A method and device for determining the cost of surgery, electronic equipment and storage medium

Abstract

The embodiment of the application discloses a kind of methods for determining the cost of surgery, device, electronic equipment and storage medium, the method includes: first, determine the data set to be processed, the data set includes multiple data, each data includes surgery identification, and at least one equipment equipment identification, start using time, end using time, part or all of actual running time length;Second, for each data, the cost information corresponding to the data is calculated by applying the pre-set cost calculation method, the cost information corresponding to the data is used to indicate the cost corresponding to the surgery represented by the data;Finally, according to the cost information corresponding to multiple data respectively, and, pre-set corresponding relationship, determine the cost of each department, the pre-set corresponding relationship includes the first corresponding relationship of surgery and department, or the second corresponding relationship of equipment and department.The method of the embodiment of the application can improve the degree of refinement and accuracy of the calculated cost of surgery.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for determining surgical costs. Background Technology

[0002] With the development of technology, hospitals have increasingly higher demands for refined management, and accurate calculation of equipment and department costs has become a core challenge for hospital operation and management.

[0003] In related technologies, the cost of surgery is usually calculated based on a single driving factor such as the duration of the operation. However, the surgical process may involve multiple devices. Therefore, this cost calculation method cannot reflect the actual cost burden that the devices impose on the department during the specific surgical process.

[0004] Therefore, there is an urgent need for a more refined and accurate method of cost calculation. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for determining surgical costs, thereby improving the precision and accuracy of the calculated surgical costs.

[0006] In a first aspect, one embodiment of this application provides a method for determining surgical costs, including: Determine the dataset to be processed; wherein the dataset to be processed includes multiple data entries, each data entry including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the following: actual runtime. For each piece of data, a pre-defined cost calculation method is applied to calculate the corresponding cost information; the cost information corresponding to the data is used to indicate the cost of the surgery represented by the data. The cost of each department is determined based on the cost information corresponding to each of the multiple data points, as well as the pre-defined correspondences. The pre-defined correspondences include a first correspondence between surgery and department, or a second correspondence between equipment and department.

[0007] Secondly, one embodiment of this application provides an apparatus for determining surgical costs, comprising: The data acquisition unit is used to: determine the dataset to be processed; wherein the dataset to be processed includes multiple data entries, each data entry including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the actual runtime. The data processing unit is used to: calculate the cost information corresponding to each piece of data using a pre-set cost calculation method; wherein, the cost information corresponding to the data is used to indicate the cost of the surgery represented by the data; The data processing unit is also used to: determine the cost of each department based on the cost information corresponding to each of the multiple data points, and the pre-set correspondence; wherein the pre-set correspondence includes a first correspondence between surgery and department, or a second correspondence between equipment and department.

[0008] Thirdly, one embodiment of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0009] Fourthly, one embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of any of the above methods.

[0010] Fifthly, one embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above methods.

[0011] In this embodiment, firstly, a dataset to be processed is determined, which includes multiple data entries. Each data entry includes a surgical identifier and, in part, all of the following: device identifier, start time, end time, and actual runtime of at least one device. Secondly, for each data entry, a pre-defined cost calculation method is applied to calculate the corresponding cost information. This cost information indicates the cost of the surgery represented by the data. Finally, based on the cost information corresponding to each of the multiple data entries and a pre-defined correspondence, the cost for each department is determined. This pre-defined correspondence includes a first correspondence between surgery and department, or a second correspondence between device and department. This process considers device operation data, improving the real-time performance and accuracy of cost calculations. Finally, the cost for each department is calculated, achieving refined management of surgical costs. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating an automatic cost accounting method provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for determining surgical costs, provided as an embodiment of this application; Figure 3A flowchart illustrating the establishment of an association relationship is provided in one embodiment of this application; Figure 4 A schematic diagram illustrating cost calculation and verification according to an embodiment of this application; Figure 5 A schematic diagram illustrating precise cost aggregation as provided in an embodiment of this application; Figure 6 A schematic diagram illustrating a multi-dimensional cost analysis according to an embodiment of this application; Figure 7 A schematic diagram of a device for determining surgical costs according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0015] The number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0016] Currently, under the background of refined hospital management, cost accounting for high-cost, high-consumption departments such as operating rooms, especially the accurate collection and allocation of costs for equipment departments (such as anesthesiology, operating room nursing units, and equipment support departments), has become a core challenge for hospital operation and management.

[0017] With the deepening of refined hospital operations and medical insurance payment reforms (such as payment by Diagnosis Related Groups (DRG) or payment by Diagnosis-Intervention Packet (DIP)), the accurate cost accounting of equipment departments in the operating room, as the core unit of hospital resource consumption, has become a key factor in management decisions.

[0018] However, the cost calculation methods in related technologies include the following: Method 1: The traditional full cost allocation model relies on a single driving factor (such as operation time), resulting in a crude and distorted cost allocation. Method 2: Activity-based costing. This method has an advanced concept, but the surgical procedure is complex and data collection is difficult, making it hard to implement. Method 3: The single-device accounting method. This method is detached from clinical scenarios and cannot reflect the true cost burden that the equipment brings to the department in specific surgeries. Method 4: Traditional accounting subject method. This method is disconnected from clinical operations and is difficult to support cost analysis at the disease or surgical level.

[0019] The aforementioned methods generally suffer from core flaws such as crude identification of cost drivers, disconnect between equipment and clinical operations, insufficient integration of multi-source data, and neglect of indirect collaborative costs. These flaws prevent hospitals from accurately measuring the equipment-related departmental costs associated with each surgery, resulting in a lack of reliable data for departmental performance evaluation, resource allocation optimization, and surgical pricing strategies. Therefore, developing a cost driver accounting method that can connect the "equipment-department-surgery" business chain and achieve refined cost driver analysis and automated data processing has become a crucial technical challenge urgently needing breakthroughs in hospital management.

[0020] To address this, this application provides an automated cost accounting method for hospital operating room equipment based on data linkage, aiming to solve the technical shortcomings of existing cost accounting methods, such as the disconnect between equipment usage and specific surgeries, reliance on manual data statistics, and inaccurate cost allocation. This method automatically links specific surgical procedure information in the surgical anesthesia system with real-time location data, on / off status, and runtime data of medical equipment (hereinafter referred to as equipment), constructing a precise dynamic mapping relationship between "surgery-equipment-department." Based on this association, the system can automatically collect the actual equipment depreciation cost, energy consumption cost, and maintenance cost consumed in each surgery, and accurately allocate the total cost to the corresponding department responsible for equipment management and maintenance (such as the anesthesiology department, operating room nursing unit, equipment support department, etc.) according to preset rules. This method achieves a leap in accounting granularity from "overall department cost" to "surgery-driven cost," providing reliable data support for hospitals to conduct refined cost management, departmental performance evaluation, and resource allocation optimization based on disease or surgery. Figure 1 This is an overall flowchart of an automatic cost accounting method provided in an embodiment of this application.

[0021] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-inventive methods. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application.

[0022] The technical solutions provided in the embodiments of this application will be described below.

[0023] refer to Figure 2 This application provides a method for determining surgical costs, comprising the following steps: S201: Determine the dataset to be processed.

[0024] S202: For each piece of data, calculate the corresponding cost information using a pre-defined cost calculation method.

[0025] S203: Determine the cost of each department based on the cost information corresponding to each of the multiple data points, as well as the pre-defined correspondence.

[0026] In this embodiment, firstly, a dataset to be processed is determined, which includes multiple data entries. Each data entry includes a surgical identifier and, in part, all of the following: device identifier, start time, end time, and actual runtime of at least one device. Secondly, for each data entry, a pre-defined cost calculation method is applied to calculate the corresponding cost information. This cost information indicates the cost of the surgery represented by the data. Finally, based on the cost information corresponding to each of the multiple data entries and a pre-defined correspondence, the cost for each department is determined. This pre-defined correspondence includes a first correspondence between surgery and department, or a second correspondence between device and department. This process considers device-related data, improving the real-time performance and accuracy of cost calculations. Finally, the cost for each department is calculated, achieving refined management of surgical costs.

[0027] Regarding S201, the dataset to be processed includes multiple data entries, each including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the actual runtime.

[0028] Optionally, the process of determining the dataset to be processed can be achieved through steps A1-A3: A1: Obtain multiple surgical procedure data and multiple equipment operation data.

[0029] Optionally, each data entry includes a surgery identifier, operating room identifier, surgery start duration, and surgery end duration; each piece of equipment operation data includes an equipment identifier, equipment start time, equipment end time, equipment runtime, and equipment operating mode status data.

[0030] In this embodiment, a parallel acquisition channel for diverse heterogeneous data is established to achieve synchronous acquisition of surgical process data and equipment operation data. Specifically, on one hand, surgical process data (also known as structured scheduling data) from the surgical anesthesia system is acquired through a standard interface of the hospital information system, including surgical identifiers; on the other hand, operational data (also known as real-time operational status data) of equipment in each operating room is collected, including equipment identifiers, power-on / off timestamps, and operating mode status data.

[0031] It should be noted that the surgical process data and equipment operation data obtained in the embodiments of this application may be within a set time period (e.g., within one month). This is only an example and does not constitute a specific limitation.

[0032] In addition, to ensure data quality and consistency, the system performs standard preprocessing on the collected raw data (multiple surgical procedure data and multiple equipment operation data). First, all timestamps are uniformly calibrated using the Network Time Protocol to eliminate time base differences between multiple systems; second, raw data in different formats are converted into a unified standardized data object model; finally, data integrity verification and outlier detection are performed to remove invalid records and correct obviously erroneous data, providing reliable input for subsequent correlation analysis.

[0033] This application embodiment automatically collects equipment power-on / off, running time, and energy consumption data through Internet of Things (IoT) technology, avoiding errors from manual recording and achieving precise cost measurement in minutes. This provides a highly reliable data foundation for hospital operational decisions and significantly improves the real-time performance and accuracy of cost data.

[0034] A2: Based on pre-defined matching rules, establish the correlation between multiple surgical procedure data and multiple equipment operation data.

[0035] Optionally, the system's core component, the spatiotemporal matching engine, implements this process based on a hybrid matching strategy of rules and algorithms. The pre-defined matching rules include temporal matching rules and spatial matching rules. Optionally, this process can be implemented through steps A21-A22: A21: For any surgery, if the first device is located in the operating room corresponding to the surgery in the first time period, and the running time of the first device is within the range of the first time period, then the first device is determined to be a device associated with the surgery.

[0036] The first time period is the time period consisting of the operation duration of the surgery. For any surgery, if the first device is located in the operating room corresponding to the surgery during the first time period, it indicates that the first device is associated with the surgery from a spatial perspective. In addition, if the operating time of the first device is within the range of the first time period, it indicates that the first device is operating within the range of the operation duration, which indicates that the first device is associated with the surgery from a temporal perspective.

[0037] Therefore, in terms of spatial matching, the engine establishes a precise mapping between operating room numbers and the physical locations of equipment, ensuring that only equipment operation records located within the same physical space are included in the candidate matching set. In terms of temporal matching, the engine constructs a dynamic time window model. This model uses the actual surgical time as the base window and intelligently expands it with reasonable buffer periods for preoperative preparation and postoperative cleanup, forming a complete equipment matching time interval.

[0038] In practical applications, there are also situations where a device is used for multiple surgeries simultaneously. In this case, the surgery associated with the device is determined based on the device location description information, which includes the device's movement trajectory or operation log information.

[0039] Specifically, during the matching process, the engine uses a time-series alignment algorithm to accurately calculate the overlap between the device's runtime and the surgery matching time interval. For matching conflict scenarios (such as the same device being used in multiple surgeries), the system employs a multi-level resolution mechanism. It prioritizes determining the actual user affiliation based on the device's movement trajectory; if no trajectory data is available, it references user records in the device's operation log; ultimately, conflicts that cannot be automatically resolved will generate tasks awaiting manual review to ensure the accuracy of the matching results.

[0040] This process identifies a device associated with any given surgery. In practice, a surgery can also be associated with other devices. The same method can be used to identify devices associated with other surgeries, which will not be elaborated upon here.

[0041] In this embodiment, surgical process data and equipment operation data are automatically associated based on spatiotemporal matching rules, thus solving the problem of automatic matching between surgical anesthesia system and equipment operation data.

[0042] A22: Based on the equipment associated with each surgery, establish the correlation between multiple surgical process data and multiple equipment operation data.

[0043] Figure 3 A flowchart illustrating the establishment of an association relationship provided in this application embodiment is shown below. Figure 3 Through the above steps, the associated equipment for each surgery can be identified. Each surgery corresponds to one surgical procedure data point, and each device corresponds to one device operation data point. Therefore, the relationship between multiple surgical procedure data points and multiple device operation data points can be established.

[0044] A3: Generate the dataset to be processed based on the relationship.

[0045] Optionally, data fusion can be performed based on surgical procedure data and equipment operation data indicated by correlation relationships to obtain a dataset to be processed. Each data point in the dataset precisely represents the specific usage of a single piece of equipment during a single surgery.

[0046] The dataset to be processed can exist in the form of a data table. This data table is designed to support efficient query and traceability operations, meeting both the data requirements for real-time cost calculation and the auditing of surgical equipment usage at any historical time. The system establishes a related version management mechanism to record the impact of each update to the related rules on historical data, ensuring the consistency of cost traceability. This related dataset serves as the data foundation for the entire cost accounting method, realizing a precise correspondence between the surgical clinical process and the physical consumption of equipment.

[0047] In the case of S202, the dataset to be processed includes multiple data points, each of which can also be called a data record. For each data point, a pre-defined cost calculation method is applied to calculate the cost information corresponding to the data. The cost information corresponding to the data is used to indicate the cost of the surgery represented by the data.

[0048] Optionally, the parameters in the pre-set cost calculation method include depreciation parameters, energy consumption parameters, and maintenance strategy parameters; among them, the depreciation parameters are determined based on equipment asset information and actual operating time; the energy consumption parameters are determined based on equipment physical characteristic parameters and time-of-use electricity pricing model; and the maintenance strategy parameters are determined based on the cumulative operating time of the equipment.

[0049] In a specific example, equipment asset information is stored in the equipment basic information layer, including key parameters such as original purchase value, commissioning date, financial depreciation period, and expected total working life; equipment physical characteristic parameters are recorded in the technical specifications layer, including rated power, typical operating voltage range, energy efficiency rating, and power curves under different operating modes; maintenance strategy parameters are defined by the maintenance standards layer, including preventive maintenance cycle thresholds, calibration and testing frequency, baseline value for single maintenance cost, and consumable replacement trigger conditions.

[0050] The structured parameter database containing the above parameters supports multi-version parameter management, allowing hospitals to adjust depreciation methods (straight-line method, units-of-production method, or accelerated depreciation method) according to financial management policies, or dynamically update maintenance cycles based on actual equipment usage intensity. All parameter changes are recorded with a complete audit trail, ensuring the traceability of the cost calculation process. The parameter database interfaces with the hospital's asset management system through a standardized equipment classification and coding system, achieving consistent maintenance of equipment master data.

[0051] Specifically, the cost calculation engine adopts a modular architecture design, with depreciation calculation module, energy consumption calculation module, maintenance calculation module, and consumables calculation module working collaboratively. The depreciation calculation module automatically selects a preset depreciation allocation algorithm based on equipment asset parameters and actual operating time. Among them, the straight-line depreciation method allocates the periodic depreciation amount according to the operating time, while the workload method calculates depreciation based on the cumulative usage ratio of the equipment. The energy consumption calculation module accurately calculates the electricity consumption cost based on the equipment power parameters and the time-of-use electricity pricing model. The maintenance calculation module monitors the cumulative operating time of the equipment, intelligently triggers maintenance events, and allocates maintenance costs according to preset rules.

[0052] In this way, each computing module uses the aforementioned dataset to be processed (surgical equipment associated dataset) as a unified input and performs calculations one by one using a streaming processing mode. The system establishes a computing task queue mechanism to ensure computing efficiency and data consistency in high-concurrency scenarios. The output of each computing module contains complete computing path information, recording in detail the parameter values ​​used, calculation formulas, and intermediate results, forming a verifiable chain of cost calculation evidence. Modules interact through standardized data interfaces, supporting flexible replacement and expansion of computing rules.

[0053] In practical applications, it can also generate and verify detailed cost data. Figure 4 This is a schematic diagram illustrating a cost calculation and verification method provided in an embodiment of this application.

[0054] See Figure 4 Specifically, the outputs of each calculation module are aggregated and processed to generate detailed cost data with a single operation and a single piece of equipment as the smallest accounting unit. Each detailed data entry includes a surgery identifier, equipment identifier, the amount of each cost item (depreciation, energy consumption, maintenance, etc.), a cost calculation timestamp, and a calculation version number. The system automatically performs data validity checks, including checking the range of amounts, verifying the consistency between the total and the sum of the items, and detecting abnormal fluctuations.

[0055] Subsequently, the verified cost details are permanently stored in the cost fact table, and corresponding cost allocation vouchers are generated simultaneously. The system provides a cost recalculation mechanism; when parameters are adjusted or relationships are corrected, batch recalculation can be performed on data within a specific time range to ensure the timeliness and accuracy of cost data. The cost details data are accessible to various hospital management systems through a standardized Application Programming Interface (API), supporting multiple application scenarios such as financial accounting, departmental performance evaluation, and disease-specific cost analysis.

[0056] Therefore, this application proposes an automatic calculation method for equipment usage costs based on a multi-dimensional parameter model, which realizes the automatic conversion from equipment physical usage time to accurate cost amount.

[0057] Regarding S203, after obtaining the cost information corresponding to each of the multiple data points, the cost of each department can be determined based on the cost information corresponding to each of the multiple data points and the pre-set correspondence.

[0058] This process mainly includes the following two implementation methods: In the first implementation method, a pre-defined correspondence is established between equipment and departments. In this method, the cost of the department can be calculated through steps B1-B3.

[0059] B1: For each department, determine the equipment identifier of at least one piece of equipment corresponding to the department based on the pre-set second correspondence between equipment and department.

[0060] For example, if the current department is Department 1, then according to the pre-set first correspondence between equipment and department, the equipment identifiers of the 5 equipment corresponding to Department 1 can be determined as Equipment 1, Equipment 2, Equipment 3, Equipment 4 and Equipment 5.

[0061] B2: The device identifier of at least one device, and the cost information corresponding to each of the multiple data points, to determine the cost corresponding to at least one device.

[0062] Optionally, the sum of the costs of these 5 devices can be used as the cost of Department 1.

[0063] B3: The sum of the costs of all the equipment is used as the department's cost.

[0064] Optionally, the sum of the costs of these 5 devices can be used as the cost of Department 1.

[0065] The second implementation method pre-sets the correspondence between surgeries and departments. In this method, the cost of the department can be calculated through steps C1-C3.

[0066] C1: For each department, determine the surgical identifier of at least one surgery corresponding to the department based on the pre-set first correspondence between surgeries and departments.

[0067] For example, if the current department is Department 1, then according to the pre-set first correspondence between the surgery and the department, the surgery identifiers of the 5 surgeries corresponding to Department 1 can be determined as Surgery 1, Surgery 2, Surgery 3, Surgery 4 and Surgery 5.

[0068] C2: The surgical identifier of at least one surgery, and the cost information corresponding to each of the multiple data points, to determine the cost corresponding to at least one surgery.

[0069] Specifically, among the cost information corresponding to each of the multiple data points, find the costs corresponding to surgery 1, surgery 2, surgery 3, surgery 4, and surgery 5 respectively.

[0070] C3: The sum of the costs of each surgery is used as the department's cost.

[0071] Optionally, the sum of the costs of these 5 surgeries can be used as the cost of Department 1.

[0072] This application's embodiments achieve a fundamental shift in operating room equipment cost accounting from "extensive allocation across the entire department" to "precise aggregation for each individual surgery." By automating the linking of surgical and equipment operation data, it completely resolves the pain points of traditional methods, such as ambiguous cost attribution and reliance on manual estimation. Furthermore, it enables the construction of a full-link cost traceability system from "equipment to department to surgery," allowing managers to clearly understand the true cost burden each surgery places on specific equipment departments, providing refined quantitative evidence for departmental performance evaluation and resource allocation.

[0073] Figure 5 A schematic diagram illustrating precise cost aggregation provided in this application embodiment is shown below. Figure 5 The following example illustrates the process of accurate cost aggregation based on management mapping relationships: First, the process of establishing a management responsibility mapping system.

[0074] Specifically, to achieve accurate cost allocation to management entities, this application's embodiments construct a multi-layered management responsibility mapping system. The core mapping layer establishes the correspondence between equipment identifiers and management department codes, clarifying the administrative department responsible for the management of each medical device; the extended mapping layer supports the responsibility correspondence between equipment and cost centers, including scenarios such as multi-departmental allocation of shared equipment and dynamic ownership of mobile devices; the auxiliary mapping layer records the business relationship between the equipment-using department and the patient's billing department, supporting cost traceability to the final payer.

[0075] The mapping system employs version control, recording the history of changes in equipment management responsibilities due to each organizational restructuring. The system provides a visual mapping configuration tool, supporting a combination of batch import and manual maintenance for mapping management. For newly introduced equipment, the system supports automatic allocation based on default mapping rules according to equipment categories, reducing manual configuration workload. All mapping relationship changes are controlled through an approval workflow, ensuring the seriousness and accuracy of cost aggregation rules.

[0076] Secondly, the process of automating cost collection and allocation.

[0077] Based on the management responsibility mapping system, the cost aggregation engine automatically allocates detailed cost data to the corresponding management responsibility units. For directly attributable costs, the engine locates the unique responsible department based on the equipment identifier and aggregates the full cost to that department's cost account. For indirect costs that need to be allocated, the engine calls preset allocation algorithms (such as allocation based on the proportion of operation time, allocation based on the number of uses, allocation based on a fixed proportion, etc.) to split the cost into multiple benefiting departments.

[0078] The cost aggregation process employs a transaction processing mechanism to ensure the atomicity and consistency of the aggregation operation for each cost detail. The system updates the cumulative cost values ​​of each responsible department in real time and generates a cost aggregation log, which records in detail the source, destination, aggregation time, and execution rules for each cost. For any unmapped devices or mapping conflicts discovered during the aggregation process, the system automatically generates an exception task and notifies management personnel for handling, preventing the omission of cost data.

[0079] Secondly, the process of generating multi-dimensional management analysis reports.

[0080] Based on comprehensive cost aggregation results, the system automatically generates analytical reports that meet different management needs. Departmental cost reports display the total cost, composition, and trend changes of each department within a specified period, supporting year-on-year and month-on-month analysis. Disease-specific cost analysis reports, based on surgical diagnosis-related groups, statistically analyze the equipment resource consumption characteristics of surgeries for each disease, providing data support for clinical pathway optimization. Physician performance cost reports link equipment costs to the surgeon performing the surgery, reflecting the resource utilization efficiency of different physicians.

[0081] All reports support multi-level drill-down analysis, allowing users to drill down from summary data to specific cost details. The system provides a standard report template library and allows users to customize analysis views based on predefined dimensions. Report generation supports both scheduled automatic execution and manual triggering modes, with output formats including structured data files, visualization charts, and optimized print documents. By integrating with the hospital's decision support system, cost analysis reports can be further correlated with indicators such as revenue and workload to form a complete operational decision support information chain.

[0082] Figure 6 A schematic diagram illustrating a multi-dimensional cost analysis provided in this application embodiment is shown below. Figure 6 The embodiments of this application can effectively support the cost control needs of hospitals under the DRG / DIP payment reform. By outputting equipment cost analysis reports based on specific surgeries and diseases, it helps hospitals to carry out disease cost accounting, optimize clinical pathways, and provide key data support for medical equipment procurement and usage benefit evaluation.

[0083] In summary, the embodiments of this application mainly include the following processes: (1) Operating room equipment department cost calculation process based on the correlation between surgical procedure and equipment operation data. This process aims to solve the problem that existing cost accounting methods cannot accurately aggregate equipment usage costs to a single surgery. Specifically, by establishing an automatic correlation mapping between surgical procedure and equipment operation data, and performing refined calculations based on a preset cost model, the cost can be accurately traced back to the equipment management department. Its core implementation logic is a data-driven, interconnected three-stage automated process.

[0084] (2) Automatic association process between surgical procedure and equipment operation data based on spatiotemporal rules. By collecting two types of heterogeneous data, surgical procedure data and equipment operation data in parallel, and using the operating room number as a spatial anchor and the surgical time window as a time filter, a time-series matching algorithm is used to automatically filter and bind the valid operation records of all equipment within the same operating room during the surgical period, generating a precise association table of "surgery-equipment-actual running time". This method fundamentally establishes a data link between surgery and equipment consumption, providing a unique and reliable data foundation for subsequent cost calculation. The automatic binding method of surgery-equipment based on spatiotemporal matching intelligently associates the surgical time and operating room number in the surgical anesthesia system with the real-time positioning and on / off status of medical equipment, constructing a precise "surgery-equipment" correspondence and solving the problem of disconnect between equipment use and specific surgery.

[0085] (3) Automatic calculation process of equipment usage costs based on a multi-dimensional parameter model. Using the association table generated in the first step as input, the built-in structured cost parameter library is driven to automatically trigger and calculate the depreciation, energy consumption, maintenance and other sub-costs based on the actual running time of the equipment. Among them, the depreciation cost is allocated according to the running time, the energy consumption cost is accurately measured according to the power and time, and the maintenance cost is allocated according to the running time threshold or proportion. This method realizes the automated and refined conversion from the physical running time of the equipment to the multi-dimensional cost amount. The automatic calculation and collection method of all cost elements, based on the bound equipment relationship, automatically calculates the actual equipment depreciation cost, energy cost and operation and maintenance cost consumed by a single operation, and collects each cost item to the corresponding equipment management department in real time, realizing the automation and accuracy of cost accounting.

[0086] (4) Accurate cost collection and report generation process for management entities. The system maintains the mapping relationship between equipment and departments, and automatically collects the cost details calculated in the second step, with "single surgery - single equipment" as the granularity, to the corresponding equipment management department according to the equipment number, and then sums them up. Through the sequential progression and data flow of the above three steps, the system can finally automatically generate cost accounting reports from the finest granularity to multi-dimensional summaries of departments and diseases, realizing the automated extraction of cost data into management decision-making information. The dynamic cost allocation method for multiple departments allocates costs for shared equipment and indirect support costs according to preset allocation rules at multiple levels, ensuring that costs can be accurately traced to the final beneficiary or responsible department. The multi-dimensional cost data service method for management decision-making generates analysis reports for departmental, surgical / disease, and equipment benefit dimensions by aggregating and visualizing the collected cost data, providing direct data support for the hospital's refined operation.

[0087] like Figure 7 As shown, based on the same inventive concept as the method for determining surgical costs described above, this application also provides an apparatus for determining surgical costs, which includes a data acquisition unit 71 and a data processing unit 72.

[0088] The data acquisition unit 71 is used to: determine the dataset to be processed; wherein the dataset to be processed includes multiple data entries, each data entry including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the actual running time. The data processing unit 72 is used to: calculate the cost information corresponding to each piece of data using a pre-set cost calculation method; wherein the cost information corresponding to the data is used to indicate the cost of the surgery represented by the data; The data processing unit 72 is also used to: determine the cost of each department based on the cost information corresponding to each of the multiple data points and the pre-set correspondence; wherein the pre-set correspondence includes a first correspondence between surgery and department, or a second correspondence between equipment and department.

[0089] In one optional implementation, the data acquisition unit 71 is specifically used for: Acquire multiple surgical procedure data and multiple equipment operation data; Based on pre-defined matching rules, establish correlations between multiple surgical procedure data and multiple equipment operation data; Generate a dataset to be processed based on the relationships between the data.

[0090] In one optional implementation, the data acquisition unit 71 is specifically used for: For any given surgery, if the first device is located in the operating room corresponding to the surgery during the first time period, and the runtime of the first device is within the range of the first time period, then the first device is determined to be a device associated with the surgery; wherein, the first time period is the time period consisting of the duration of the surgery. Based on the equipment associated with each surgery, establish the correlation between multiple surgical process data and multiple equipment operation data.

[0091] In an optional implementation, the data acquisition unit 71 is further configured to: If a device is used in multiple surgeries simultaneously, the surgeries associated with the device are determined based on the device location description information; the device location description information includes the device's movement trajectory or operation log information.

[0092] In one optional implementation, the parameters in the pre-set cost calculation method include depreciation parameters, energy consumption parameters, and maintenance strategy parameters; Among them, the depreciation parameters are determined based on equipment asset information and actual operating time; the energy consumption parameters are determined based on equipment physical characteristic parameters and time-of-use electricity pricing model; and the maintenance strategy parameters are determined based on the cumulative operating time of the equipment.

[0093] In one optional implementation, the pre-defined correspondence is a second correspondence between equipment and departments; The data processing unit 72 is specifically used for: For each department, based on the pre-defined second correspondence between equipment and department, determine the equipment identifier of at least one piece of equipment corresponding to the department; The cost corresponding to at least one device is determined by the device identifier of at least one device and the cost information corresponding to each of the multiple data points. The sum of the costs of all the equipment is used as the department's cost.

[0094] In one optional implementation, the pre-defined correspondence is a first correspondence between surgery and department; The data processing unit 72 is specifically used for: For each department, based on the pre-defined first correspondence between surgeries and departments, determine the surgical identifier for at least one surgery corresponding to that department; The cost of at least one surgery is determined by identifying the surgical identifier of at least one surgery and the cost information corresponding to each of the multiple data points. The sum of the costs of each surgery is used as the department's cost.

[0095] The device for determining surgical costs proposed in this application embodiment adopts the same inventive concept as the method for determining surgical costs described above, and can achieve the same beneficial effects, so it will not be described again here.

[0096] Based on the same inventive concept as the method for determining surgical costs described above, this application also provides an electronic device, which may specifically be a desktop computer, portable computer, smartphone, tablet computer, personal digital assistant (PDA), server, etc. Figure 8 As shown, the electronic device may include a processor 801 and a memory 802.

[0097] The processor 801 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0098] Memory 802, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 802 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0099] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned computer storage medium can be any available medium or data storage device that a computer can access, including but not limited to: mobile storage devices, random access memory (RAM), magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)) and other media capable of storing program code.

[0100] Alternatively, if the integrated units described above in this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes: mobile storage devices, random access memory (RAM), magnetic memory (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs), etc.) and other media capable of storing program code.

[0101] Based on the same inventive concept, this application also provides a computer program product, which includes computer program code that, when run on a computer, causes the computer to execute any of the methods for determining surgical costs discussed above. Since the principle by which the above-described computer program product solves the problem is similar to that of the method for determining surgical costs, the implementation of the above-described computer program product can be referred to the implementation of the method, and repeated details will not be elaborated further.

[0102] The above embodiments are only used to provide a detailed description of the technical solutions of this application. However, the description of the above embodiments is only for the purpose of helping to understand the methods of the embodiments of this application and should not be construed as a limitation on the embodiments of this application. Any changes or substitutions that can be easily conceived by those skilled in the art should be covered within the protection scope of the embodiments of this application.

Claims

1. A method for determining surgical costs, characterized in that, include: Determine the dataset to be processed; wherein the dataset to be processed includes multiple data entries, each data entry including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the following: actual running time. For each piece of data, a pre-defined cost calculation method is applied to calculate the cost information corresponding to the data; wherein, the cost information corresponding to the data is used to indicate the cost of the surgery represented by the data; Based on the cost information corresponding to each of the multiple data points, and the pre-set correspondence, the cost of each department is determined; wherein, the pre-set correspondence includes a first correspondence between surgery and department, or a second correspondence between equipment and department.

2. The method according to claim 1, characterized in that, The process of determining the dataset to be processed includes: Acquire multiple surgical procedure data and multiple equipment operation data; Based on pre-defined matching rules, establish the association between the multiple surgical procedure data and the multiple equipment operation data; The dataset to be processed is generated based on the aforementioned relationships.

3. The method according to claim 2, characterized in that, The process of establishing the association between the multiple surgical procedure data and the multiple equipment operation data based on pre-defined matching rules includes: For any given surgery, if the first device is located in the operating room corresponding to the surgery during a first time period, and the runtime of the first device is within the range of the first time period, then the first device is determined to be a device associated with the surgery; wherein, the first time period is the time period consisting of the duration of the surgery. Based on the devices associated with each surgery, establish the association between the multiple surgical process data and the multiple device operation data.

4. The method according to any one of claims 3, characterized in that, The method further includes: If a device is used for multiple surgeries simultaneously, the surgeries associated with the device are determined based on the device location description information; wherein, the device location description information includes the device's movement trajectory or operation log information.

5. The method according to claim 1, characterized in that, The parameters in the pre-set cost calculation method include depreciation parameters, energy consumption parameters, and maintenance strategy parameters; The depreciation parameters are determined based on equipment asset information and actual operating time; the energy consumption parameters are determined based on equipment physical characteristic parameters and time-of-use electricity pricing models; and the maintenance strategy parameters are determined based on the cumulative operating time of the equipment.

6. The method according to any one of claims 1 to 5, characterized in that, The pre-defined correspondence is the second correspondence between equipment and departments; Based on the cost information corresponding to each of the multiple data points, and the pre-defined correspondence, the cost of each department is determined, including: For each department, based on a pre-defined second correspondence between equipment and department, determine the equipment identifier of at least one piece of equipment corresponding to that department; The cost corresponding to the at least one device is determined by the device identifier of the at least one device and the cost information corresponding to each of the multiple data entries; The sum of the costs of all the equipment is taken as the cost of the department.

7. The method according to any one of claims 1 to 5, characterized in that, The pre-defined correspondence is the first correspondence between surgery and department; Based on the cost information corresponding to each of the multiple data points, and the pre-defined correspondence, the cost of each department is determined, including: For each department, based on the pre-defined first correspondence between surgeries and departments, determine the surgical identifier for at least one surgery corresponding to the department; The cost corresponding to the at least one surgery is determined by the surgical identifier of the at least one surgery and the cost information corresponding to each of the multiple data points; The sum of the costs of each surgery is taken as the cost of the department.

8. A device for determining surgical costs, characterized in that, include: The data acquisition unit is used to: determine the dataset to be processed; wherein the dataset to be processed includes multiple data entries, each data entry including a surgical identifier, and at least one device identifier, start time, end time, and part or all of the following: actual running time. The data processing unit is configured to: calculate the cost information corresponding to each piece of data using a pre-defined cost calculation method; wherein the cost information corresponding to the data is used to indicate the cost of the surgery represented by the data; The data processing unit is further configured to: determine the cost of each department based on the cost information corresponding to each of the multiple data points and a pre-defined correspondence; wherein the pre-defined correspondence includes a first correspondence between surgery and department, or a second correspondence between equipment and department.

9. An electronic 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 computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the computer program instructions implement the steps of the method according to any one of claims 1 to 7.

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