A DRG DIP disease type operation performance accounting method

Abstract

The present application relates to the medical technical field, specifically relates to a kind of DRGDIP disease operation performance accounting method, comprising the following steps: S100: obtaining the actual medical expense data of target disease and medical insurance settlement data, based on the first preset rule, the income confirmation data and the cost confirmation data of the target disease are calculated;S200: according to the income confirmation data and the cost confirmation data, the marginal income data of the target disease is calculated;The present application breaks the data barrier that traditional medical accounting system account receivable and payable is disjointed, and the real marginal fund pool containing medical insurance profit and loss is accurately stripped out through the condition trigger mechanism of machine bottom layer;While introducing the multi-branch data fusion algorithm based on role label, the complex clinical labor value is converted into multi-dimensional integral data and dynamic unit price model that can be efficiently processed by computer, the automation and fine segmentation of doctor, nursing and medical skill performance under the packaged payment mechanism are realized.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a method for calculating the operational performance of DRGDIP disease categories. Background Technology

[0002] With the continuous advancement of the reform of the medical security system, my country's medical insurance payment method is shifting from the traditional "fee-for-service" to a bundled payment model based on "Diagnosis Related Groups (DRG)" and "Disease-Specific Values ​​(DIP)". This fundamental institutional transformation requires medical institutions to shift from the past extensive expansion model to a refined, intrinsic cost control model.

[0003] However, existing hospital information systems and most traditional operational performance accounting software still rely on an "item-based" data architecture and accounting logic. Faced with the DRG / DIP payment model, existing technologies exhibit the following significant technical shortcomings: First, the basic revenue and expenditure data model is distorted. The existing system can only capture the actual medical expenses incurred by patients during their hospital stay, and cannot connect with and automatically process the "weights / scores" and "rates / points" issued by the medical insurance system at the data level. This results in the system being unable to identify and calculate the medical insurance settlement surplus and overspending loss data unique to the DRG / DIP model. This causes the hospital's performance accounting to deviate from the true boundary of the fund pool at the "data source".

[0004] Second, there is a lack of multi-dimensional data quantification and allocation algorithms for bundled payment systems. Under the DRG / DIP bundled payment mechanism, the revenue for the same disease is a mixed product of the joint labor of multiple business units such as doctors, nurses, and medical technicians. However, most existing financial accounting systems use simple static instructions such as "full cost allocation" or "fixed percentage commission," which cannot automatically capture and integrate multi-dimensional heterogeneous data (such as CCHI operation code points, surgical grade and level points, and disease severity) that reflect the true medical difficulty and labor value through computer systems. This results in the performance allocation process being highly dependent on manual subjective analysis, with high computational costs and a high risk of data disputes.

[0005] Third, the system lacks a closed-loop decision feedback mechanism. Most existing accounting tools can only output static summary reports and lack the ability to map micro-level disease performance data to macro-level operational structured characteristics. They cannot automatically construct data matrices to output strategic instructions regarding hospital-wide performance fairness and disease structure adjustments, resulting in a serious lag in the allocation of medical resources compared to clinical practice.

[0006] In summary, there is an urgent need for a data processing method that can adapt to DRG / DIP payment logic and achieve full-link automation from cost deduction and multi-dimensional performance allocation to macro-operation matrix analysis through multi-branch data fusion and dynamic parameter conversion at the computer's underlying level. Summary of the Invention

[0007] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for calculating the operational performance of DRGDIP disease categories, comprising the following steps: S100: acquiring actual medical expense data and medical insurance settlement data for the target disease, and calculating revenue confirmation data and expense confirmation data for the target disease based on a first preset rule; S200: calculating marginal revenue data for the target disease based on the revenue confirmation data and the expense confirmation data; S300: acquiring performance score data of each business unit participating in the diagnosis and treatment of the target disease, and calculating disease performance data for each business unit based on preset score unit prices; S400: aggregating the disease performance data of each business unit to obtain total disease performance data, and calculating operational performance revenue data for the target disease based on the marginal revenue data; S500: generating and outputting disease operation evaluation indicators based on the operational performance revenue data, wherein the disease operation evaluation indicators are used to indicate the allocation of medical resources and the adjustment of disease structure.

[0008] Further, the step of calculating the revenue recognition data for the target disease based on the first preset rule specifically includes: obtaining the actual medical expense data and medical insurance settlement surplus data for the target disease; summing the actual medical expense data and the medical insurance settlement surplus data, and determining the summation result as the revenue recognition data for the target disease; wherein, the step of generating the medical insurance settlement surplus data includes: obtaining the actual weight data or score data for the target disease, and the corresponding rate data or point value data; multiplying the actual weight data or score data and the rate data or point value data to obtain medical insurance settlement data; calculating the difference between the medical insurance settlement data and the actual medical expense data, and determining the difference as the medical insurance settlement surplus data if the difference is greater than zero.

[0009] Further, the step of calculating the cost confirmation data for the target disease based on the first preset rule specifically includes: acquiring drug cost data, consumable cost data, and medical insurance settlement loss data for the target disease; adding and fusing the drug cost data, the consumable cost data, and the medical insurance settlement loss data to obtain the cost confirmation data for the target disease; wherein, the drug cost data is obtained by summing the acquired Western medicine and traditional Chinese medicine cost data with the traditional Chinese medicine decoction piece revenue data converted according to a preset ratio parameter, the preset ratio parameter being 80%; the consumable cost data is obtained by summing the acquired high-value consumable cost data with the general-charge consumable cost data; the step of generating the medical insurance settlement loss data includes: calculating the difference between the medical insurance settlement data and the actual medical cost data, and determining the absolute value of the difference as the medical insurance settlement loss data if the difference is less than zero.

[0010] Further, the step of calculating the marginal revenue data of the target disease based on the revenue recognition data and the expense recognition data specifically includes: extracting the revenue recognition data and the expense recognition data calculated for the target disease; performing a difference calculation on the revenue recognition data and the expense recognition data to obtain a first difference result; determining the first difference result as the marginal revenue data of the target disease; after calculating the marginal revenue data of the target disease, performing a ratio calculation on the marginal revenue data and the revenue recognition data to obtain the marginal revenue rate data of the target disease, wherein the marginal revenue rate data is used to characterize the marginal profitability level of the target disease.

[0011] Furthermore, the business units participating in the diagnosis and treatment of the target disease include: a physician business unit, a nursing business unit, and a medical technology business unit; the step of calculating the disease performance data of each business unit by combining the preset point unit price corresponding to each business unit specifically includes: for any business unit, extracting the performance point data corresponding to that business unit and the pre-configured point unit price data; performing a product operation on the performance point data and the point unit price data, and determining the product result as the disease performance data corresponding to that business unit; and summarizing the product results corresponding to the physician business unit, the nursing business unit, and the medical technology business unit respectively to construct a disease performance data set for each business unit.

[0012] Further, the acquisition of performance score data for each business unit participating in the diagnosis and treatment of the target disease specifically includes: If the business unit is determined to be a physician business unit, capturing the admission assessment score data, discharge case score data, CCHI score data, surgical grading score data, and DRG / DIP quadrant score data associated with the target disease; and adding and fusing the captured data to determine the performance score data corresponding to the physician business unit; If the business unit is determined to be a nursing business unit, capturing the admission score data, discharge case score data, CCHI score data, surgical grading score data, and DRG / DIP quadrant score data associated with the target disease; and adding and fusing the data to determine the performance score data corresponding to the nursing business unit; If the business unit is determined to be a medical technology business unit, directly extracting preset DRG / DIP disease performance score data as the performance score data corresponding to the medical technology business unit.

[0013] Furthermore, the pre-configured points-based unit price data is generated using the following data processing rules: Obtain the actual total performance amount and total performance points data for a specific business unit within the target accounting period; calculate the ratio between the actual total performance amount and the total performance points data; and feed back the calculated ratio result and update it to the points unit price data corresponding to the specific business unit.

[0014] Further, the aggregation of disease performance data from each of the business units to obtain total disease performance data, and the calculation of operational performance revenue data for the target disease in conjunction with the marginal revenue data, specifically includes: extracting disease performance data corresponding to the doctor business unit, the nursing business unit, and the medical technology business unit from the generated disease performance data set; adding and merging the disease performance data corresponding to each of the above business units, and determining the sum of the merged data as the total disease performance data for the target disease; extracting the marginal revenue data generated for the target disease, and performing a difference calculation between the marginal revenue data and the total disease performance data to obtain a second difference result; and determining the second difference result as the operational performance revenue data for the target disease, wherein the operational performance revenue data is used to characterize the net revenue of the target disease after deducting the performance costs of each business unit.

[0015] Furthermore, after calculating the operational performance revenue data for the target disease, the method further includes performing contribution data derivation calculations for specific business units and the overall disease. Specific steps include: calculating the ratio between the operational performance revenue data and the marginal revenue data to obtain the operational performance return rate data for the target disease; for any given business unit, calculating the difference between the marginal revenue data and the disease performance data corresponding to that business unit to generate the direct contribution data of that business unit to the target disease; calculating the ratio between the disease performance data corresponding to that business unit and the generated direct contribution data, and combining this with percentage conversion logic to generate the direct contribution rate data for the performance of that business unit.

[0016] Further, the step of generating and outputting disease-specific operational evaluation indicators based on the operational performance revenue data specifically includes: extracting the disease-specific performance data corresponding to each of the business units, and the marginal revenue data of the target disease; calculating the ratio between the disease-specific performance data corresponding to each of the business units and the marginal revenue data to generate performance allocation ratio data corresponding to each of the business units; importing the performance allocation ratio data into a preset fairness evaluation algorithm model for dispersion comparison processing to generate hospital-wide performance fairness analysis indicator data; and rendering and outputting the performance fairness analysis indicator data in the form of a graphical interface; the step of generating and outputting disease-specific operational evaluation indicators based on the operational performance revenue data... The process includes generating and outputting disease-specific operational evaluation indicators, as well as performing quadrant analysis to optimize the disease structure. Specific steps include: acquiring the actual weight data or score data corresponding to the target disease; constructing a two-dimensional data analysis matrix in system memory using the actual weight data or score data as the first dimension coordinate data and the operational performance revenue data as the second dimension coordinate data; performing quadrant mapping processing based on the two-dimensional data analysis matrix to determine the quadrant distribution interval data to which the target disease belongs under a preset coordinate system; inputting the quadrant distribution interval data into a preset structural adjustment strategy library for correlation matching, generating and outputting strategy recommendation data to indicate adjustments to the medical disease structure or revenue structure.

[0017] Beneficial effects This invention breaks down the data barriers that separate income and expenditure in traditional medical accounting systems. Through a machine-level conditional triggering mechanism, it accurately extracts the true marginal fund pool containing medical insurance profits and losses. Simultaneously, it introduces a multi-branch data fusion algorithm based on role tags, transforming complex clinical labor value into multi-dimensional integral data and a dynamic unit price model that can be efficiently processed by computers. This enables automated and refined segmentation of doctor, nurse, and medical technology performance under a bundled payment mechanism. Furthermore, by constructing a two-dimensional data analysis matrix and a rule base for calling strategies, this invention directly elevates massive amounts of micro-level accounting data into visualized structural adjustment instructions and fairness early warning indicators. While significantly reducing the computational cost of manual accounting, it provides hospitals with a highly reliable, low-latency intelligent decision-making and control system for the dynamic restructuring and lean operation of medical resources under the DRG / DIP reform framework. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for calculating the operational performance of DRGDIP disease categories according to the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but includes other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] The present invention will now be described in further detail with reference to the accompanying drawings: Example: As shown in the figure, a method for calculating the operational performance of DRGDIP includes the following steps: S100: Obtain actual medical expense data and medical insurance settlement data for the target disease, and calculate the revenue confirmation data and expense confirmation data for the target disease based on the first preset rule; S200: Based on the revenue recognition data and the expense recognition data, calculate the marginal revenue data for the target disease; S300: Obtain the performance score data of each business unit participating in the diagnosis and treatment of the target disease, and calculate the disease performance data of each business unit in combination with the preset unit price of each business unit. S400: Aggregate the disease performance data of each business unit to obtain total disease performance data, and combine it with the marginal revenue data to calculate the operational performance revenue data of the target disease; S500: Generate and output disease operation evaluation indicators based on the operational performance revenue data. The disease operation evaluation indicators are used to indicate the allocation of medical resources and the adjustment of disease structure.

[0022] Furthermore, the specific implementation process of step S100 is as follows: In a preferred embodiment of the present invention, it is specifically illustrated how computer equipment (such as an HRP operations management server or performance accounting system deployed in a hospital) can accurately extract and transform target disease revenue and expense confirmation data that conforms to the DRG / DIP payment logic based on massive amounts of underlying medical business data. In the traditional fee-for-service model, medical revenue and expenses are often directly equivalent to the patient's on-paper expenses; however, under the DRG / DIP payment system, a profit and loss mechanism for medical insurance settlement must be introduced to reconstruct the underlying data.

[0023] Specifically, regarding the generation process of revenue recognition data for the target disease, the computer equipment first retrieves the actual medical expense data for the target disease from the Hospital Information System (HIS) or the medical insurance settlement front-end machine through a preset data interface. Simultaneously, the system needs to calculate the medical insurance settlement surplus data generated by the target disease at the medical insurance end. This calculation process manifests as a set of condition-triggered data processing logic: the system first obtains the actual weight data (for DRG mode) or score data (for DIP mode) corresponding to the target disease after enrollment, and obtains the corresponding rate data or point value data published by the medical insurance bureau within the current accounting period; subsequently, the system performs a product operation, i.e.: Medical insurance settlement data = actual weighted data / score data × rate data / point value data.

[0024] After obtaining the medical insurance settlement data, the system compares it with the aforementioned actual medical expense data. When the difference is determined to be greater than zero (i.e., a medical insurance overspending surplus occurs), the system extracts this positive difference and defines it as "medical insurance settlement surplus data." Finally, the system completes the fusion and reconstruction of revenue data using the following formula: Disease-specific revenue confirmation data = actual medical expenses data + medical insurance settlement surplus data.

[0025] The technical logic behind this step is to automatically identify and capture the surplus benefits brought about by medical insurance policies through algorithms, and inject them as positive incentive data into the total revenue of the disease, breaking the data limitation of the traditional HIS system that only records the "incurrence amount".

[0026] The process of generating cost confirmation data for a target disease essentially involves computer equipment performing refined decomposition and aggregation of multi-source heterogeneous data constituting medical costs. The system first analyzes the detailed cost invoices for the target disease, separating drug cost data and consumable cost data. In processing drug cost data, the system goes beyond simple summation, introducing a policy-oriented weighted conversion algorithm. Specifically, the system fully includes the captured Western medicine cost data and traditional Chinese medicine cost data. For traditional Chinese medicine decoction piece revenue data, the system calls a preset proportion parameter (preferably configured as 80% in this embodiment) to perform dimensionality reduction conversion, using the formula: Drug cost data = Cost data of Western medicine and prepared Chinese medicine + Revenue data of Chinese herbal medicine pieces × 80% The final drug data is calculated. This parameterized conversion algorithm design allows computers to automatically perform cost separation and performance optimization for specific TCM diagnosis and treatment items.

[0027] Regarding consumable costs, the system extracts high-value consumable cost data for the target disease and linearly adds it to the general consumable cost data. Furthermore, to accurately reflect operational risks under DRG / DIP payments, the system also calculates medical insurance settlement loss data. The system again retrieves the aforementioned medical insurance settlement data and compares it with the actual medical expense data. When the difference is determined to be less than zero (i.e., medical insurance overspending occurs), the system extracts the absolute value of the difference and defines it as "medical insurance settlement loss data." Finally, the system aggregates the cost data from all the above dimensions: Disease-specific cost confirmation data = drug cost data + consumable cost data + medical insurance settlement loss data.

[0028] By capturing the aforementioned underlying data, performing conditional judgments (If-Else logic branches for surplus or loss), and conducting data transformation calculations based on specific coefficients (such as 80%), computer equipment can automatically clean and reconstruct the original medical billing records into a standardized revenue and expense dataset that conforms to DRG / DIP cost control logic. This not only eliminates the risk of omissions and miscalculations that are easily caused by manual verification, but also provides a highly reliable data input foundation for the accurate calculation of marginal benefits in subsequent steps.

[0029] Furthermore, the specific implementation process of step S200 is as follows: In a preferred embodiment of the present invention, the technical process of how a computer device (such as a hospital's performance accounting server) further extracts and constructs core operational indicators (i.e., a marginal revenue system) based on the underlying revenue and expenditure data generated in the preceding steps is described in detail. The essence of this step is that, after deducting direct costs and medical insurance penalties, the system dynamically delineates the data boundaries of a "fund pool" that can be used for performance allocation across all business units of the hospital through an algorithm.

[0030] Specifically, regarding the generation of marginal revenue data for the target disease, the computer device's processor first uses memory access instructions to selectively extract the "revenue confirmation data" and "expense confirmation data" that have been calculated and structured for the target disease in step S100 from the database. After successfully retrieving these two basic data sets, the system's computing engine triggers the difference calculation processing logic, using the revenue confirmation data as the minuend and the expense confirmation data as the subtrahend for precise floating-point subtraction, thereby generating a first difference result in the system memory representing the deduction of core medical costs. Subsequently, the system assigns and persistently stores this first difference result as the "marginal revenue data" for the target disease. The underlying execution logic formula corresponding to this data processing flow is as follows: DRG / DIP disease marginal revenue data = DRG / DIP disease revenue confirmation data - DRG / DIP disease cost confirmation data.

[0031] Through the automated differential calculations performed by the aforementioned machines, the system can quickly pinpoint the actual marginal contribution amount generated by a specific disease after eliminating direct resource consumption (such as drugs, consumables, and settlement losses caused by medical insurance overspending). This technical approach effectively overcomes the technical shortcomings of traditional methods that directly guide performance based on "hospital-wide cost allocation," such as excessively long calculation chains, data distortion, and inability to match the characteristics of DRG / DIP disease-based bundled payment.

[0032] Furthermore, in order to eliminate the incomparability of data caused by the difference in absolute income and expenditure between different types of diseases (for example, the huge difference in the volume between major surgical diseases and ordinary internal medicine conservative treatment diseases), the computer device will automatically trigger a set of data conversion and quantification operations of derivative relative indicators after calculating the absolute value of marginal income data.

[0033] The specific steps are as follows: The system extracts the marginal income data that was just generated and simultaneously retrieves the income confirmation data for the same disease. The two are then input into a preset ratio calculation module for division, thereby generating "marginal income rate data" that characterizes the relative profitability of the target disease. The corresponding calculation formula is embedded in the following logic: DRG / DIP marginal revenue rate data = DRG / DIP marginal revenue data ÷ DRG / DIP revenue confirmation data.

[0034] Here, the generated marginal income rate data is transformed by the system into a dimensionless standardized parameter. This standardized data not only objectively and swiftly reflects the input-output efficiency of the target disease under the current allocation of medical resources, but more importantly, it provides a crucial benchmark for subsequent steps (such as the discrete comparison of performance fairness in S500 and the construction of the disease quadrant matrix) for cross-disease and cross-departmental horizontal machine comparisons. Through the dual computational output of absolute values ​​(marginal income data) and relative values ​​(marginal income rate data), the system constructs a complete and multidimensional disease marginal benefit evaluation dataset.

[0035] Furthermore, the specific implementation process of step S300 is as follows: In a preferred embodiment of the present invention, a detailed explanation is provided of how a computer device can accurately and fairly quantify and map the macroscopic marginal benefits generated for a target disease to the specific business entities involved in diagnosis and treatment. In medical information systems, the diagnosis and treatment process for the same disease typically involves collaborative work among multiple roles, including doctors, nurses, and medical technicians. To enable machines to objectively assess the value of the complex labor of multiple roles, the system's underlying layer is configured with multi-branch conditional judgment logic (such as a switch-case program architecture) and multi-source data fusion algorithms to generate disease performance data for each business unit.

[0036] Specifically, the system first executes a pre-set dynamic points-based unit price generation logic. In traditional accounting software, the unit price is often a static constant entered manually. However, in this application, the computer equipment automatically retrieves the actual total performance data and the total performance points generated within a specific business unit (such as a clinical department) at set accounting cycle nodes (e.g., monthly or quarterly). The system loads these two macro-level data into the ratio calculation module, performs a division calculation, and feeds back the calculated floating-point result in real time, overwriting and updating it to the currently effective "points-based unit price data" for that specific business unit. Its underlying calculation logic is as follows: The unit price of points for a specific business unit = the total amount of actual performance paid ÷ the total performance points.

[0037] This dynamic update mechanism ensures that when the system calculates the performance of individual cases, the unit price parameter it calls can adapt to the fluctuations of the hospital's overall cash flow, demonstrating strong data robustness.

[0038] After the unit price parameters are ready, the system's main control program begins to traverse all business units involved in the diagnosis and treatment of the target disease, and triggers different data capture and fusion branches based on the attribute tags of the business units: When the system determines that the currently traversed business unit is tagged with "Doctor Business Unit," the first data processing branch is triggered. The system will call the medical behavior trajectory data for this disease across databases, extracting admission assessment points and discharge case point data representing patient severity and diagnostic difficulty, CCHI (China Classification and Coding of Medical Services) point data reflecting core operational technical barriers, surgical grading point data representing surgical risk level, and DRG / DIP quadrant point data reflecting the disease's position in the DRG / DIP profit and loss matrix. Subsequently, the system's computing engine inputs the above heterogeneous indicator data into an adder for addition and fusion processing, and the output result is the core workload metric of the doctor team: Physician business unit performance score data = admission diagnosis score data + discharge case score data + CCHI score data + surgical grading score data + DRG / DIP quadrant score data + other derived score data.

[0039] When the system determines that the currently traversed business unit has a "Nursing Business Unit" tag, it triggers the second data processing branch. Similar to the doctor branch, the system retrieves admission score data, discharge case score data, CCHI score data, surgical grade and level score data, and quadrant score data from a nursing perspective. Although there is overlap in the data dimension names, the system uses different data dictionary mapping rules at the underlying level to retrieve scores specifically converted for nursing workload (such as special care days, pressure ulcer management, etc.), and performs the same addition and fusion processing to generate nursing business unit performance score data.

[0040] When the system determines that the currently traversed business unit is tagged with "Medical Technology Business Unit" (such as radiology department or laboratory department), since its work is mainly for auxiliary examinations, the system triggers the third data processing branch. Under this branch, the system adopts a flat data extraction strategy, directly matching and extracting the preset standard medical technology performance score data for the DRG / DIP disease, as the performance score data for the medical technology business unit. This eliminates the need for splicing and merging multiple complex indicators, thus greatly saving the system's computing power.

[0041] Finally, the system extracts the performance score data generated by each of the above branches from memory one by one with their corresponding matching unit price data, and sends them to the multiplication module for product operation processing: The performance data for a specific business unit for a particular disease is calculated as follows: performance score data for that unit × unit price per score.

[0042] The system encapsulates the product results corresponding to doctors, nurses, and medical technicians in a structured manner, summarizes and constructs a complete "disease performance data set" for the target disease, and stores it in a relational database for subsequent modules to call.

[0043] Through the above-mentioned multi-branch systematic processing flow, this invention successfully transforms the mixed labor value of "doctors treating patients, nurses providing care, and medical technicians conducting examinations," which is traditionally difficult to define and prone to human disputes, into a multi-dimensional data integration model that can be accurately read, classified, integrated, and calculated by computers. This not only achieves refined performance segmentation under the DRG / DIP payment method, but also provides a reliable technical path for completely eliminating "data silos" in medical operation management.

[0044] Furthermore, the specific implementation process of step S400 is as follows: In a preferred embodiment of the present invention, the technical process of how the computer device further performs closed-loop data collection and macro-level revenue calculation after the preliminary step S300 completes the refined performance segmentation of each business unit is described in detail. The essence of this step is that the system calculates the baseline of the "net revenue" for the target disease after actually deducting all direct costs (drugs, consumables) and human labor costs (doctors, nurses, medical technology performance) through multi-dimensional data addition and subtraction operations, and simultaneously generates multi-dimensional contribution indicators to guide the allocation of medical resources.

[0045] Specifically, regarding the generation of operational performance revenue data for the target disease, the computer device's microprocessor first sends retrieval and extraction instructions to the database. From the "disease performance data set" generated by S300, it reads the disease performance data corresponding to the doctor's business unit, the nursing business unit, and the medical technology business unit, respectively. The system then sends these three sets of independent performance data into an internal accumulator for addition and fusion processing. The sum of the fused data is instantiated in system memory as the "total disease performance data" for the target disease. Its underlying processing logic is as follows: Total DRG / DIP Disease Performance Data = Doctor Business Unit Disease Performance Data + Nursing Business Unit Disease Performance Data + Medical Technology Business Unit Disease Performance Data.

[0046] After obtaining the total labor cost data, the system immediately retrieves the "marginal income data," which represents the boundary of the "funding pool" for that disease, from the cache, pre-calculated in step S200. The system's Arithmetic Logic Unit (ALU) uses the marginal income data as the minuend and the newly generated total performance data for that disease as the subtrahend, performing high-precision difference calculation. The second difference result output by the calculation is confirmed by the system as the "operational performance revenue data" for the target disease. The corresponding core algorithm formula is: DRG / DIP disease operation performance revenue data = DRG / DIP disease marginal revenue data - DRG / DIP disease performance total data.

[0047] The beneficial effects of this data processing technology are that it completely breaks down the data barrier between "revenue" and "human resources expenditure" in the traditional medical financial system, enabling computers to output the most realistic profitability model at the micro level of a single disease with extremely low latency (or even near real-time), thus avoiding the misallocation of medical resources caused by the lag in financial statements.

[0048] Furthermore, to adapt the aforementioned absolute value revenue data to the complex management decision-making scenarios of hospitals, the computer equipment automatically triggers a set of background threads for "contribution data derivation calculations" after generating operational performance revenue data. This thread aims to transform the absolute values ​​into a dimensionless, standardized data matrix that can be compared across business lines.

[0049] First, the system calculates the ratio between the operational performance revenue data and the marginal revenue data to generate "DRG / DIP disease operational performance return rate data" (the calculation formula is: disease operational performance return rate = disease operational performance revenue data ÷ disease marginal revenue data), which serves as the core benchmark for measuring the overall input-output ratio of the disease.

[0050] Secondly, for any specific business unit involved in diagnosis and treatment (taking the doctor business unit as an example), the system will extract the marginal revenue data and accurately deduct the disease performance data corresponding to the single business unit from it. The difference result will be used to generate the "direct contribution data" of the business unit to the target disease (the calculation formula is: direct contribution data of a unit = DRG / DIP disease marginal revenue data - disease performance data of the unit).

[0051] Subsequently, the system further inputs the disease performance data of the unit and the newly generated direct contribution data into the divider, and processes them in conjunction with percentage conversion logic to generate "performance direct contribution rate data" (the calculation formula is: performance direct contribution rate data of a unit = disease performance data of the unit ÷ direct contribution data of the unit × 100%). In this embodiment, the computer system not only outputs the final financial baseline data, but also automatically constructs a multi-dimensional data dashboard that includes "overall rate of return", "direct contribution of departments" and "performance leverage ratio".

[0052] Furthermore, the specific implementation process of step S500 is as follows: In a preferred embodiment of the present invention, it is illustrated how computer equipment (such as a BI data platform or decision support system deployed in a hospital) performs in-depth mining and high-dimensional feature extraction on the underlying accounting data generated in the preceding steps, thereby transforming abstract financial and performance data into machine instructions and visual evaluation indicators to drive the dynamic reorganization of medical resources throughout the hospital. The execution of this step marks the system's leap from a simple "data accounting tool" to a closed-loop "intelligent decision control system."

[0053] Specifically, the computer equipment first initiates the background data processing process for the "hospital-wide performance fairness evaluation." The system extracts in batches the disease-specific performance data and corresponding marginal revenue data for each business unit (doctor, nurse, medical technology) within the target accounting period from the underlying database. The system's arithmetic logic unit (ALU) performs high-concurrency division operations on these two indicators to accurately generate the "performance allocation ratio data" for each business unit.

[0054] Subsequently, the system did not stop at simple data display, but instead packaged the generated batch proportion data and input it into a preset "fairness evaluation algorithm model." This algorithm model is internally configured with dispersion analysis and standard deviation calculation logic, capable of automatically calculating the allocation deviation values ​​between different clinical departments or between different diseases within the same department. When the system determines that the deviation value of a certain business unit exceeds the preset normal tolerance threshold, it will trigger a system-level interruption and alarm mechanism, and push the abnormal performance fairness analysis indicator data to the front-end visualization engine for rendering and output in the form of graphical interfaces such as heatmaps or early warning radar charts. This provides hospital administrators with a precise "system-level probe," automatically detecting allocation unfairness caused by resource misallocation.

[0055] Furthermore, in order to achieve intelligent diagnosis of the hospital's entire medical business structure, the computer device dynamically constructs a "two-dimensional data analysis matrix for disease operation" in memory to execute the core quadrant mapping algorithm.

[0056] During the matrix construction process, the system's main control program extracts "actual weight data (for DRG)" or "score data (for DIP)" issued by the National Healthcare Security Administration or local healthcare security systems, which characterize the difficulty of medical technology and the standards of resource consumption, and maps them to the first dimension coordinate data (i.e., X-axis baseline data) of the two-dimensional data analysis matrix. At the same time, the system calls back the "DRG / DIP disease operation performance benefit data" (i.e., absolute net benefit of the disease) calculated in step S400 and maps it to the second dimension coordinate data (i.e., Y-axis baseline data) of the matrix.

[0057] After the coordinate system is established, the system executes the spatial coordinate positioning command, treating each DRG / DIP target disease in the hospital as a data node, and projecting and anchoring it to a specific quadrant distribution range of the two-dimensional matrix based on its corresponding (X,Y) coordinate values ​​(e.g., high weight-high benefit quadrant, high weight-low benefit quadrant, low weight-high benefit quadrant, low weight-low benefit quadrant).

[0058] After spatial mapping is completed, the system's underlying layer calls a pre-configured "structural adjustment strategy matching rule library." This rule library stores standardized intervention instructions for different quadrant characteristics. The system uses the quadrant distribution interval data of each disease as the retrieval key and performs automated matching and comparison in the rule library. For example, for disease data falling into the "high weight - low benefit" quadrant, the system will automatically match and generate strategy recommendation data such as "optimize clinical pathways and initiate a special cost control mechanism for high-value consumables"; while for disease data falling into the "high weight - high benefit" quadrant, the system will match and generate strategy recommendation data such as "expand bed configuration and increase equipment scheduling priority." These strategy data are ultimately output in the form of structured JSON messages or front-end control dashboards, directly supporting the dynamic adjustment of the hospital's disease structure and revenue structure.

[0059] Furthermore, based on the aforementioned end-to-end data loop, the system also encapsulates all detailed costs, marginal and performance deductions generated for each disease in stages S100 to S400 into multi-dimensional "disease cost accounting slice data." This slice data is stored in a distributed data warehouse, which not only meets the need for more accurate disease cost accounting but also enables managers to initiate data drill-down and source tracing analysis of any dimension at any time through the system front-end.

[0060] This embodiment addresses the technological lag in traditional hospital management, which heavily relies on manual experience for report compilation and post-event analysis. The system not only automatically performs multi-dimensional clustering and graphical matrix construction of complex data, but also provides machine-level assisted output for management actions through a built-in strategy library, significantly improving the accuracy and responsiveness of medical institution operation scheduling under the DRG / DIP payment system.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the operational performance of DRGDIP disease categories, characterized in that, Includes the following steps: S100: Obtain actual medical expense data and medical insurance settlement data for the target disease, and calculate the revenue confirmation data and expense confirmation data for the target disease based on the first preset rule; S200: Based on the revenue recognition data and the expense recognition data, calculate the marginal revenue data for the target disease; S300: Obtain the performance score data of each business unit participating in the diagnosis and treatment of the target disease, and calculate the disease performance data of each business unit in combination with the preset unit price of each business unit. S400: Aggregate the disease performance data of each business unit to obtain total disease performance data, and combine it with the marginal revenue data to calculate the operational performance revenue data of the target disease; S500: Generate and output disease operation evaluation indicators based on the operational performance revenue data. The disease operation evaluation indicators are used to indicate the allocation of medical resources and the adjustment of disease structure.

2. The method for calculating the operational performance of DRGDIP disease category according to claim 1, characterized in that, The income recognition data for the target disease calculated based on the first preset rule specifically includes: Obtain actual medical expense data and medical insurance settlement surplus data for the target disease; The actual medical expenses data and the medical insurance settlement surplus data are summed, and the summation result is determined as the revenue recognition data for the target disease. The steps for generating the medical insurance settlement surplus data include: Obtain the actual weight data or score data of the target disease, as well as the corresponding rate data or point value data; The actual weight data or score data is multiplied by the rate data or point value data to obtain medical insurance settlement data. Calculate the difference between the medical insurance settlement data and the actual medical expense data. If the difference is greater than zero, determine the difference as the medical insurance settlement surplus data.

3. The method for calculating the operational performance of DRGDIP disease categories according to claim 2, characterized in that, The calculation of the cost confirmation data for the target disease based on the first preset rule specifically includes: Obtain drug cost data, consumable cost data, and medical insurance settlement loss data for the target disease; The drug cost data, the consumable cost data, and the medical insurance settlement loss data are added and merged to obtain the cost confirmation data for the target disease; The drug cost data is obtained by summing the cost data of Western medicine and traditional Chinese medicine preparations with the revenue data of traditional Chinese medicine decoction pieces after conversion according to a preset ratio parameter, which is 80%. The consumable cost data is obtained by summing the cost data of high-value consumables with the cost data of general-charge consumables; The steps for generating the medical insurance settlement loss data include: calculating the difference between the medical insurance settlement data and the actual medical expense data; and determining the absolute value of the difference as the medical insurance settlement loss data if the difference is less than zero.

4. The method for calculating the operational performance of DRGDIP disease category according to claim 3, characterized in that, The step of calculating the marginal revenue data for the target disease based on the revenue recognition data and the expense recognition data specifically includes: extracting the revenue recognition data and the expense recognition data calculated for the target disease; The difference between the revenue recognition data and the expense recognition data is calculated to obtain the first difference result; The first difference result is determined as the marginal income data of the target disease; After calculating the marginal revenue data of the target disease, the marginal revenue data is compared with the revenue recognition data to obtain the marginal revenue rate data of the target disease. The marginal revenue rate data is used to characterize the marginal profitability level of the target disease.

5. The method for calculating the operational performance of DRGDIP disease category according to claim 4, characterized in that, The business units involved in the diagnosis and treatment of the target disease include: physician business unit, nursing business unit, and medical technology business unit; The step of calculating the disease performance data for each business unit by combining the preset point-based unit price for each business unit specifically includes: For any one of the business units, extract the performance points data corresponding to that business unit and the pre-configured points unit price data; The performance score data and the unit price data are multiplied together, and the product result is determined as the disease performance data corresponding to the business unit. The product results corresponding to the doctor business unit, the nursing business unit, and the medical technology business unit are summarized to construct a disease performance data set for each business unit.

6. The method for calculating the operational performance of DRGDIP disease category according to claim 5, characterized in that, The acquisition of performance score data for each business unit participating in the diagnosis and treatment of the target disease specifically includes: Under the condition that the business unit is determined to be a doctor business unit, the admission diagnosis score data, discharge case score data, CCHI score data, surgical grade and grading score data and DRG / DIP quadrant score data associated with the target disease are captured. The captured data of the above indicators are then added together and merged, and the fusion result is determined as the performance score data corresponding to the doctor's business unit. Under the condition that the business unit is determined to be a nursing business unit, the admission score data, discharge case score data, CCHI score data, surgical grade and grading score data, and DRG / DIP quadrant score data associated with the target disease are captured; and the above-mentioned indicator data are added together and fused, and the fusion result is determined as the performance score data corresponding to the nursing business unit. If the business unit is determined to be a medical technology business unit, the preset DRG / DIP disease performance score data is directly extracted as the performance score data corresponding to the medical technology business unit.

7. The method for calculating the operational performance of DRGDIP disease category according to claim 6, characterized in that, The pre-configured points-based unit price data is generated using the following data processing rules: Obtain the actual total performance amount and total performance points for a specific business unit within the target accounting period; The ratio of the actual total performance payout data to the total performance score data is calculated. The calculated ratio result is fed back and updated to the points unit price data corresponding to the specific business unit.

8. The method for calculating the operational performance of DRGDIP disease category according to claim 7, characterized in that, The process of aggregating the disease-specific performance data of each business unit to obtain total disease-specific performance data, and combining this with the marginal revenue data to calculate the operational performance revenue data for the target disease, specifically includes: From the generated disease performance data set, extract the disease performance data corresponding to the doctor's business unit, the disease performance data corresponding to the nursing business unit, and the disease performance data corresponding to the medical technology business unit, respectively. The disease performance data corresponding to each of the above business units are added together and merged. The sum of the merged data is determined as the total disease performance data of the target disease. Extract the marginal income data generated for the target disease, and perform difference calculation on the marginal income data and the total performance data of the disease to obtain a second difference result; The second difference result is determined as the operational performance revenue data of the target disease, and the operational performance revenue data is used to characterize the net revenue of the target disease after deducting the performance costs of each business unit.

9. A method for calculating the operational performance of DRGDIP disease categories according to claim 8, characterized in that, After calculating the operational performance revenue data for the target disease, the method further includes performing contribution data derivation calculations for specific business units and the overall disease, specifically including: The ratio of the operational performance revenue data to the marginal revenue data is calculated to obtain the operational performance return rate data for the target disease. For any of the aforementioned business units, the marginal revenue data and the corresponding disease performance data of the business unit are processed by difference calculation to generate the direct contribution data of the business unit to the target disease. The ratio of the disease performance data corresponding to the business unit to the generated direct contribution data is calculated, and combined with the percentage conversion logic, to generate the direct contribution rate data of the business unit's performance.

10. A method for calculating the operational performance of DRGDIP disease categories according to claim 8, characterized in that, The step of generating and outputting disease-specific operational evaluation indicators based on the operational performance revenue data specifically includes: Extract the disease-specific performance data corresponding to each of the aforementioned business units, as well as the marginal revenue data for the target disease; The ratio of the disease-specific performance data corresponding to each business unit to the marginal revenue data is calculated to generate the performance allocation ratio data corresponding to each business unit. The performance allocation percentage data is imported into a preset fairness evaluation algorithm model for dispersion comparison processing to generate performance fairness analysis index data across the entire college. The performance fairness analysis indicator data is rendered and output in the form of a graphical interface; The step of generating and outputting disease-specific operational evaluation indicators based on the operational performance revenue data also includes quadrant analysis for optimizing the disease structure. Specific steps include: Obtain the actual weight data or score data corresponding to the target disease; Using the actual weight data or score data as the first dimension coordinate data and the operational performance revenue data as the second dimension coordinate data, a two-dimensional data analysis matrix is ​​constructed in the system memory. Based on the two-dimensional data analysis matrix, quadrant mapping processing is performed to determine the quadrant distribution interval data of the target disease in the preset coordinate system; The quadrant distribution interval data is input into a preset structural adjustment strategy library for correlation matching, generating and outputting strategy recommendation data for indicating adjustments to the medical disease structure or income structure.

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