Accurate value incentive accounting method for DIP disease category subdivision value portrait under DRG

Abstract

The invention discloses an accurate value incentive accounting method for a DIP disease category subdivision value portrait under DRG, and belongs to the technical field of medical payment and hospital management, and the method comprises the following steps: 1) a data fusion model: integrating DRG grouped data, a DIP score, a clinical path execution rate, cost accounting and patient satisfaction data, and constructing economic, clinical and social three-dimensional value portraits; 2) dynamic grouping optimization: introducing a machine learning algorithm, and dynamically subdividing DIP disease species into subgroups according to complication severity, treatment mode difference and resource consumption dispersion; after disease category subdivision, the matching degree of the DIP score and the actual cost is improved by 20%-30%, the medical insurance payment refusal risk is reduced, payment accuracy improvement is achieved, through strategic disease category support, the CMI value is averagely improved by 0.15, the difficult case recovery rate is increased by 12%, medical quality optimization is achieved, and through linkage excitation of the balance contribution rate and the patient satisfaction degree, the medical quality is improved. The medicine consumption ratio is reduced by 5-8%, the hospitalization day is shortened by 1.2 days, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of medical payment and hospital management technology, and in particular relates to a method for calculating accurate value incentives for DIP disease segmentation value portraits under DRG. Background Art

[0002] DRG and DIP, as core tools for medical insurance payment reform, have been widely used in hospital cost control and performance management.

[0003] At present, the existing DRG / DIP accounting focuses more on economic costs (such as the proportion of drug consumption), and lacks a multi-dimensional comprehensive evaluation of clinical value (such as CMI value), social value (such as patient satisfaction) and brand value. Performance incentives are mostly based on departmental surpluses or RBRVS point values, which can easily lead to over-medicalization or shirking of critically ill patients, and conflict with the DRG / DIP cost control goals. The DRG and DIP grouping rules, clinical pathways, and cost accounting data are isolated, making it difficult to support dynamic adjustments and accurate decision-making. The DIP score setting relies on the historical cost average, and does not take into account the complexity of the disease, complications, and differences in resource consumption, resulting in deviations between the grouping and actual costs. Summary of the Invention

[0004] The purpose of the present invention is to propose a method for calculating the precise value incentives of the DIP disease segmentation value portrait under DRG in order to solve the above-mentioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for calculating the precise value incentives of DIP disease segmentation value portraits under DRG, which comprises the following steps:

[0006] 1) Data fusion model: Integrate DRG grouping data, DIP scores, clinical pathway execution rates, cost accounting, and patient satisfaction data to construct a three-dimensional value portrait of economic, clinical, and social value;

[0007] 2) Dynamic grouping optimization: Introducing machine learning algorithms to dynamically subdivide DIP diseases into subgroups based on complication severity, treatment differences, and resource consumption dispersion;

[0008] 3) Four-quadrant value analysis: Based on the Boston Matrix, diseases are divided into four categories: advantageous diseases, strategic diseases, suitable diseases, and disadvantageous diseases;

[0009] 4) Calculation of surplus contribution: Define the surplus contribution rate of each disease type, combine the CMI value and patient satisfaction, and dynamically adjust the performance weight;

[0010] 5) Dynamic feedback and intelligent decision-making: Predict DRG / DIP grouping results through the electronic medical record interface, and automatically trigger clinical pathway optimization suggestions for overspending cases; dynamically optimize the disease admission structure based on regional medical insurance point value adjustment trends.

[0011] As a further description of the above technical solution:

[0012] In step 3), for advantageous diseases: 50% of the surplus contribution is used for department performance; for strategic diseases: a technology innovation fund is established, and 30% of the surplus is invested in research and development; for disadvantageous diseases: 20% of the surplus loss is specially subsidized by the hospital to encourage process optimization.

[0013] As a further description of the above technical solution:

[0014] In step 4), for every 0.1 increase in the CMI value, the balance contribution rate will be increased by 5%; if the patient satisfaction rate is ≥90%, the performance reward will be increased by 10%.

[0015] As a further description of the above technical solution:

[0016] In step 4), the calculation formula for the disease balance contribution rate is defined as:

[0017]

[0018] As a further description of the above technical solution:

[0019] In step 3), the advantageous diseases have high economic value + high clinical value, the strategic diseases have low economic value + high clinical value, the suitable diseases have high economic value + low clinical value, and the disadvantageous diseases have low economic value + low clinical value.

[0020] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0021] In the present invention, after disease segmentation, the matching degree between DIP score and actual cost is improved by 20%-30%, which reduces the risk of medical insurance refusal and improves payment accuracy. Through support for strategic diseases, the CMI value is increased by an average of 0.15, and the admission rate of difficult cases increases by 12%, which optimizes medical quality. Through the linkage incentive between surplus contribution rate and patient satisfaction, the proportion of drug consumption is reduced by 5%-8%, the length of hospital stay is shortened by 1.2 days, and operational efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for calculating precise value incentives based on the value profiling of DIP disease segments under DRG. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] Example:

[0025] S01: Data fusion model: Integrate DRG grouping data, DIP scores, clinical pathway execution rate, cost accounting and patient satisfaction data to build a three-dimensional value portrait of economic, clinical and social aspects;

[0026] S02: Dynamic grouping optimization: Introducing machine learning algorithms to dynamically subdivide DIP diseases into subgroups based on the severity of complications, differences in treatment methods, and discrete resource consumption;

[0027] S03: Four-quadrant value analysis: Based on the Boston Matrix, diseases are divided into four categories: advantageous diseases, strategic diseases, suitable diseases, and disadvantageous diseases. Advantageous diseases: 50% of the surplus contribution is used for department performance; Strategic diseases: A technology innovation fund is established, and 30% of the surplus is invested in research and development; Disadvantageous diseases: 20% of the surplus loss is subsidized by the hospital to incentivize process optimization. Advantageous diseases are characterized by high economic value + high clinical value, strategic diseases are characterized by low economic value + high clinical value, suitable diseases are characterized by high economic value + low clinical value, and disadvantageous diseases are characterized by low economic value + low clinical value.

[0028] S04: Calculation of balance contribution: Define the disease type balance contribution rate. The calculation formula for the disease type balance contribution rate is:

[0029]

[0030] Dynamically adjust performance weights based on CMI values and patient satisfaction. For every 0.1 increase in CMI value, the balance contribution rate will be increased by 5%. For patient satisfaction ≥ 90%, the performance bonus will be increased by 10%.

[0031] S05: Dynamic feedback and intelligent decision-making: Predict DRG / DIP grouping results through the electronic medical record interface, and automatically trigger clinical pathway optimization suggestions for overspending cases; dynamically optimize the disease admission and treatment structure based on the regional medical insurance point value adjustment trend.

[0032] Specifically:

[0033] Data collection and cleaning: Extract medical record front page data (primary diagnosis, surgical procedure, complications) from the HIS system, obtain full cost data (drug consumption, labor, equipment depreciation) from the financial system, synchronize DIP scores and DRG weights with the medical insurance platform. Cleaning rules: Eliminate coding errors (such as mismatch between primary diagnosis and surgery) and outliers (costs exceeding the payment standard by more than three times);

[0034] Disease segmentation and profile generation: Clustering algorithms (such as K-means) are used to divide patients into subgroups based on treatment complexity (surgical classification), resource consumption (cost structure), and complication level. These subgroups generate disease labels (such as "acute myocardial infarction - low-risk thrombolysis group"). The profile output includes economic indicators (cost rate, balance contribution), clinical indicators (CMI value, mortality rate), and social indicators (satisfaction, readmission rate) associated with each disease label.

[0035] Incentive accounting and performance distribution: For advantageous diseases, 50% of the surplus contribution will be used for department performance; for strategic diseases, a technology innovation fund will be established, and 30% of the surplus will be invested in research and development; for disadvantaged diseases, 20% of the surplus loss will be subsidized by the hospital. Incentive process optimization and performance distribution: transparent accounting will be achieved through blockchain technology to avoid human intervention.

[0036] Dynamic optimization and iteration: Update disease profiles every quarter, combine medical insurance policy adjustments (such as DRG2.0 grouping rules) with clinical feedback, retrain algorithm models, and optimize grouping and incentive parameters.

[0037] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for calculating the precise value incentives for DIP disease segmentation value profiling under DRG, characterized by: The steps include: 1) Data fusion model: Integrate DRG grouping data, DIP scores, clinical pathway execution rates, cost accounting, and patient satisfaction data to construct a three-dimensional value portrait of economic, clinical, and social value; 2) Dynamic grouping optimization: Introducing machine learning algorithms to dynamically subdivide DIP diseases into subgroups based on complication severity, treatment differences, and resource consumption dispersion; 3) Four-quadrant value analysis: Based on the Boston Matrix, diseases are divided into four categories: advantageous diseases, strategic diseases, suitable diseases, and disadvantageous diseases; 4) Calculation of surplus contribution: Define the surplus contribution rate of each disease type, combine the CMI value and patient satisfaction, and dynamically adjust the performance weight; 5) Dynamic feedback and intelligent decision-making: Predict DRG / DIP grouping results through the electronic medical record interface, and overspending cases automatically trigger clinical pathway optimization suggestions; Based on the adjustment trend of regional medical insurance point values, the disease treatment structure is dynamically optimized.

2. The method for calculating the precise value incentive of DIP disease segmentation value portrait under DRG according to claim 1 is characterized in that: In step 3), for advantageous diseases: 50% of the surplus contribution is used for department performance; for strategic diseases: a technology innovation fund is established, and 30% of the surplus is invested in research and development; for disadvantageous diseases: 20% of the surplus loss is specially subsidized by the hospital to encourage process optimization.

3. The method for calculating the precise value incentive of DIP disease segmentation value portrait under DRG according to claim 1 is characterized in that: In step 4), for every 0.1 increase in the CMI value, the balance contribution rate will be increased by 5%; if the patient satisfaction rate is ≥ 90%, the performance reward will be increased by 10%.

4. The method for calculating the precise value incentive of DIP disease segmentation value portrait under DRG according to claim 1 is characterized in that: In step 4), the calculation formula for the disease balance contribution rate is defined as:

5. The method for calculating the precise value incentive of DIP disease segmentation value portrait under DRG according to claim 1 is characterized in that: In step 3), the advantageous diseases have high economic value + high clinical value, the strategic diseases have low economic value + high clinical value, the suitable diseases have high economic value + low clinical value, and the disadvantageous diseases have low economic value + low clinical value.

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