A medical service sub-item cost accounting method and system based on multi-dimension weight

Abstract

This invention proposes a method and system for calculating the itemized costs of medical services based on multi-dimensional weights, belonging to the field of cost accounting technology. It includes: integrating raw itemized cost data with equivalent calibration values ​​of medical service items to construct a standardized basic dataset; constructing multi-dimensional weight indicators, determining indicator weights using the entropy weight method, and training a dynamic weight calculation model to obtain initial splitting weights; constructing a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity to nonlinearly correct the initial splitting weights; based on the corrected splitting weights, allocating the total cost of each type to each service item; verifying the splitting results, and feeding back deviation information to upstream processes through a deviation backpropagation mechanism to achieve closed-loop calibration. This effectively improves the accuracy and reliability of cost accounting while reducing manual intervention costs, achieving closed-loop optimization of the accounting process.

Description

Technical Field

[0001] This invention relates to the field of cost accounting technology, and in particular to a method and system for calculating the cost of medical services based on multi-dimensional weights. Background Technology

[0002] Cost accounting for medical service items is a key step in accurately measuring service resource consumption and supporting pricing and management. It is usually based on equivalent fitting, cross-section calibration and data standardization, and then the costs of personnel, consumables, equipment depreciation, management expenses and other items are broken down and collected to finally form a reliable accounting result.

[0003] Current cost breakdown methods often use fixed proportions or single-dimensional weighting, such as allocating labor costs by number of employees or depreciation costs by equipment value. While these methods are simple to implement, they are difficult to match the complex actual consumption patterns of medical services.

[0004] The fundamental problem with this type of method is that the weight design is static and simplistic and disconnected from the basic calibration data: it only relies on superficial indicators such as the number of people and the amount to allocate costs, which cannot reflect the resource differences of different services such as surgery and outpatient services; the fixed weights are difficult to adapt to business changes and are not linked to the equivalent calibration results, resulting in a discrepancy between cost allocation and actual consumption; at the same time, a lot of manual correction is required, resulting in low consistency of results and low accounting efficiency, which seriously affects the accuracy and credibility of cost accounting. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method and system for calculating the cost of medical services based on multi-dimensional weights. By introducing multi-dimensional weights and a dynamic correction mechanism, the method achieves a refined and dynamic breakdown of the cost of medical services, effectively improving the accuracy and reliability of cost accounting while reducing the cost of manual intervention and realizing closed-loop optimization of the accounting process.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for calculating the cost of medical services based on multi-dimensional weights, including: Integrate the raw cost data of each item with the equivalent calibration values ​​of medical service items to construct a standardized basic dataset; Construct multi-dimensional weight indicators, determine indicator weights through entropy weight method, and train dynamic weight calculation model to obtain initial split weights; A three-dimensional dynamic correction factor is constructed based on cost type, resource consumption intensity, and operational complexity to perform nonlinear correction on the initial split weights; Based on the revised splitting weights, the total cost of each type is broken down into the service items; The splitting results are verified, and the deviation information is fed back to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

[0007] Secondly, the present invention provides a medical service item cost accounting system based on multi-dimensional weights, comprising: The data acquisition module is used to integrate the raw data of itemized costs with the equivalent calibration values ​​of medical service items to build a standardized basic dataset; The initial weight module is used to construct multi-dimensional weight indicators. It determines the indicator weights through the entropy weight method and trains a dynamic weight calculation model to obtain the initial split weights. The correction module is used to construct a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity, and to perform non-linear correction on the initial split weights. The cost breakdown module is used to break down the total cost of each type into various service items based on the revised breakdown weights; The calibration module is used to verify the splitting results. It feeds back the deviation information to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

[0008] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for calculating the cost of medical services based on multi-dimensional weights as described in the first aspect.

[0009] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for calculating the cost of medical services based on multi-dimensional weights as described in the first aspect.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a standardized dataset by integrating raw data with equivalent calibration values, providing a unified and reliable data foundation for cost accounting. It employs the entropy weight method to construct multi-dimensional weights and train a dynamic calculation model, changing the traditional single and fixed allocation method and making cost allocation more aligned with actual resource consumption patterns. A three-dimensional dynamic correction factor is introduced to non-linearly correct the initial weights, adapting to different cost types, operational complexities, and changes in business scenarios, significantly improving the rationality and accuracy of the accounting results. Based on the corrected weights, precise breakdown of sub-costs is achieved, and a closed-loop calibration is formed through a deviation backpropagation mechanism to continuously optimize the accounting model, effectively reducing errors and inconsistencies caused by manual adjustments. This significantly improves the efficiency and reliability of medical service cost accounting, providing scientific support for hospital cost control and pricing decisions.

[0011] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0012] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0013] Figure 1 The main flowchart of a method for calculating the cost of medical services based on multi-dimensional weights, provided in an embodiment of the present invention, is shown below. Detailed Implementation

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] To address the problems of low segmentation accuracy, poor dynamic adaptability, and disconnect from basic calibration data in existing methods for decomposing medical service costs, this invention provides a method, system, medium, and equipment for calculating medical service costs based on multi-dimensional weights.

[0016] Existing fixed-ratio allocation or single-dimensional weighting models are difficult to adapt to the complexity and dynamic changes of medical service scenarios. This results in the breakdown of core cost items such as manpower, consumables, equipment depreciation, and management expenses deviating significantly from the actual resource consumption logic, and thus failing to provide reliable data support for refined hospital management and scientific decision-making.

[0017] Given that equivalent value is a core indicator for measuring the intensity of medical service resource consumption, its disconnect from the cost breakdown process is the root cause of the aforementioned problems. This invention uses equivalent calibration results as the core anchor point, constructs a multi-dimensional weighted indicator system encompassing both quantitative and qualitative indicators, and designs a dynamic weight calculation model based on weighted summation and entropy weighting, thereby achieving accurate breakdown of individual costs.

[0018] Specifically, firstly, the original cost data and equivalent calibration data are integrated to establish a standardized basic dataset; secondly, a multi-dimensional weight index system is constructed and qualitative indicators are quantified; thirdly, a dynamic weight calculation model is trained using the entropy weight method, and a three-dimensional dynamic correction factor based on cost type and resource consumption intensity is introduced; then, the automated breakdown calculation of four types of cost items is completed based on the dynamic weights; next, a closed-loop calibration of the verification process and upstream data is achieved through a deviation backpropagation mechanism; finally, a dynamic iterative optimization mechanism for the weight model is established to ensure that the method can continuously adapt to business changes.

[0019] Through the above solution, this invention improves the accuracy and dynamic adaptability of cost breakdown, connects the entire process from basic data calibration to cost breakdown, and realizes the optimization and upgrade of medical service cost accounting from static allocation to intelligent matching.

[0020] Example 1 likeFigure 1 As shown in the figure, this embodiment discloses a method for calculating the cost of medical services based on multi-dimensional weights, including the following steps: S1: Integrate the raw data of itemized costs with the equivalent calibration values ​​of medical service items to construct a standardized basic dataset; S2: Construct multi-dimensional weight indicators, determine indicator weights through entropy weight method and train dynamic weight calculation model to obtain initial split weights; S3: Construct a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity to perform nonlinear correction on the initial split weights; S4: Based on the revised splitting weights, the total cost of each type is split into each service item; S5: Verify the splitting results and feed back the deviation information to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

[0021] Next, combined Figure 1 This embodiment provides a detailed description of a method for calculating the cost of medical services based on multi-dimensional weights.

[0022] (i) Integration of basic data on itemized costs and equivalent calibration data In the initial stage of itemized cost accounting, it is first necessary to systematically integrate the multi-source heterogeneous basic data.

[0023] As one implementation method, the data accessed mainly includes four categories of raw cost information: labor cost data, consumable cost data, equipment depreciation data, and management cost data.

[0024] Among them, the human resource cost data includes the professional title level of the staff in the department, the actual working hours (distinguishing between on-call, regular shift and overtime) and the total salary, which provides a basis for the subsequent allocation of human resource costs according to labor value; The consumable cost data is further refined to include the usage, unit price, and type of consumables (disposable or reusable) for each service item, in order to support accurate traceability of consumable costs. Equipment depreciation data includes equipment model, purchase price, usage duration, depreciation rate and corresponding service items, ensuring that the value consumption of equipment matches the intensity of use; Management cost data includes the total amount of hospital administrative office expenses, water and electricity fees, and site rental fees, which serve as the basis for allocating indirect costs.

[0025] Based on the aforementioned cost data, the equivalent values ​​of service items generated in the previous "equivalent fitting and cross-chapter calibration" process are further integrated. Using the "service item code" as the core identifier, a correlation mapping table of "service item code - equivalent value - sub-item cost data" is established to correspond the cost data one-to-one with the equivalent values ​​reflecting the intensity of resource consumption.

[0026] It should be understood that equivalent fitting and cross-chapter calibration results are obtained by statistically analyzing the resource consumption data of medical service items and by cross-departmental and cross-chapter benchmarking and calibration.

[0027] After data access and association are completed, consistency verification must be performed on the integrated dataset. The focus is on checking whether key fields such as service item codes and department codes are consistent across different data sources. This eliminates ambiguity caused by inconsistent coding or missing data, ultimately forming a standardized cost accounting dataset that provides a reliable data foundation for subsequent multi-dimensional weight calculations and accurate cost breakdown.

[0028] (II) Construction of a Multi-Dimensional Weighting Indicator System The design incorporates a multi-dimensional weighted indicator system encompassing both quantitative and qualitative metrics, adapting to the breakdown characteristics of the four types of cost items. As one implementation method, the specific indicators are shown in Table 1: Table 1. Cost Indicators; Sub-item cost type Core weight index (quantitative) Auxiliary weight index (qualitative, quantified by AHP) Labor cost Actual working hours, title coefficient, service item equivalent value Technical difficulty, operation complexity (1-5 points) Consumable cost Consumable usage, consumable unit price, service item equivalent value Consumable scarcity, consumable control level (1-3 points) Equipment depreciation cost Equipment usage time, depreciation rate, service item equivalent value Equipment precision, operation threshold (1-5 points) Management cost Department service times, department area, service item equivalent value Department management complexity (1-3 points) A quantitative indicator module written in Python converts qualitative indicators into calculable values ​​(e.g., a coefficient of 1.2 for "5 points of technical difficulty" and a coefficient of 1.0 for "3 points"), forming a standardized set of weighted indicators.

[0029] (III) Design and Training of Dynamic Weight Calculation Model A dynamic weight calculation model based on weighted summation is constructed, with the core formula as follows:

[0030] in, For the first Category of itemized costs in the first The split weight on each service item; For the first Category of itemized costs, the first The first service item Each weighted indicator value; For the first The weighting coefficients of each indicator (calculated using the entropy weighting method, automatically adapted to data characteristics). The total number of weighted indicators; This represents the total number of service items.

[0031] Specifically, using historical high-quality accounting data as the training set, inputting multi-dimensional weight index values, and calculating the initial coefficients of each index using the entropy weight method. .

[0032] It should be understood that the process of calculating the index weight coefficients using the entropy weight method is something that can be done by those skilled in the art, and will not be elaborated here.

[0033] Train a dynamic weight calculation model so that the deviation rate between the weight values ​​output by the model and the actual resource consumption is less than 1%.

[0034] Although the model has implemented multi-dimensional indicator weighting using the entropy weighting method, it only assigns weights based on historical accounting data and basic indicators, failing to consider key detailed factors affecting cost allocation. Specifically, it does not fully integrate the differences in resource consumption intensity reflected by the equivalent values, nor does it further consider the impact of the operational complexity of different service items on cost allocation. This can easily lead to a disconnect between the weighted results and the actual clinical resource allocation logic. Therefore, this embodiment introduces an equivalent value correction factor to calibrate the model's output weights. ; in, For example, labor costs correspond to "time-sensitive", equipment depreciation corresponds to "duration-sensitive", consumable costs correspond to "usage-sensitive", and management costs correspond to "comprehensive-sensitive". The resource consumption intensity index is defined as follows: This is a further development of the original equivalent value; This is the operational complexity coefficient, quantified from qualitative indicators such as technical difficulty and operational barriers. Specifically: ; in, Cost type The basic correction factor; This is the resource consumption elasticity index, representing the cost type. Sensitivity to the intensity of resource consumption (e.g., γ for equipment depreciation is close to 1, and γ for management costs is close to 0.3). This is a complexity factor, representing the operational complexity for this cost type. The amplification effect; This is a residual learning term, fitted using historical bias data, used to capture unmodeled factors. For the input cost type... Automatically match the corresponding data based on predefined relationships. The parameters are used to calculate the correction factor specific to the cost type. .

[0035] In this embodiment, a power function form is introduced. It can characterize the "marginal diminishing returns" effect of resource consumption; for example, equipment wear and tear in high-volume surgeries may exhibit superlinear growth. (Adding interactive items...) This couples the impact of complexity with the cost type; a learnable residual term is set. This enables the model to have self-correcting capabilities. Consequently, the dynamic correction factor can more realistically reflect the nonlinear characteristics and individual differences in actual resource consumption across different healthcare services.

[0036] (iv) Precise breakdown and calculation of itemized costs After completing the construction of the multi-dimensional weighted index system and the training of the dynamic weight calculation model, each medical service item is based on All of them obtained refined weighting for four categories of costs: labor, consumables, equipment depreciation, and management costs.

[0037] These weights deeply integrate quantitative and qualitative indicators such as actual working hours, professional title coefficient, consumable usage, equipment usage time, equivalent value, operational complexity, and scarcity, which can truly reflect the differences in resource consumption intensity among different service projects.

[0038] Based on this, this step links the aforementioned weights with the original data of each cost item, and uses an automated calculation module to achieve accurate breakdown of the cost items, ensuring that the accounting results are highly consistent with the actual resource consumption logic.

[0039] 1. Labor Cost Breakdown: Based on Labor Cost Weights The total human resource cost of the department Assigned to various service items, formula:

[0040] 2. Consumable Cost Breakdown: Based on Consumable Cost Weights Based on the usage of consumables, calculate the consumable costs for each service item:

[0041] in For the first The first project Consumable usage This is the unit price.

[0042] 3. Equipment depreciation cost breakdown: based on equipment depreciation weights Total equipment depreciation Assigned to various service items:

[0043] 4. Management Cost Breakdown: Based on Management Cost Weights The total cost of hospital management Assigned to various service items:

[0044] As one implementation method, a cost breakdown module written in Python is used to automate the above calculations and output the four categories of cost details for each service item.

[0045] By linking the calibrated multidimensional weights with the department's actual total cost and the amount of consumables used, the precise quantification of the four cost components of each service item was achieved, solving the problem of the disconnect between the weights and the logic of actual clinical resource consumption in the traditional allocation method.

[0046] (v) Verification of splitting results and correction of deviations First, calculate the sum of the costs of each item after the breakdown, and verify whether it is consistent with the original total cost to ensure that no data is lost or double-counted during the breakdown process. This is the basic accuracy requirement for cost accounting.

[0047] However, the verification process in traditional solutions usually stops there. Once a deviation is detected, manual intervention is required for adjustment, which is a typical "post-mortem correction" model that cannot trace and correct the root cause of the deviation.

[0048] Given the dynamic and complex nature of healthcare service cost data, deviations often originate from upstream processes, such as errors in raw data collection, biases in weighting indicator settings, or insufficient timeliness of equivalent calibration values. Simply focusing on result-level verification cannot prevent the continued transmission of these deviations.

[0049] Therefore, this embodiment introduces a deviation backpropagation mechanism, which maps the deviation information identified in the verification process back to upstream processes such as original data, weighting coefficients, and preliminary cost calculations according to their source. Through a collaborative correction matrix, targeted compensation and parameter optimization of the deviation are achieved, upgrading the verification process from a passive "result check" to an active "closed-loop calibration," truly opening up the feedback loop of the entire cost accounting process. Specifically: A bias backpropagation mechanism is introduced, and a collaborative correction matrix M is designed to back-allocate the bias in each round of accounting to three levels according to its source: original data, indicator weights, and preliminary costs.

[0050] in, This indicates the amount of correction to the original data. For example, if the working hours data of a certain department are abnormally high, its weight will be automatically reduced in the next round. This represents the amount of correction to the weighting coefficients, such as adding bias feedback to the α output by the entropy weighting method. This represents a direct correction to the initial cost, such as making local adjustments for projects with significant deviations. M is a 3×n linear transformation matrix, where n depends on the number of deviation types. The coefficients of M are trained using the correspondence between historical deviations and correction effects, essentially establishing a mapping table from deviations to correction strategies.

[0051] As one implementation method, if in round t it is found that the depreciation cost of "orthopedic surgery" equipment is generally 5% lower than expected, the system will perform a backtracking check: Is it because the equipment usage time records in the original data are incomplete? Or is the score of "equipment precision" in the weighted index too low? Or does the equivalent value itself need to be adjusted? Based on the mapping relationship of the M matrix, the system will automatically generate the correction instruction for the next round: "equipment usage time data × 1.05, precision coefficient + 0.2, equivalent value temporarily unchanged"; in round t+1, the system will recalculate using the corrected data, and the deviation will be reduced.

[0052] This embodiment introduces a backpropagation mechanism for deviations, enabling multi-source correction through matrix operations alone, without the need for complex models. This effectively lowers the barrier to algorithm deployment and maintenance. The correction strategy does not rely on manually preset static rules, but rather automatically learns mapping relationships from historical deviation data. This allows the system to identify the inherent correlation between different deviation types and correction actions, avoiding the subjectivity and inconsistency of human intervention.

[0053] As deviation data from each round of cost accounting is backpropagated to the original data, weight parameters, and preliminary calculation stages, the system continuously optimizes the input quality at each level, forming a self-iteratory closed loop of "accounting—verification—correction—optimization." This mechanism not only improves the accuracy and stability of individual cost breakdowns but also enables the overall accounting system to continuously evolve, dynamically adapting to business changes and data feature migrations, truly achieving a leap from "static execution" to "intelligent evolution" in cost accounting.

[0054] (vi) Dynamic Iterative Optimization of the Weight Model To ensure that the dynamic weight model can continuously adapt to changes in medical service operations and the evolution of cost accounting needs, this embodiment establishes a systematic dynamic iterative optimization mechanism for the weight model.

[0055] Given the significant dynamic characteristics of hospital operations, such as the introduction of new diagnostic and treatment technologies, equipment upgrades, personnel restructuring, and the increase or decrease of medical service items, if the weighting model remains fixed for a long period of time, it will gradually deviate from the actual logic of resource consumption.

[0056] Therefore, this embodiment takes periodic updates as its core, with a monthly or quarterly cycle, automatically accessing the latest cost accounting raw data and the equivalent values ​​of service items after equivalent calibration, as the input basis for model iteration.

[0057] Based on this, the entropy weight method is used to objectively re-weight each indicator in the weighted indicator system, and the updated indicator weight coefficients are calculated. Because the entropy weight method can dynamically adjust the weights of indicators based on the dispersion of the data itself, it can automatically capture the impact of business changes on cost drivers without manual intervention, ensuring the timeliness and data adaptability of the weight system.

[0058] When new medical service items or new cost types are added, such as new consumable categories or new equipment types, it is only necessary to add corresponding quantitative or qualitative indicators to the established weight indicator system, such as the usage of new consumables or the precision score of new equipment. The model can then automatically generate new split weights based on the updated indicator set without reconstructing the overall algorithm framework.

[0059] This results in a weighting model with excellent scalability and low maintenance costs, enabling it to flexibly respond to the needs of medical institutions as their business expands and management deepens. Through the aforementioned iterative mechanism, the dynamic weighting model not only achieves self-evolution and continuous optimization, but also forms a complete closed-loop linkage with previous data integration, weight calculation, cost breakdown, and deviation correction, ensuring that the entire cost accounting system maintains high accuracy and adaptability throughout long-term operation, providing sustainable technical support for the refined management of medical institutions.

[0060] This specific embodiment integrates itemized cost data with equivalent calibration values ​​of medical service items to construct a standardized dataset, providing a reliable foundation for cost accounting. It employs an entropy weighting method to establish a multi-dimensional dynamic weighting model, replacing the traditional fixed-ratio allocation method. Combined with a three-dimensional dynamic correction factor, the initial weights are non-linearly corrected, making cost allocation more closely aligned with actual resource consumption. By correcting the weights, various costs are accurately broken down, and a deviation backpropagation mechanism is used to form a closed-loop calibration, effectively improving accounting accuracy and consistency, reducing manual intervention, and significantly increasing the efficiency and reliability of medical service cost accounting, providing strong support for refined hospital management.

[0061] Example 2 This embodiment provides a medical service item cost accounting system based on multi-dimensional weights, including: The data acquisition module is used to integrate the raw data of itemized costs with the equivalent calibration values ​​of medical service items to build a standardized basic dataset; The initial weight module is used to construct multi-dimensional weight indicators. It determines the indicator weights through the entropy weight method and trains a dynamic weight calculation model to obtain the initial split weights. The correction module is used to construct a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity, and to perform non-linear correction on the initial split weights. The cost breakdown module is used to break down the total cost of each type into various service items based on the revised breakdown weights; The calibration module is used to verify the splitting results. It feeds back the deviation information to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

[0062] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the multi-dimensional weight-based cost accounting method for medical services as described in Embodiment 1 above.

[0063] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-dimensional weight-based cost accounting method for medical services as described in Embodiment 1 above.

[0064] The steps or modules involved in Embodiments 2 to 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the cost of medical services based on multi-dimensional weights, characterized in that, include: Integrate the raw cost data of each item with the equivalent calibration values ​​of medical service items to construct a standardized basic dataset; Construct multi-dimensional weight indicators, determine indicator weights through entropy weight method, and train dynamic weight calculation model to obtain initial split weights; A three-dimensional dynamic correction factor is constructed based on cost type, resource consumption intensity, and operational complexity to perform nonlinear correction on the initial split weights; Based on the revised splitting weights, the total cost of each type is broken down into the service items; The splitting results are verified, and the deviation information is fed back to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

2. The method for calculating the cost of medical services based on multi-dimensional weights as described in claim 1, characterized in that, The integration of raw cost data and equivalent calibration values ​​for medical service items to construct a standardized basic dataset specifically includes: Obtain raw data on labor costs, consumable costs, equipment depreciation costs, and management costs, and obtain the equivalent value of each service item; A mapping table is established using service item codes as identifiers, and the consistency of the integrated dataset is verified to form a standardized cost accounting base dataset.

3. The method for calculating the cost of medical services based on multi-dimensional weights as described in claim 1, characterized in that, The multi-dimensional weighting indicators are specifically as follows: The weighting indicators for human resource costs include actual working hours, professional title coefficient, service item equivalent value, as well as technical difficulty and operational complexity; The cost weighting indicators for consumables include the usage of consumables, the unit price of consumables, the equivalent value of service items, the scarcity of consumables, and the level of consumable control. The weighting indicators for equipment depreciation costs include equipment usage time, depreciation rate, service item equivalent value, equipment precision, and operational threshold. The management cost weighting indicators include the number of people served by the department, the area of ​​the department, the equivalent value of the service items, and the complexity of department management.

4. The method for calculating the cost of medical services based on multi-dimensional weights as described in claim 1, characterized in that, The process of determining index weights using the entropy weight method and training a dynamic weight calculation model to obtain initial splitting weights specifically includes: Using historical accounting data as the training set, input multi-dimensional weight index values, and calculate the weight coefficient of each index using the entropy weight method; A dynamic weight calculation model is constructed based on weighted summation to obtain the initial split weights of each service item; Train the model until the deviation rate between the weight values ​​it generates and the actual resource consumption is lower than a preset threshold.

5. The method for calculating the cost of medical services based on multi-dimensional weights as described in claim 1, characterized in that, The process of constructing a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity to nonlinearly correct the initial splitting weights specifically includes: We construct a cost type feature vector to distinguish the resource consumption sensitivity patterns of different cost items, construct a resource consumption intensity vector to deepen the expression of equivalent values, and construct an operation complexity vector to quantify the technical difficulty of service projects. Substitute the three vectors into the nonlinear correction function to generate a dynamic correction factor, which is then multiplied by the initial split weights to perform a nonlinear correction on the initial split weights.

6. The method for calculating the cost of medical services based on multi-dimensional weights as described in claim 1, characterized in that, The verification of the splitting results, through a backpropagation mechanism to feed back deviation information to the upstream process, achieves closed-loop calibration, specifically including: Calculate the deviation between the sum of the costs of each item after breakdown and the original total cost; Construct a collaborative correction matrix to back-map deviation information to the original data, weighting coefficients, and preliminary cost calculation stages according to their sources; By training the mapping relationship of the correction matrix using historical deviation data, a targeted compensation strategy for each upstream link is generated, enabling the next round of accounting to be re-executed based on the corrected input data, thus forming a self-iteratory closed-loop calibration mechanism.

7. A medical service item cost accounting system based on multi-dimensional weights, characterized in that, include: The data acquisition module is used to integrate the raw data of itemized costs with the equivalent calibration values ​​of medical service items to build a standardized basic dataset; The initial weight module is used to construct multi-dimensional weight indicators. It determines the indicator weights through the entropy weight method and trains a dynamic weight calculation model to obtain the initial split weights. The correction module is used to construct a three-dimensional dynamic correction factor based on cost type, resource consumption intensity, and operational complexity, and to perform non-linear correction on the initial split weights. The cost breakdown module is used to break down the total cost of each type into various service items based on the revised breakdown weights; The calibration module is used to verify the splitting results. It feeds back the deviation information to the upstream process through the deviation backpropagation mechanism to achieve closed-loop calibration.

8. The medical service item cost accounting system based on multi-dimensional weights as described in claim 7, characterized in that, The integration of raw cost data and equivalent calibration values ​​for medical service items to construct a standardized basic dataset specifically includes: Obtain raw data on labor costs, consumable costs, equipment depreciation costs, and management costs, and obtain equivalent values ​​for each service item generated after equivalent fitting and cross-chapter calibration; A mapping table is established using service item codes as the core identifier, and the consistency of the integrated dataset is verified to form a standardized cost accounting basic dataset.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for calculating the cost of medical services based on multi-dimensional weights as described in any one of claims 1-6.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for calculating the cost of medical services based on multi-dimensional weights as described in any one of claims 1-6.

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