Hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion

Abstract

The invention discloses a hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion, particularly relates to the technical field of performance assessment, and comprises a data acquisition module, a multi-source data fusion module, a dynamic accounting module, an assessment and evaluation module and a result output module. The data acquisition module is used for acquiring hospital multi-dimensional medical service original data; and the multi-source data fusion module is in communication connection with the data acquisition module, and the multi-source data fusion module is used for cleaning and standardizing the acquired multi-dimensional medical service original data. According to the method, after the multi-source heterogeneous medical business original data is cleaned and standardized, feature extraction and fusion are carried out, data of different sources and formats can be precisely processed, standardized performance data of a unified format can be output, the problem that the data are too many and miscellaneous and are difficult to cover multi-dimensional key indexes is effectively solved, and the accuracy is higher during accounting and assessment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance evaluation, more specifically, the present application relates to a hospital employee performance dynamic accounting and evaluation system based on multi-dimensional medical business data fusion. BACKGROUND

[0002] The hospital employee performance dynamic accounting and evaluation system based on multi-dimensional medical business data fusion realizes automatic performance accounting and dynamic evaluation, helps hospitals optimize resource allocation, improve management efficiency, ensure medical quality, stimulate employee enthusiasm, and supports data-driven decision optimization and fine operation.

[0003] In the existing public literature, patent CN117371972A discloses a performance salary management method and system. This technology designs performance standards through various evaluation items in the target enterprise target post performance salary standard evaluation item structure, obtains and evaluates the target post completion information, and outputs the final salary of the target post. The performance salary management method and system provided by the present application can conveniently and efficiently design the standard performance salary evaluation structure and standards of the target post of the enterprise. However, this technology still has the following defects.

[0004] In the medical business data fusion processing process, due to the large amount of hospital employee performance accounting data and the large number of processing types, it is difficult to cover the multi-dimensional key indicators of the hospital, and there is a lack of multi-dimensional dynamic performance accounting and evaluation requirements, and the accuracy of accounting and evaluation is poor. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides the following technical scheme: a hospital employee performance dynamic accounting and evaluation system based on multi-dimensional medical business data fusion, comprising a data acquisition module, a multi-source data fusion module, a dynamic accounting module, an evaluation module and a result output module; The data acquisition module is used to acquire multi-dimensional medical business raw data of the hospital. The multi-source data fusion module is in communication connection with the data acquisition module. The multi-source data fusion module is used to clean and standardize the acquired multi-dimensional medical business raw data, and then uses a deep learning fusion model based on an attention mechanism to extract and fuse features of data from different sources and different formats, so as to obtain standardized performance data in a unified format. The dynamic accounting module is in communication connection with the data acquisition module. The dynamic accounting module is internally provided with a plurality of configurable performance accounting rule libraries. The evaluation module is in communication connection with the dynamic accounting module. The evaluation module constructs a multi-dimensional evaluation index system based on the dynamic accounting results, determines the weight of each evaluation index by using the analytic hierarchy process, and realizes comprehensive evaluation of employee performance. The result output module is connected to the assessment module. The result output module is used to display the dynamic accounting results and comprehensive assessment results in the form of visual reports, and supports data export and hierarchical viewing with different permissions.

[0006] In a preferred embodiment, the collection of multi-dimensional medical business raw data from hospitals includes: The hospital information system data includes outpatient workload, inpatient bed days, and number of surgeries; the electronic medical record system data includes medical record writing quality and implementation of treatment guidelines; the laboratory information system and medical imaging archiving and communication system data includes the number of examinations and tests and the timeliness of reports; the human resources management system data includes attendance records, job information, and professional title data; the cost accounting system data includes material consumption and equipment utilization efficiency; and the patient satisfaction survey system data includes satisfaction scores and complaint records.

[0007] In a preferred embodiment, the multi-source data fusion module includes: The data cleaning unit is used to fill in missing values, detect and correct outliers, and delete duplicate data in the raw data. The data standardization unit is used to format and unify the data from different sources according to the preset medical business data standards. It uses a multimodal neural network to weight and fuse the features of different data sources and output a standardized performance data table with a unified structure.

[0008] In a preferred embodiment, the performance accounting rule base includes: Differentiated accounting rules are configured according to job categories, including physicians, nurses, medical technicians, and administrative and logistical staff. The rules are dynamically adjusted according to the accounting cycle, supporting daily, weekly, monthly, quarterly, and annual accounting. The rules parameters can be updated in real time according to hospital management policies, and multiple accounting methods such as weighted scoring, threshold triggering, and interval segmented calculation are supported.

[0009] In a preferred embodiment, the dynamic accounting module further includes a rule engine and a real-time computing unit: The rule engine is used to parse and execute performance accounting rules and supports a graphical configuration interface. The real-time calculation unit is based on stream processing technology and triggers accounting logic on continuously input business data to achieve dynamic updates of performance results.

[0010] In a preferred embodiment, the multi-dimensional assessment indicator system constructed in the assessment module includes: Workload indicators include outpatient visits, number of surgeries, and number of discharges; quality indicators include cure rate, complication rate, and medical record addition rate; efficiency indicators include average length of stay, equipment utilization rate, and clinic utilization rate; cost control indicators include average cost per visit and proportion of consumables; teaching and research indicators include teaching hours, paper publications, and participation in research projects; and medical ethics and patient satisfaction indicators include the number of complaints and satisfaction scores.

[0011] In a preferred embodiment, the assessment module uses the analytic hierarchy process (AHP) to determine the weights of each indicator to construct a hierarchical structure model. The performance evaluation target is decomposed into a criterion layer and an indicator layer. A judgment matrix is ​​constructed through expert scoring. The relative weights of elements at each level are calculated, and a consistency check is performed to ensure the rationality of the weight allocation. Weight templates are set according to the differences in job types.

[0012] In a preferred embodiment, the assessment module further includes a performance feedback unit: It is used to generate performance analysis reports for individuals and departments, including strengths and areas for improvement, supports setting performance goals, compares trends with historical data, provides early warning functions, and provides real-time alerts for abnormal situations of key indicators.

[0013] In a preferred embodiment, the visualization report provided by the result output module includes: The individual performance dashboard displays real-time performance scores, rankings, and key indicator completion status. The departmental performance comparison analysis chart supports horizontal comparisons between multiple departments under the same indicator system. The performance trend chart reflects the performance changes of individuals or departments over a period of time. It supports data export in PDF and Excel formats and has hierarchical data viewing and operation permissions for hospital leaders, department directors, and individual employees.

[0014] In a preferred embodiment, a management module is also included: The result output module and the management module are connected for communication. The management module is used to manage user roles and permissions, define the access and operation scope of different roles to the system, manage the version of accounting rules and assessment indicator system, monitor the system operation status, and record operation logs and performance data.

[0015] The technical effects and advantages of this invention are as follows:

[0016] 1. This invention cleans and standardizes multi-source heterogeneous medical business raw data, then extracts and fuses features. It can accurately process data from different sources and formats, output standardized performance data in a unified format, effectively solve the problem of large and complex data that is difficult to cover multi-dimensional key indicators, provide a high-quality data foundation for subsequent accounting and assessment, improve data processing efficiency and accuracy, and make accounting and assessment more precise.

[0017] 2. This invention employs a dynamic accounting module with multiple built-in configurable performance accounting rule libraries, which can be flexibly adjusted according to position and cycle, supporting various accounting methods. The assessment module constructs a multi-dimensional assessment indicator system, uses the analytic hierarchy process (AHP) to determine weights, and can also set differentiated weight templates. This can meet the management needs of different hospitals, realize multi-dimensional dynamic performance accounting and assessment, comprehensively evaluate employee performance, and make the assessment results more scientific and reasonable.

[0018] 3. This invention employs a results output module to display dynamic accounting and comprehensive evaluation results in visual reports, including various chart formats. It supports data export and hierarchical viewing with different permissions. The management module can manage user roles and permissions, and perform version management. This facilitates viewing and use by personnel at different levels, provides intuitive evidence for hospital management decisions, and ensures the safe and stable operation of the system, thereby improving overall management efficiency. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the operation of the hospital employee performance dynamic accounting and assessment system that integrates multi-dimensional medical business data according to the present invention.

[0020] The attached diagram is labeled as follows: 1. Data acquisition module; 2. Multi-source data fusion module; 3. Dynamic accounting module; 4. Assessment and evaluation module; 5. Result output module; 6. Management module. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0022] In this embodiment, as shown in the attached figure, a hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion includes a data acquisition module 1, a multi-source data fusion module 2, a dynamic accounting module 3, an assessment module 4, and a result output module 5. The data acquisition module 1 is used to collect original multi-dimensional medical business data from the hospital. The multi-source data fusion module 2 is communicatively connected to the data acquisition module 1. The multi-source data fusion module 2 cleans and standardizes the collected original multi-dimensional medical business data, and then uses a deep learning fusion model based on an attention mechanism to extract features from data from different sources and in different formats. The system integrates and merges data to obtain standardized performance data in a unified format. The dynamic accounting module 3 is communicatively connected to the data acquisition module 1, and the dynamic accounting module 3 has multiple built-in configurable performance accounting rule libraries. The assessment and evaluation module 4 is communicatively connected to the dynamic accounting module 3. Based on the dynamic accounting results, the assessment and evaluation module 4 constructs a multi-dimensional assessment indicator system, uses the analytic hierarchy process (AHP) to determine the weight of each assessment indicator, and achieves a comprehensive assessment of employee performance. The result output module 5 is communicatively connected to the assessment and evaluation module 4. The result output module 5 is used to display the dynamic accounting results and comprehensive evaluation results in a visual report format, and supports data export and hierarchical viewing with different access permissions.

[0023] The collected original medical business data from multiple dimensions of the hospital includes: data from the hospital information system, including outpatient workload, inpatient bed days, and number of surgeries; data from the electronic medical record system, including the quality of medical record writing and the implementation of treatment guidelines; data from the laboratory information system and medical image archiving and communication system, including the number of examinations and tests and the timeliness of reports; data from the human resources management system, including attendance records, job information, and professional title data; data from the cost accounting system, including material consumption and equipment utilization efficiency; and data from the patient satisfaction survey system, including satisfaction scores and complaint records.

[0024] The multi-source data fusion module 2 includes: a data cleaning unit for filling missing values, detecting and correcting outliers, and deleting duplicate data from the original data; a data standardization unit for formatting and unifying data from different sources according to preset medical business data standards; and a multimodal neural network for weighted fusion of features from different data sources to output a standardized performance data table with a unified structure.

[0025] The performance accounting rule library includes: differentiated accounting rules configured according to job categories, including physicians, nurses, medical technicians, and administrative and logistical staff. The rules are dynamically adjusted according to the accounting cycle, supporting daily, weekly, monthly, quarterly, and annual accounting. The rules parameters can be updated in real time according to hospital management policies, and multiple accounting methods such as weighted scoring, threshold triggering, and interval segmented calculation are supported.

[0026] The dynamic accounting module 3 also includes a rule engine and a real-time calculation unit: the rule engine is used to parse and execute performance accounting rules and supports a graphical configuration interface; the real-time calculation unit is based on stream processing technology and triggers accounting logic on continuously input business data to realize dynamic updates of performance results.

[0027] The multi-dimensional assessment indicator system constructed in the assessment and evaluation module 4 includes: workload indicators including outpatient visits, number of surgeries, and number of discharges; quality indicators including cure rate, complication rate, and medical record addition rate; efficiency indicators including average length of stay, equipment utilization rate, and clinic utilization rate; cost control indicators including average cost control per visit and consumables ratio; teaching and research indicators including teaching time, paper publication, and participation in research projects; and medical ethics and patient satisfaction indicators including number of complaints and satisfaction score.

[0028] The assessment module 4 uses the analytic hierarchy process (AHP) to determine the weights of each indicator in order to construct a hierarchical structure model. The performance evaluation objectives are decomposed into a criterion layer and an indicator layer. A judgment matrix is ​​constructed through expert scoring. The relative weights of elements at each level are calculated, and a consistency check is performed to ensure the rationality of the weight allocation. Weight templates are set according to the differences in job types.

[0029] The assessment module 4 also includes a performance feedback unit: used to generate performance analysis reports for individuals and departments, including strengths and areas for improvement, supporting the setting of performance goals, trend comparison with historical data, providing early warning functions, and providing real-time reminders for abnormal situations of key indicators.

[0030] The visualization reports provided by the result output module 5 include: a personal performance dashboard, which displays real-time performance scores, rankings and key indicator completion status; a departmental performance comparison analysis chart, which supports horizontal comparison of multiple departments under the same indicator system; a performance trend chart, which reflects the performance changes of individuals or departments over a period of time; data export in PDF and Excel formats; and hierarchical data viewing and operation permissions for hospital leaders, department directors, and individual employees.

[0031] It also includes a management module 6: the result output module 5 and the management module 6 are connected for communication. The management module 6 is used to manage user roles and permissions, define the access and operation scope of different roles to the system, perform version management of accounting rules and assessment indicator system, monitor the system operation status, and record operation logs and performance data.

[0032] Based on the above description The following three sets of examples are derived: Example 1: Dynamic accounting and assessment of the performance of general surgeons in clinical departments.

[0033] Data sources were collected through the data acquisition module (1), including: Hospital Information System (HIS): 150 outpatient visits / week, 40 inpatient bed-days / week, and 12 surgeries / week, of which 4 were Class III surgeries; Electronic Medical Record System (EMR): 98% of medical record writing quality was Grade A, and 100% of treatment guidelines were followed; Cost Accounting System: 3000 yuan / week for material consumption, and 28 hours / week for operating room equipment utilization; Patient Satisfaction System: 95 points in satisfaction score and 0 complaints / week.

[0034] Data cleaning was performed using the multi-source data fusion module (2): missing value imputation, the missing surgery duration of one case in the HIS system was filled with the average duration of similar surgeries. Outlier correction: the writing time of one medical record in the EMR was corrected to be more than 24 hours to be a reasonable value.

[0035] Standardized processing: The number of surgical cases is converted according to complexity: Class III surgery × 1.5, Class II surgery × 1, Class I surgery × 0.5. Material consumption is normalized: converted to standard points based on the departmental average: 3000 yuan / 4000 yuan benchmark = 0.75 points.

[0036] Feature fusion: A multimodal neural network is used to weight and fuse surgical volume, medical record quality, and cost data to output a standardized performance data table containing fields such as "surgical contribution value", "quality compliance score", and "cost control score".

[0037] The dynamic accounting module (3) is configured through the rule engine. Physician job rules: surgical contribution value weight 40% + medical record quality score 30% + cost control score 20% + satisfaction score 10%. Accounting cycle: weekly accounting, monthly summary. Real-time calculation unit: after inputting new data, the rule engine is triggered to calculate: surgical contribution value = 12 cases × 1.2 Class III surgery weighted × 10 points / case = 144 points. Medical record quality score = 98% × 30 points = 29.4 points. Cost control score = 0.75 × 20 points = 15 points. Satisfaction score = 95 × 10% = 9.5 points. Weekly performance score: 144 + 29.4 + 15 + 9.5 = 197.9 points.

[0038] The assessment module (4) adopts a multi-dimensional indicator system: Workload: 12 surgical procedures, 150 outpatient visits. Quality: Grade A medical records rate 98%, complication rate 1%. Efficiency: Average length of stay 6 days, equipment utilization rate 90%. Cost: Average cost per visit 8,000 yuan, consumables account for 35%. Satisfaction: 0 complaints, score 95 points.

[0039] Analytic Hierarchy Process (AHP) weighting: Criterion layer weights: workload 30%, quality 30%, efficiency 20%, cost 10%, satisfaction 10%. Indicator layer weights: number of surgeries 20%, first-class medical record rate 25%, average length of stay 15%.

[0040] Comprehensive evaluation: Using the fuzzy comprehensive evaluation method, combining weights and indicator scores, a comprehensive score of 92 points was generated, which is excellent.

[0041] The results output module (5) consists of a visual report: Through the personal performance dashboard, it displays a weekly score of 197.9 points, a ranking in the top 10% of the department, and a surgical completion rate of 120%. Department comparison chart: A horizontal comparison of general surgery with orthopedics and urology in terms of surgical volume and cost indicators. Trend chart: The performance score fluctuation curve over the past 3 months shows a steady upward trend.

[0042] Access control: Hospital leaders: view hospital-wide data; Department heads: view data for their department; Physicians: view only their personal data.

[0043] When optimizing the management module (6), a report is generated through the performance feedback unit, indicating that "Class III surgeries made outstanding contributions, but the average cost per surgery slightly exceeded the standard of 8,500 yuan". Warning function: "The number of surgeries scheduled next week has increased, and attention should be paid to the efficiency of equipment use". Rule optimization: The accounting rules are adjusted according to the feedback: "Reward points for high-difficulty surgeries" are added, and the "deduction ratio for excessive material consumption" is reduced.

[0044] Example 2: Dynamic accounting and assessment of the performance of technicians in medical technology departments (laboratory departments).

[0045] The data acquisition module (1) uses data from: Laboratory Information System (LIS): Number of tests (2000 routine blood tests / week, 1500 biochemistry tests / week), and timely reporting rate (99%).

[0046] Picture Archiving and Communication System (PACS): Imaging examination volume (CT 300 cases / week, MRI 50 cases / week).

[0047] Equipment Management System: Equipment failure rate (0.5%), calibration records (compliant). Human Resources System: Attendance records (full attendance), job information (supervisor technician).

[0048] The multi-source data fusion module (2) cleaned the data by deleting five duplicate blood routine records from the LIS and correcting one case of abnormal MRI examination time (displayed as a negative number) in the PACS to a reasonable value.

[0049] Standardization: Test quantity is converted according to complexity: Biochemistry × 1.2, Complete Blood Count × 1. Report timeliness rate normalization: 99% → 0.99 points.

[0050] Feature fusion: The multimodal neural network fuses data on "inspection workload", "report quality" and "equipment status" to output "inspection efficiency score", "quality compliance score" and "equipment maintenance score".

[0051] The dynamic accounting module (3) is configured through the rule engine: Technician position rules: Inspection efficiency score (50%) + Quality compliance score (30%) + Equipment maintenance score (20%). Accounting cycle: Daily accounting, weekly summary.

[0052] Real-time calculation unit: Daily performance score calculation: Inspection efficiency score = (2000×1 + 1500×1.2) / 1000 (benchmark value)×50 points = 145 points.

[0053] Quality compliance score = 99% × 30 points = 29.7 points.

[0054] Equipment maintenance score = (1 - 0.5%) × 20 points = 19.9 points.

[0055] Daily performance score: 145 + 29.7 + 19.9 = 194.6 points.

[0056] The assessment module (4) uses a multi-dimensional indicator system: Workload: Total number of tests (3500 cases / week), number of imaging examinations (350 cases / week). Quality: Report error rate (0.1%), interlaboratory quality assessment pass rate (100%). Efficiency: Average report generation time (2 hours), equipment utilization rate (95%). Cost: Reagent loss rate (5%), equipment maintenance cost (2000 yuan / week).

[0057] Comprehensive evaluation: Analytic Hierarchy Process (AHP) weighting: workload (40%), quality (30%), efficiency (20%), cost (10%).

[0058] Overall score: 90 points (Excellent).

[0059] Visualized reports are generated through the results output and management module (6): Personal dashboard: daily score 194.6 points, cumulative score for this week 1362 points, and ranking in terms of inspection volume (2nd in the department).

[0060] Departmental Comparison Chart: A comparison of efficiency and cost indicators between the Laboratory Department, Radiology Department, and Pathology Department. Feedback Optimization: The report points out that "Biochemistry testing is efficient, but reagent loss rate is close to the upper limit (5%)." Optimization Rules: Add a "deduction item for excessive reagent loss".

[0061] Example 3: Dynamic performance accounting and assessment of administrative department (medical department) management personnel.

[0062] Data sources are obtained through the data acquisition module (1): Human Resources System: Attendance records (full attendance), job information (section chief), professional title (associate senior level). Quality Control System: Medical dispute handling volume (3 cases / month), number of system revisions (2 times / month). Training System: Number of training sessions organized (4 sessions / month), number of participants (200 people / month). Satisfaction System: Superior evaluation (90 points), inter-departmental evaluation (85 points).

[0063] Data cleaning is achieved through the multi-source data fusion module (2): the missing value of the dispute handling time in the quality control system is corrected (filled with the average handling time of similar disputes).

[0064] Standardized processing: The number of disputes handled is calculated based on complexity: major disputes × 2, ordinary disputes × 1.

[0065] Training sessions are weighted by the number of participants: 4 sessions × 200 people / 50 people (baseline) = 16 points.

[0066] Feature fusion: Integrates “management efficiency score”, “quality improvement score”, and “team collaboration score” to output standardized performance data.

[0067] The rule engine configuration is achieved through the dynamic accounting module (3): Administrative post rule: management efficiency score (40%) + quality improvement score (30%) + team collaboration score (30%).

[0068] Accounting cycle: Monthly accounting. Real-time calculation unit: Monthly performance score calculation: Management efficiency score = (3 × 1.5 + 2) × 10 points = 65 points (dispute handling). Quality improvement score = 2 revisions of the system × 15 points / revision = 30 points. Teamwork score = (90 points × 0.5 + 85 points × 0.5) = 87.5 points (after weight adjustment). Monthly performance score: 65 × 40% + 30 × 30% + 87.5 × 30% = 71.75 points.

[0069] The assessment module (4) implements a multi-dimensional indicator system: Workload: number of disputes handled (3 cases), number of system revisions (2 times). Quality: rectification completion rate (100%), quality control inspection coverage rate (100%). Efficiency: training organization timeliness rate (100%), process optimization cycle (15 days). Teamwork: superior evaluation (90 points), inter-departmental evaluation (85 points).

[0070] Overall assessment: Analytic Hierarchy Process (AHP) weights: workload (30%), quality (30%), efficiency (20%), teamwork (20%).

[0071] Overall score: 88 points (Excellent).

[0072] Visualized reports are generated through the results output and management module (6): Personal Dashboard: Displays monthly score of 71.75, ranking (top 20% of administrative departments), and dispute resolution efficiency meets standards.

[0073] Departmental comparison chart: A comparison of efficiency and teamwork indicators between the Medical Affairs Department, Nursing Department, and Hospital Office.

[0074] Feedback optimization: The report noted that "the training organization was highly efficient, but the interdepartmental peer review score was slightly low (85 points)."

[0075] Rule optimization: Add "team collaboration weight" and adjust it to 25%.

[0076] The following data table is derived from the above embodiments:

[0077] The above table describes a process where multi-source, heterogeneous raw medical business data is cleaned, standardized, and then feature extracted and fused. This approach accurately processes data from different sources and formats, outputting standardized performance data in a unified format. It effectively addresses the problem of large and complex datasets that struggle to cover multi-dimensional key indicators, providing a high-quality data foundation for subsequent accounting and evaluation. This improves data processing efficiency and accuracy, resulting in more precise accounting and evaluation.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A hospital employee performance dynamic accounting and assessment system that integrates multi-dimensional medical business data, characterized in that, It includes a data acquisition module (1), a multi-source data fusion module (2), a dynamic accounting module (3), an assessment and evaluation module (4), and a result output module (5). The data acquisition module (1) is used to collect original data of multi-dimensional medical services of the hospital; The multi-source data fusion module (2) is connected to the data acquisition module (1) for communication. The multi-source data fusion module (2) is used to clean and standardize the collected multi-dimensional medical business raw data, and then use a deep learning fusion model based on attention mechanism to extract and fuse features of data from different sources and in different formats to obtain standardized performance data in a unified format. The dynamic accounting module (3) is connected to the data acquisition module (1) and the dynamic accounting module (3) has multiple configurable performance accounting rule libraries built in. The assessment module (4) is connected to the dynamic accounting module (3). Based on the dynamic accounting results, the assessment module (4) constructs a multi-dimensional assessment indicator system, uses the analytic hierarchy process to determine the weight of each assessment indicator, and realizes a comprehensive assessment of employee performance. The result output module (5) is connected to the assessment module (4) for communication. The result output module (5) is used to display the dynamic accounting results and comprehensive assessment results in the form of a visual report, and supports data export and hierarchical viewing with permissions.

2. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The collected raw data on multi-dimensional medical operations from hospitals includes: The hospital information system data includes outpatient workload, inpatient bed days, and number of surgeries; the electronic medical record system data includes medical record writing quality and implementation of treatment guidelines; the laboratory information system and medical imaging archiving and communication system data includes the number of examinations and tests and the timeliness of reports; the human resources management system data includes attendance records, job information, and professional title data; the cost accounting system data includes material consumption and equipment utilization efficiency; and the patient satisfaction survey system data includes satisfaction scores and complaint records.

3. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The multi-source data fusion module (2) includes: The data cleaning unit is used to fill in missing values, detect and correct outliers, and delete duplicate data in the raw data. The data standardization unit is used to format and unify the data from different sources according to the preset medical business data standards. It uses a multimodal neural network to weight and fuse the features of different data sources and output a standardized performance data table with a unified structure.

4. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The performance accounting rule base includes: Differentiated accounting rules are configured according to job categories, including physicians, nurses, medical technicians, and administrative and logistical staff. The rules are dynamically adjusted according to the accounting cycle, supporting daily, weekly, monthly, quarterly, and annual accounting. The rules parameters can be updated in real time according to hospital management policies, and multiple accounting methods such as weighted scoring, threshold triggering, and interval segmented calculation are supported.

5. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The dynamic accounting module (3) also includes a rule engine and a real-time computing unit: The rule engine is used to parse and execute performance accounting rules and supports a graphical configuration interface. The real-time calculation unit is based on stream processing technology and triggers accounting logic on continuously input business data to achieve dynamic updates of performance results.

6. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The multi-dimensional assessment indicator system constructed in the assessment module (4) includes: Workload indicators include outpatient visits, number of surgeries, and number of discharges; quality indicators include cure rate, complication rate, and medical record addition rate; efficiency indicators include average length of stay, equipment utilization rate, and clinic utilization rate; cost control indicators include average cost per visit and proportion of consumables; teaching and research indicators include teaching hours, paper publications, and participation in research projects; and medical ethics and patient satisfaction indicators include the number of complaints and satisfaction scores.

7. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The assessment module (4) uses the analytic hierarchy process to determine the weight of each indicator in order to construct a hierarchical structure model. The performance evaluation target is decomposed into a criterion layer and an indicator layer. A judgment matrix is ​​constructed through expert scoring. The relative weights of elements at each level are calculated and a consistency test is performed to ensure the rationality of the weight allocation. Weight templates are set according to the differences in job types.

8. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The assessment module (4) also includes a performance feedback unit: It is used to generate performance analysis reports for individuals and departments, including strengths and areas for improvement, supports setting performance goals, compares trends with historical data, provides early warning functions, and provides real-time alerts for abnormal situations of key indicators.

9. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion as described in claim 1, characterized in that: The visualization reports provided by the result output module (5) include: The individual performance dashboard displays real-time performance scores, rankings, and key indicator completion status. The departmental performance comparison analysis chart supports horizontal comparisons between multiple departments under the same indicator system. The performance trend chart reflects the performance changes of individuals or departments over a period of time. It supports data export in PDF and Excel formats and has hierarchical data viewing and operation permissions for hospital leaders, department directors, and individual employees.

10. The hospital employee performance dynamic accounting and assessment system based on multi-dimensional medical business data fusion according to claim 1, characterized in that: It also includes a management module (6): The result output module (5) and the management module (6) are connected for communication. The management module (6) is used to manage user roles and permissions, define the access and operation scope of different roles to the system, perform version management of accounting rules and assessment indicator system, monitor the system operation status, and record operation logs and performance data.

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