to observe the business performance and profit and loss trend of the hospital in different time periods. For example, there are 50 cases of cardiovascular system DRG group and 30 cases of respiratory system DRG group in January.

[0130] In this embodiment, the MDC aggregation is to aggregate the DRG group information and the profit and loss information of the cases according to the MDC mode. MDC means major diagnostic category, which is a large category division of diseases according to human body system or disease nature, such as nervous system, circulatory system and digestive system. For example, after the hospital aggregates the cases according to MDC classification, it is found that the case number of the circulatory system category accounts for a high proportion of 40%, and the overall profit of this category is 200,000 yuan.

[0131] In this embodiment, the first profit and loss analysis result is an analysis result about the overall profit and loss of the hospital obtained by comprehensively analyzing the profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set. For example, the analysis results of time sequence aggregation and MDC aggregation are comprehensively analyzed to obtain the conclusion that the hospital has an overall profit of 150,000 yuan in the first half of the year, among which the circulatory system category contributes an 80,000 yuan profit, but in the second quarter, the cost increases due to the update of medical equipment, resulting in a loss of 30,000 yuan, which is the first profit and loss analysis result of the hospital.

[0132] In this embodiment, the second profit and loss analysis result is a profit and loss analysis result obtained based on a comprehensive profit and loss type of the hospital, wherein the comprehensive profit and loss type includes a medical insurance settlement rate (actual payment / paid), a distribution of reasons for refusal (such as coding errors, overpayment standards), and the like.

[0133] In this embodiment, the comprehensive profit and loss analysis result is a comprehensive summary and evaluation of the first profit and loss analysis result and the second profit and loss analysis result of the hospital, which comprehensively and systematically summarizes and evaluates the profit and loss status of the hospital. It covers analysis from different aggregation modes to overall profit and loss types and the like.

[0134] The above-mentioned technical beneficial effects are that by determining the profit and loss analysis result of each case and combining the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital, the profit and loss analysis of the hospital can be performed from multiple dimensions, so that the comprehensive profit and loss analysis result is more comprehensive and more effectively supports the hospital management.

[0135] Embodiment 7:

[0136] Based on the embodiment 6, a DRG-based profit and loss analysis method, according to the profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set, obtains the first profit and loss analysis result of the hospital, specifically including:

[0137] According to the diagnosis complexity of each case diagnosis type in the hospital, a case complexity coefficient of each case diagnosis type is obtained;

[0138] Obtain personnel attribute information and clinical diagnosis information of each case in each case diagnosis type, and based on the matching result of the personnel attribute information and the clinical diagnosis information, determine a case diagnosis influence coefficient of each case in the case diagnosis type;

[0139] The average of the diagnosis influence coefficients of each case in the case diagnosis type is taken as the diagnosis influence coefficient of the current case diagnosis type;

[0140] According to the time sequence aggregation mode, a profit and loss analysis result of each case diagnosis type is obtained as the first profit and loss analysis information of the current case diagnosis type;

[0141] Based on the case complexity coefficient and the diagnosis influence coefficient of each case diagnosis type, the corresponding first profit and loss analysis information is optimized to obtain the first optimized profit and loss analysis information of the case diagnosis type;

[0142] Based on the first optimized profit and loss analysis information of each case diagnosis type, a first profit and loss analysis information set is obtained;

[0143] Based on the MDC aggregation mode, a profit and loss analysis result of each case diagnosis type of the same disease is obtained as the second profit and loss analysis information of the current disease;

[0144] Based on the case complexity coefficient of each case diagnosis type of the same disease, a disease complexity coefficient of the current disease is obtained;

[0145] Meanwhile, based on the diagnosis influence coefficient of each case diagnosis type of the same disease, a disease diagnosis influence coefficient of the current disease is obtained;

[0146] Based on the disease complexity coefficient and the disease diagnosis influence coefficient of each disease, the corresponding second profit and loss analysis information is optimized to obtain second optimized profit and loss analysis information of each disease;

[0147] Based on the second optimized profit and loss analysis information of each disease, a second profit and loss analysis information set is obtained;

[0148] According to the first profit and loss analysis information set and the second profit and loss analysis information set, a first profit and loss analysis result of the hospital is obtained.

[0149] In this embodiment, the case complexity coefficient is used to measure the complexity of each case diagnosis type in the hospital diagnosis process. It comprehensively considers the severity of the disease itself, the difficulty of treatment, the types and quantities of required medical resources, the complications of the disease, and the risks that may occur during the treatment process, etc. For example, the case diagnosis type of "simple clavicular fracture" is relatively simple, usually treated conservatively or with simple surgery, and its case complexity coefficient is low, which is set to 0.3. The value range of the case complexity coefficient is (0, 1).

[0150] In this embodiment, the personnel attribute information refers to the basic attribute information of patients and medical personnel related to the case. The patient aspect includes age, gender, occupation, medical insurance type, residence area, etc.; the medical personnel aspect involves the doctor's title, professional field, work experience, and department to which the medical personnel belongs, etc.

[0151] In this embodiment, the case diagnosis influence coefficient is determined based on the matching result of the personnel attribute information and the clinical diagnosis information. For example, a young patient without other underlying diseases and with mild renal function impairment has a low diagnosis influence coefficient, which is set to 0.4. The value range of the case diagnosis influence coefficient is (0, 1).

[0152] In this embodiment, the diagnosis influence coefficient is obtained by averaging the diagnosis influence coefficients of each case in the case diagnosis type. For example, there are 10 cases under a certain case diagnosis type, and the case diagnosis influence coefficients are 0.7, 0.8, 0.6, 0.9, 0.7, 0.8, 0.6, 0.7, 0.8, and 0.7. The average value of the case diagnosis influence coefficients is (0.7+0.8+0.6+0.9+0.7+0.8+0.6+0.7+0.8+0.7) / 10=0.73, i.e. the diagnosis influence coefficient of the case diagnosis type is 0.73.

[0153] In this embodiment, the first profit and loss analysis information is a summary analysis of the case treatment information in chronological order. Through time series aggregation, the income, cost, profit or loss of each case diagnosis type in different time periods can be obtained, for example, time series aggregation is performed in a monthly time dimension, for the "acute myocardial infarction" case diagnosis type, the income in January is 800,000 yuan, the cost is 900,000 yuan, and the loss is 10,000 yuan; the income in February is 95,000 yuan, the cost is 85,000 yuan, and the profit is 10,000 yuan. The profit and loss of these two months is the first profit and loss analysis information of the case diagnosis type.

[0154] In this embodiment, the first optimized profit and loss analysis information is obtained by optimizing the first profit and loss analysis information of each case diagnosis type based on the case complexity coefficient and the diagnosis influence coefficient of the case diagnosis type. For example, the first profit and loss analysis information of a certain case diagnosis type shows a loss of 150,000 yuan, the case complexity coefficient is 0.8, and the diagnosis influence coefficient is 0.7. After optimization calculation, the first optimized profit and loss analysis information is a loss of 12,000 yuan.

[0155] In this embodiment, the first profit and loss analysis information set is a set obtained by summarizing the first optimized profit and loss analysis information of each case diagnosis type. For example, a hospital has "cardiovascular disease", "respiratory disease", "digestive disease" and other case diagnosis types, and their first optimized profit and loss analysis information is a profit of 250,000 yuan, a loss of 180,000 yuan, and a profit of 12,000 yuan, respectively. The first profit and loss analysis information set is {a profit of 250,000 yuan, a loss of 180,000 yuan, and a profit of 12,000 yuan}.

[0156] In this embodiment, the second profit and loss analysis information refers to the profit and loss analysis diagnosis results of multiple case diagnosis types of the same disease.

[0157] In this embodiment, the disease complexity coefficient is calculated based on the case complexity coefficient of each case diagnosis type of the same disease. For example, under the "nervous system" disease, the case complexity coefficient of the "cerebral infarction" case diagnosis type is 0.7, and the case complexity coefficient of the "cerebral hemorrhage" case diagnosis type is 0.8. Through weighted average or other reasonable methods, the disease complexity coefficient of the disease is calculated as (0.7+0.8) / 2=0.75.

[0158] In this embodiment, the disease diagnosis influence coefficient is calculated according to the diagnosis influence coefficient of each case diagnosis type of the same disease. For example, under the "nervous system" disease, the diagnosis influence coefficient of the "cerebral infarction" case diagnosis type is 0.6, and the diagnosis influence coefficient of the "cerebral hemorrhage" case diagnosis type is 0.7. The disease diagnosis influence coefficient of the disease is calculated as (0.6+0.7) / 2=0.65.

[0159] In this embodiment, the second optimized profit and loss analysis information is obtained by optimizing the second profit and loss analysis information of each disease type based on the disease complexity coefficient and the disease diagnosis influence coefficient. The complexity of the disease and the diagnosis influence factor are considered to adjust the second profit and loss analysis information, so that the profit and loss analysis can more accurately reflect the actual operation of the disease type. For example, the second profit and loss analysis information of a certain disease type shows a profit of 80,000 yuan, the disease complexity coefficient is 0.7, and the disease diagnosis influence coefficient is 0.6. After optimization calculation, the second optimized profit and loss analysis information is a profit of 60,000 yuan.

[0160] In this embodiment, the second profit and loss analysis information set is a set obtained by summarizing the second optimized profit and loss analysis information of each disease type, which includes the optimized profit and loss of all disease types of the hospital.

[0161] In this embodiment, the first profit and loss analysis information and the second profit and loss analysis information include case profit and loss trends and case weight proportion trends.

[0162] In this embodiment, the first profit and loss analysis result is obtained according to the first profit and loss analysis information set and the second profit and loss analysis information set. The optimized profit and loss information from the case diagnosis type and the disease type is comprehensively considered, which more comprehensively reflects the overall profit and loss situation of the hospital. The first profit and loss analysis information set shows a total profit of 200,000 yuan, the second profit and loss analysis information set shows a total profit of 150,000 yuan, and the first profit and loss analysis result of the hospital is a total profit of 180,000 yuan after comprehensive consideration.

[0163] The beneficial effects of the above technology are: by analyzing the first profit and loss information of each case diagnosis type through the case complexity coefficient and the diagnosis influence coefficient of each case diagnosis type, the accuracy of the profit and loss analysis result of each case diagnosis type is improved. At the same time, by analyzing the second profit and loss information of each disease type through the disease complexity coefficient and the disease diagnosis complexity coefficient of each disease type, the profit and loss analysis situation of the hospital can be more accurately mastered from different levels.

[0164] Embodiment 8:

[0165] Based on the embodiment 1, a DRG-based profit and loss analysis method further comprises: performing case diagnosis optimization according to the comprehensive profit and loss analysis result, specifically comprising:

[0166] Extracting the case diagnosis types corresponding to the cases with losses in the comprehensive profit and loss analysis result to obtain a case diagnosis type set;

[0167] Obtaining weight information corresponding to each case diagnosis type in the case diagnosis type set;

[0168] The weight information is compared with a preset weight threshold, so that different diagnosis optimization schemes are determined based on different comparison results, to optimize diagnosis of each case diagnosis type.

[0169] In this embodiment, the set of case diagnosis types is a set of case diagnosis types corresponding to all cases with losses in the comprehensive profit and loss analysis result.

[0170] In this embodiment, the preset weight threshold is a weight limit value preset by the hospital according to its own business objectives, resource conditions and medical level and other factors. This threshold is used to determine whether the weight of the case diagnosis type is too high, so as to determine whether optimization adjustment of the diagnosis type is needed. When the weight of the case diagnosis type exceeds the preset weight threshold, it means that the case diagnosis type may cause great pressure on the resources of the hospital, for example, the hospital sets the preset weight threshold to 2.8. Then, for the case diagnosis type of "coronary atherosclerotic heart disease, unstable angina" with a weight of 2.5, the threshold is not exceeded; and for the case diagnosis type of "cerebral hemorrhage without complications" with a weight of 3.0, the threshold is exceeded.

[0171] In this embodiment, the diagnosis optimization scheme compares the weight information of the case diagnosis type with the preset weight threshold, and formulates different diagnosis optimization schemes according to the comparison result (exceeding the threshold or not exceeding the threshold), such as, for the case diagnosis type with a weight exceeding the threshold, if it is in a loss state, the adjustment of the diagnosis and treatment process or the use of consumables is carried out, and for the case diagnosis type with a weight exceeding the threshold, if it is in a loss state, the admission can be reduced or the cost structure can be optimized.

[0172] The beneficial effects of the above technology are that by performing diagnosis optimization in different ways for case diagnosis types with different weights, the profit situation of each case diagnosis type of the hospital can be improved, and the cost structure of each case diagnosis type of the hospital can be optimized.

[0173] Embodiment 9:

[0174] The present application provides a DRG-based profit and loss analysis system, referring to Figure 2 , comprising:

[0175] The data acquisition module is configured to acquire case treatment information of the hospital, and group the case treatment information by using disease diagnosis related grouping (DRG) to obtain DRG group information corresponding to each case, wherein the case treatment information comprises treatment cost information.

[0176] The information determination module is configured to determine the profit and loss information of each case based on the treatment cost information of each case and the DRG preset payment standard of the case diagnosis type corresponding to the case.

[0177] The profit and loss analysis module is configured to obtain the profit and loss analysis result of each case based on the DRG group information and the profit and loss information corresponding to each case, and to obtain the comprehensive profit and loss analysis result of the hospital by combining the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital.

[0178] The above technology has the beneficial effect that: by obtaining the case treatment information of the hospital, using the DRG grouping technology to accurately group the case treatment information, and determining the profit and loss of each case based on the treatment cost information and the DRG preset payment standard of each case after grouping, the income and expenditure of each case can be more accurately mastered, and the comprehensive profit and loss analysis result of the hospital can be determined by combining the profit and loss analysis result corresponding to the comprehensive profit and loss analysis type of the hospital, so that the profit and loss analysis result of the hospital can be more comprehensively, meticulously and accurately presented, and more effective support can be provided for hospital management.

[0179] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A DRG-based break-even analysis method, characterized by, The method comprises the following steps: S1, obtaining case treatment information of a hospital, and grouping the case treatment information by using disease diagnosis related grouping (DRG) to obtain DRG group information corresponding to each case, wherein the case treatment information comprises treatment cost information; S2, determining profit and loss information of each case based on the treatment cost information of each case and DRG preset payment standards of the case diagnosis type corresponding to the case; S3, obtaining profit and loss analysis results of each case based on the DRG group information corresponding to each case and the profit and loss information, and synthesizing the profit and loss analysis results with profit and loss analysis results of a comprehensive profit and loss analysis type of the hospital to obtain comprehensive profit and loss analysis results of the hospital.

2. The DRG-based profit and loss analysis method of claim 1, wherein, S1, obtaining case treatment information of a hospital, and grouping the case treatment information by using disease diagnosis related grouping (DRG) to obtain DRG group information corresponding to each case, comprising: Obtaining initial case treatment information of a hospital, and extracting key information from the initial case treatment information according to the diagnosis requirements of DRG to obtain case treatment information, wherein the case treatment information comprises clinical diagnosis information, treatment cost information and treatment resource consumption information; Judging information consistency of information corresponding to the clinical diagnosis information and the treatment resource consumption information, and if the information consistency is higher than a preset information consistency, judging that the case treatment information is valid case treatment information; Converting the valid case treatment information by information coding, and grouping the coded case treatment information based on disease diagnosis related grouping (DRG); Obtaining a case treatment set of the hospital according to the grouping result, wherein each subset in the case treatment set corresponds to a case diagnosis type; Extracting DRG group information corresponding to each case diagnosis type based on each case diagnosis type, wherein the DRG group information comprises DRG group coding and weight information.

3. The DRG-based profit and loss analysis method of claim 2, wherein, Extracting DRG group information corresponding to each case diagnosis type, comprising: Extracting DRG group coding corresponding to each case diagnosis type from a preset DRG database; And obtaining cases of each case diagnosis type of the hospital within a preset time period, and obtaining a ratio of the cases of each case diagnosis type to total cases of the hospital as weight information corresponding to each case diagnosis type.

4. The DRG-based profit and loss analysis method of claim 1, wherein, S2, determining profit and loss information of each case based on the treatment cost information of each case and DRG preset payment standards of the case diagnosis type corresponding to the case, comprising: Obtaining standard payment information of DRG corresponding to each case diagnosis type, hospital level and region information corresponding to the hospital; Determining a first level coefficient of the standard payment information of DRG based on the hospital level, and determining a second level coefficient of the standard payment information of DRG based on the region information corresponding to the hospital; Optimizing the standard payment information of DRG of the corresponding case diagnosis type according to the first level coefficient and the second level coefficient to obtain DRG preset payment standards corresponding to the case diagnosis type of the current case; Determining profit and loss information of the current case according to the DRG preset payment standards corresponding to the current case.

5. The DRG-based profit and loss analysis method of claim 4, wherein, Determining profit and loss information of the current case based on the DRG preset payment standards corresponding to the current case, comprising: judging a profit and loss type of the current case based on treatment cost information of the current case and a DRG preset payment standard corresponding to a case diagnosis type of the current case; combining weight information in DRG group information corresponding to the current case and the profit and loss type to obtain profit and loss information of the current case.

6. The DRG-based profit and loss analysis method of claim 1, wherein, S3, obtaining a profit and loss analysis result of each case based on DRG group information and profit and loss information corresponding to each case, and synthesizing the profit and loss analysis result of the comprehensive profit and loss analysis type of the hospital to obtain a comprehensive profit and loss analysis result of the hospital, including: aggregating DRG group information and profit and loss information of each case based on each information aggregation mode in a preset information aggregation set to obtain a profit and loss analysis result corresponding to each information aggregation mode, the information aggregation mode including time sequence aggregation and MDC aggregation; obtaining a first profit and loss analysis result of the hospital according to the profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set; obtaining a comprehensive profit and loss type of the hospital, and obtaining a profit and loss analysis result of each comprehensive profit and loss type of the hospital based on DRG group information and profit and loss information corresponding to each case of the hospital, thereby obtaining a second profit and loss analysis result of the hospital; obtaining a comprehensive profit and loss analysis result of the hospital according to the first profit and loss analysis result and the second profit and loss analysis result of the hospital.

7. The DRG-based profit and loss analysis method of claim 6, wherein, obtaining a first profit and loss analysis result of the hospital according to the profit and loss analysis result corresponding to each information aggregation mode in the preset information aggregation set, specifically including: obtaining a case complexity coefficient of each case diagnosis type according to diagnosis complexity of each case diagnosis type in the hospital; obtaining personnel attribute information and clinical diagnosis information of each case in each case diagnosis type, and determining a case diagnosis influence coefficient of each case in the case diagnosis type based on a matching result of the personnel attribute information and the clinical diagnosis information; taking an average value of the diagnosis influence coefficients of each case in the case diagnosis type as a diagnosis influence coefficient of the current case diagnosis type; obtaining a profit and loss analysis result of each case diagnosis type according to the time sequence aggregation mode as first profit and loss analysis information of the current case diagnosis type; optimizing the corresponding first profit and loss analysis information based on the case complexity coefficient and the diagnosis influence coefficient of each case diagnosis type to obtain first optimized profit and loss analysis information of the case diagnosis type; obtaining a first profit and loss analysis information set based on the first optimized profit and loss analysis information of each case diagnosis type; obtaining a profit and loss analysis result of each case diagnosis type of the same disease as second profit and loss analysis information of the current disease based on the MDC aggregation mode; obtaining a disease complexity coefficient of the current disease based on a case complexity coefficient of each case diagnosis type of the same disease; at the same time, obtaining a disease diagnosis influence coefficient of the current disease based on a diagnosis influence coefficient of each case diagnosis type of the same disease; optimizing the corresponding second profit and loss analysis information based on the disease complexity coefficient and the disease diagnosis influence coefficient of each disease to obtain second optimized profit and loss analysis information of each disease; obtaining a second profit and loss analysis information set based on the second optimized profit and loss analysis information of each disease; The first profit and loss analysis result of the hospital is obtained according to the first profit and loss analysis information set and the second profit and loss analysis information set.

8. The DRG-based profit and loss analysis method of claim 7, wherein, Further comprising: The case diagnosis optimization is performed according to the comprehensive profit and loss analysis result, and specifically comprising: The case diagnosis type set is obtained by extracting the case diagnosis types corresponding to the cases with losses in the comprehensive profit and loss analysis result; The weight information corresponding to each case diagnosis type in the case diagnosis type set is obtained; The weight information is compared with a preset weight threshold, so that different diagnosis optimization schemes are determined based on different comparison results to perform diagnosis optimization on each case diagnosis type.

9. A DRG-based break-even analysis system, characterized by, Comprising: The data acquisition module is configured to acquire case treatment information of a hospital, group the case treatment information by using disease diagnosis related grouping (DRG), and obtain DRG group information corresponding to each case, wherein the case treatment information comprises treatment cost information; The information determination module is configured to determine profit and loss information of each case based on the treatment cost information of each case and a DRG preset payment standard of the case diagnosis type corresponding to the case; The profit and loss analysis module is configured to obtain a profit and loss analysis result of each case based on the DRG group information and the profit and loss information corresponding to each case, and integrate the profit and loss analysis result with a comprehensive profit and loss analysis result of the hospital to obtain the comprehensive profit and loss analysis result of the hospital.

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