Clinical department performance panoramic intelligent monitoring and evaluation management method and system

Abstract

The invention provides a clinical department performance panoramic intelligent monitoring and evaluation management method and system. The method comprises the following steps: developing a data interface for connecting an HIS system and a performance management system, acquiring cardiology data from a plurality of source systems, and cleaning, converting, integrating and standardizing the data; designing a workload calculation rule, and calculating the workload of each doctor; integrating medical quality indexes to comprehensively evaluate the work quality of doctors, and taking the work quality as a dimension coefficient of performance calculation; monitoring and analyzing index data of overall operation of the department of cardiology; resource configuration is optimized; and presenting a monitoring analysis result to a user through an interactive interface. According to the invention, integrated management and analysis of data are realized, and data islands are broken; real-time updating and dynamic monitoring of data are realized, and the data statistics efficiency and accuracy are improved; a cardiology exclusive data analysis algorithm is designed to meet cardiology special data processing requirements; offering department operation key index monitoring and deep data analysis; and a modularized and extensible system architecture is adopted, so that the expansibility is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical department operation and performance management, in particular, to the technical field of cardiovascular disease data collection and processing in cardiology; specifically, to a method and system for panoramic intelligent monitoring and evaluation management of clinical department performance. Background Art

[0002] Cardiovascular disease, a major public health issue, has seen its incidence rise annually, placing higher demands on cardiology services. With Peking University People's Hospital establishing the "digitalized specialty development, operational management, ecosystem, and interactive experience" development dimensions, the hospital's information technology development is evolving from traditional demand-driven functional development to a digital hospital characterized by comprehensive data, universal connectivity, intelligent decision-making, and digital automation. In this context, the degree of information technology in cardiology management, a key specialty of the hospital, directly impacts both patient service quality and departmental operational efficiency.

[0003] However, cardiology department data on outpatient visits, hospitalizations, surgeries, and auxiliary examinations is currently scattered across disparate systems. Data silos are severe, making integration and correlation analysis difficult. This prevents the full mining and utilization of data value, significantly limits data collection and processing, and creates a significant workload for manual statistics. Furthermore, key operational data such as the CMI (Case Mix Index) is not timely and accurate, creating challenges in workload assessment and resource allocation, making it impossible to objectively, promptly, and impartially assess physician workload. Furthermore, existing hospital information management (HIS) systems suffer from poor data real-time performance and untimely statistical updates, making it difficult to meet the real-time decision-making needs of department management. This leads to irrational resource allocation, a lack of sufficient data support for departmental decision-making, and inaccurate outpatient and inpatient flow management, which can sometimes lead to resource shortages or waste.

[0004] The statistics modules of existing hospital HIS systems typically include basic statistical functions, enabling the generation of simple reports on outpatient visits, hospitalizations, and so on. However, these systems are primarily geared towards hospital-wide management and lack in-depth support for the specialized needs of cardiology departments. They are unable to provide detailed statistics on cardiology-specific examination data such as coronary artery, echocardiography, electrocardiograms, and electrophysiology, nor can they assess physician workload based on the specific characteristics of cardiology departments.

[0005] Existing hospital performance management systems (BI systems) on the market can perform basic statistics and analysis on physician workloads. However, these systems are typically general-purpose solutions for all hospital departments and cannot meet the cardiology department's needs for integrating data from specialized examinations such as coronary and electrophysiology groups, or calculating specific performance indicators. These systems typically use generic data models and evaluation criteria, lacking scientific evaluation models tailored to the specific characteristics of cardiology departments, and their unscientific evaluation criteria make it impossible to objectively and impartially assess cardiologists' workloads, failing to reflect the unique nature and complexity of cardiology work.

[0006] Currently, many hospital cardiology departments use self-developed Excel spreadsheets for data statistics and analysis. However, these tools rely heavily on manual collection, organization, and statistical analysis. This manual process is not only labor-intensive and inefficient, but also prone to errors, making it difficult to ensure data timeliness and accuracy. Furthermore, due to decentralized data storage, real-time updates and in-depth data mining are difficult, and they cannot support complex analysis and decision-making needs.

[0007] In addition, the existing hospital performance management system mainly provides basic data statistics, lacks in-depth analysis, and cannot provide sufficiently effective support for department decision-making; the hospital's existing system architecture is rigid and has poor system scalability, making it difficult to adapt to the changes and development of cardiology business and unable to meet the growing needs of data management and analysis. These problems seriously restrict the refined management and high-quality development of cardiology departments. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to design a panoramic intelligent monitoring and evaluation management method and system for clinical department performance, which uses data integration technology and intelligent analysis algorithms to automatically collect, integrate and analyze the outpatient, inpatient, surgical and auxiliary examination data of the cardiology department, realize the objective evaluation of the workload of cardiologists, real-time monitoring of department operation indicators and intelligent optimization of resource allocation, thereby improving department management efficiency and medical service quality, and meeting the needs of contemporary digital hospital construction.

[0009] The present invention provides a method for comprehensive intelligent monitoring and evaluation of clinical department performance, comprising the following steps:

[0010] S1. Develop data interfaces for connecting to HIS systems and performance management systems, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion, and integration to achieve medical data standardization and build a unified data view;

[0011] S2. Design a calculation rule for the cardiology department's workload, and calculate each doctor's workload based on the calculation rule. The workload covers multiple dimensions including outpatient care, hospitalization, surgery, and auxiliary examinations;

[0012] S3. Integrate medical quality indicators to comprehensively evaluate the quality of doctors' work and use them as an important dimension coefficient for performance calculation;

[0013] S4. Monitor and analyze the indicator data of the overall operation of the cardiology department to provide data support for department management decisions;

[0014] S5. Provide intelligent decision-making recommendations based on the results of indicator data monitoring and analysis, optimize resource allocation, and assist in optimizing department management. This includes: using linear programming algorithms to generate optimal physician scheduling plans based on historical outpatient volume and seasonal variation patterns; taking into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization efficiency while ensuring medical quality; and monitoring key indicators based on statistical anomaly detection algorithms, automatically triggering risk warnings when indicators exceed preset thresholds.

[0015] S6. Present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display.

[0016] Furthermore, the S1 step includes:

[0017] S11. Develop data interfaces for connecting to the hospital's HIS, LIS, PACS, and EMR systems based on the HL7 standard, and extract cardiology-related data from multiple medical data source systems using Web Services, database links, and file transfers.

[0018] For example, the data interface of the HIS system is connected to extract cardiology-related data including:

[0019] Extracted content: patient basic information, registration records, fee details, etc.

[0020] Extraction method: database view interface or hospital integration platform API;

[0021] Extraction frequency: The scheduled task automatically synchronizes data during non-peak hours every morning. Data can be manually updated with one click during other periods.

[0022] Connect to the data interface of the LIS system to extract cardiology related data including:

[0023] Extracted content: results of cardiology-related test items (such as myocardial enzyme spectrum, BNP, blood lipids and other indicators);

[0024] Extraction method: HL7 interface or database read-only account;

[0025] Extraction frequency: The scheduled task automatically synchronizes data during non-peak hours every morning. Data can be manually updated with one click during other periods.

[0026] Connect to the data interface of the PACS system to extract cardiology related data including:

[0027] Extracted content: electrocardiogram, echocardiography, coronary angiography, CT and other imaging examination results and reports;

[0028] Extraction method: DICOM interface and structured report interface;

[0029] Extraction frequency: The scheduled task automatically synchronizes data during non-peak hours every morning. Data can be manually updated with one click during other periods.

[0030] Connect to the EMR system's data interface to extract cardiology-related data including:

[0031] Extracted content: medical records, diagnostic information, surgical records, and discharge summaries;

[0032] Extraction method: CDA document interface or custom XML / JSON interface;

[0033] Extraction frequency: The scheduled task automatically synchronizes data during non-peak hours every morning. Data can be manually updated with one click during other periods.

[0034] S12. Incremental synchronization of the cardiology-related data is achieved using a dual mechanism of timestamp and data version number, extracting only data that has been added or modified since the last data synchronization, reducing network transmission volume and processing time; and performing full synchronization on a daily basis to ensure data consistency.

[0035] S13. Perform data cleaning and processing, using regular expressions to identify and repair malformed data, such as irregular date formats and special characters; using an outlier detection algorithm based on business rules to mark test results outside the normal range as abnormal and await manual confirmation; applying a fuzzy matching algorithm to handle information differences of the same patient in different medical data source systems and construct a unified patient index;

[0036] In one embodiment of the present invention, regular expressions are used to identify and repair irregular date formats. An example of the repair is as follows: "2023.5.10", "2023 / 5 / 10", and "2023-5-10" are uniformly converted to the standard format "2023-05-10".

[0037] Examples of outlier detection algorithms are as follows:

[0038] The normal systolic blood pressure range is 90-140 mmHg. If the systolic blood pressure is 180 mmHg, the abnormality is (180-140) / 140 = 0.286. If the systolic blood pressure is a key indicator (test type = 2) and the threshold coefficient is 1.2, then 0.286 < 1.2, and the result is considered "mild abnormality, automatically handled by the system."

[0039] An example of a fuzzy matching algorithm is as follows:

[0040] Similarity score = W1 × name + W2 × gender + W3 × date of birth + W4 × ID card + W5 × phone number;

[0041] Among them, W1, W2, W3, W4, and W5 are weight coefficients, which are 0.3, 0.1, 0.2, 0.3, and 0.1 respectively;

[0042] When the similarity score was ≥0.8, they were determined to be the same patient;

[0043] When the score was <0.8, the patients were judged to be different.

[0044] S14. Convert disease codes in different medical data source systems to the ICD-10 standard and surgical codes to the ICD-9-CM-3 standard to ensure consistency and comparability of data analysis.

[0045] Specifically, the method for uniformly converting disease codes in different medical data source systems into ICD-10 standards includes:

[0046] 1. Establish a direct mapping table to directly map commonly used disease codes in the hospital to ICD-10 disease codes;

[0047] 2. For codes that cannot be directly mapped, semantic analysis of the disease name is used to extract disease name keywords and search for the disease entry with the highest matching degree in the ICD-10 standard library.

[0048] The surgical code conversion adopts the method of surgical name standardization + coding mapping table, including:

[0049] 1. Standardize surgical names, remove irrelevant terms and modifiers, or replace with synonyms;

[0050] 2. Use multi-level mapping rules to directly search for the ICD-9-CM-3 code corresponding to the surgical name; for complex surgeries, split them into basic surgical components and match them separately.

[0051] Furthermore, the S2 step includes:

[0052] Design an outpatient workload algorithm to calculate outpatient workload. The outpatient workload algorithm includes: calculating the actual number of patients seen by each doctor based on data extracted from the outpatient clinic; setting different outpatient type weight coefficients for specialist outpatient clinics and general outpatient clinics (e.g., specialist outpatient type weight 1.5, general outpatient type weight 1.0); taking into account the difference between initial and follow-up visits, setting different workload coefficients for initial and follow-up visits (e.g., initial visit coefficient 1.2, follow-up visit coefficient 0.8);

[0053] Specifically, outpatient workload = number of visits × outpatient type weight coefficient × initial / return visit coefficient × disease complexity adjustment coefficient;

[0054] Among them, the disease complexity adjustment coefficient for the initial diagnosis is 1.2, and the disease complexity adjustment coefficient for the follow-up diagnosis is 1.0.

[0055] For example, a doctor sees 20 first-time patients (with a disease complexity adjustment factor of 1.2) and 30 return patients (with a disease complexity adjustment factor of 1.0) in the specialist clinic in one week. The doctor's outpatient workload is:

[0056] 20×1.5×1.2×1.2 + 30×1.5×0.8×1.0.

[0057] Designing an inpatient workload algorithm to calculate the inpatient workload, the inpatient workload algorithm comprising: calculating the inpatient workload using the formula: responsibility coefficient × bed-days × bed weight = inpatient workload; dynamically adjusting the workload coefficient based on the complexity of the patient's condition (assessed using the Charlson Comorbidity Index), with the maximum workload coefficient for complex cases being 2.0; preferably, the attending physician receives 100% of the workload score, and the deputy attending physician receives 60% of the workload score;

[0058] Design a surgical workload algorithm to calculate the surgical workload. The algorithm includes: setting a base score based on the difficulty level of the surgery (levels I-IV correspond to base scores of 10, 20, 30, and 40, respectively); for every 30 minutes of surgery exceeding the average duration, the workload increases by 10%; preferably, the primary surgeon receives 100% of the workload, the first assistant surgeon receives 80%, and the second assistant surgeon receives 50%;

[0059] A workload calculation rule is specially designed for cardiology-specific examination items to calculate the workload of auxiliary examinations. The basic score of echocardiography is 15 points, and 5 points are added for each additional special functional examination (such as tissue Doppler and strain rate analysis). The 12-lead electrocardiogram in the electrocardiogram is 5 points, the dynamic electrocardiogram is 15 points, and the basic score of electrophysiological examination is 30 points, including 20 points for ablation treatment.

[0060] The present invention can obtain key indicator data through an interface or manual input by setting annual performance scoring dimensions and scoring standards and calculation rules for each dimension, and realize performance scoring calculation in conjunction with an algorithm. For example, the medical scoring algorithm: based on the data extracted from the outpatient, surgical, and inpatient modules, obtain the numerical values ​​of each doctor's surgical volume, outpatient volume, CMI, turnover rate, consumption ratio, drug ratio and other indicators, assign a weight to each indicator, and calculate the final performance score of the medical dimension; scientific research scoring algorithm: based on the entered topics, articles, patents, awards and other indicators, assign scoring standards and calculation rules to each indicator, such as 10 points / item for national invention patents, 10 points / item for national awards, and 5 points / item for provincial and ministerial awards, and calculate the scientific research score. The scores of each dimension are weighted and calculated to obtain the annual performance score.

[0061] Furthermore, the S3 step includes:

[0062] S31. Integrate hospital satisfaction survey and patient complaint data, calculate physician satisfaction index, and conduct patient satisfaction analysis;

[0063] Specifically, satisfaction index = patient satisfaction evaluation score - complaint deduction score;

[0064] Note: Mild complaint = 5 points / case, moderate complaint = 10 points / case, severe complaint = 20 points / case;

[0065] For example, if a doctor scores 92 points in a monthly patient satisfaction survey and receives 1 mild complaint and 1 moderate complaint during the period, the satisfaction index is: 92-5-10;

[0066] A satisfaction index of 90 or higher is considered excellent, and the coefficient for performance calculation is 1.1;

[0067] A satisfaction index of 80-89 is considered good, and the coefficient for performance calculation is 1.0;

[0068] A satisfaction index of 70-79 is classified as average, and the coefficient for performance calculation is 0.9;

[0069] The satisfaction index <70 is considered poor, and the coefficient of performance calculation is 0.8;

[0070] S32. The three indicators of outcome assessment, document quality and satisfaction are weighted in the ratio of 5:3:2 to obtain the comprehensive quality score of the doctor, which is used as the workload adjustment coefficient.

[0071] Specifically, the expression for weighting the three indicators of outcome assessment, document quality, and satisfaction in the ratio of 5:3:2 is:

[0072] Outcome assessment × 0.5 + document quality × 0.3 + satisfaction index × 0.2.

[0073] Furthermore, the S4 step includes:

[0074] S41. Calculate average length of stay, bed turnover rate, and bed occupancy rate indicators. Set warning values ​​to highlight data exceeding the standard. Analyze key factors affecting bed efficiency, such as patient composition and physician allocation, and conduct bed efficiency analysis.

[0075] Specifically, average length of stay = total number of hospitalized patients / number of discharged patients;

[0076] Bed turnover rate = number of discharged patients / average number of open beds;

[0077] Bed occupancy rate = (total number of hospitalized person-days / average number of open beds × number of statistical days) × 100%.

[0078] S42. Calculate department CMI values ​​based on DRG grouping to assess the severity of patient diseases and resource consumption levels; use Activity-Based Costing (activity-based cost calculation) methods to accurately calculate the costs and benefits of different diseases; integrate drug, consumable, and examination fee data, calculate the economic indicators of drug ratio and consumable ratio, conduct economic indicator analysis, monitor indicator changes through year-on-year and month-on-month analysis, and highlight data exceeding the standard by setting early warning values.

[0079] Specifically, the method for calculating the department CMI value based on DRG grouping includes:

[0080] 1. Collect diagnosis and surgical information of discharged patients;

[0081] 2. According to the DRG grouping criteria of the National Health Commission, patients were divided into different DRG groups;

[0082] 3. Assign a corresponding weight to each DRG group (from the national or local DRG weight table);

[0083] Department CMI = Σ(DRG weight × number of cases for this DRG) / total number of cases.

[0084] Examples of assessing patient severity of illness and resource consumption levels include:

[0085] 1. CMI < 0.8: low complexity and low resource consumption;

[0086] 2.0.8≤CMI<1.0: medium-low complexity, slightly low resource consumption;

[0087] 3.1.0≤CMI<1.2: medium complexity, moderate resource consumption;

[0088] 4.1.2≤CMI<1.5: Medium-to-high complexity, high resource consumption;

[0089] 5. CMI ≥ 1.5: High complexity and high resource consumption.

[0090] Total cost of disease = direct cost + indirect cost;

[0091] Among them, direct cost = direct material cost + direct labor cost;

[0092] Direct material costs include the cost of consumables such as stents, balloons, and guidewires. Direct labor costs include the labor hours of doctors, nurses, and other personnel directly involved in treatment.

[0093] Furthermore, the S6 step includes:

[0094] Dynamically generate personalized, adaptive dashboards based on user roles and preferences. Use Ant Design to implement highly interactive data visualization components, supporting drag-and-drop adjustments, drill-down analysis, and conditional filtering. Preset data views for common analysis scenarios, such as physician workload rankings and monthly department trends, can be switched with one click.

[0095] Use a variety of visual chart types to display multi-dimensional data, such as line charts (displaying time trends), bar charts (displaying classification comparisons), heat maps (displaying multi-dimensional cross-analysis), radar charts (displaying comprehensive scores), etc., and support interactive linkage between charts. Click on an element in a chart, and the related charts will automatically update to display related data.

[0096] Provides a variety of report templates and supports generating standard reports based on different cycles such as daily, weekly, monthly, quarterly, and annually. Reports can be exported to multiple formats including Excel, PDF, and Word to meet the needs of different scenarios.

[0097] Furthermore, the method for managing the comprehensive intelligent monitoring and evaluation of clinical department performance also includes providing system configuration, authority management, and log auditing functions to ensure the safe and stable operation of the system, including:

[0098] Provides role-based permission management, adopts the RBAC (Role-Based Access Control) model to achieve refined permission control, sets different roles for administrators, department directors, doctors, and data analysts, and each role has different functional permissions and data access scopes; supports custom roles and permission matrices, and flexible configuration of system functions;

[0099] Specifically, the principles for implementing refined permission control using the RBAC model include:

[0100] 1. The core of RBAC is to achieve indirect association between users and permissions, using an indirect authorization mechanism: users are assigned to one or more roles; roles are granted a set of specific permissions; users indirectly obtain permissions through their roles;

[0101] 2. In the cardiology performance system, RBAC implements refined control in the following ways:

[0102] (1) Establish a role hierarchy with role inheritance relationships. For example, the department director role inherits all permissions of the doctor role; the senior physician role inherits the permissions of the general physician; and the administrator role has global permissions.

[0103] (2) Implement mutually exclusive role restrictions to achieve static separation of duties and ensure that the same user cannot simultaneously hold roles that conflict with interest (e.g., the roles of performance evaluator and evaluated cannot overlap). Implementation methods include:

[0104] Design a database. A typical RBAC database design includes the following core tables: Users, Roles, Permissions, User_Role, Role_Permission, and Resources.

[0105] The operating procedures in the cardiology performance management system are as follows:

[0106] Load user role information after login authentication;

[0107] Use permission interceptors to check user operation permissions;

[0108] The front end dynamically renders the function menu according to the permissions;

[0109] The back-end API interface performs permission verification;

[0110] Add permission filtering conditions when querying data.

[0111] Provide data security, use AES-256 encryption algorithm to protect sensitive data storage security, implement data access audit function, and record all access operations of sensitive data, including visitor, access time, and access content;

[0112] A unified configuration management interface is provided to support administrators in adjusting system parameters, performance calculation rules, and key indicator warning value settings. Configuration modifications support version control, enabling the review of historical configurations and rollback when necessary. Key configuration changes require multi-level approval to prevent misoperations from affecting the normal operation of the system.

[0113] The present invention also provides a comprehensive intelligent monitoring and evaluation management system for clinical department performance, which is used to implement the comprehensive intelligent monitoring and evaluation management method for clinical department performance as described above, including:

[0114] Data collection and integration module: used to develop data interfaces for connecting to HIS systems and performance management systems, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion, and integration to achieve medical data standardization and build a unified data view;

[0115] Doctor workload and performance score calculation module: used to design calculation rules for cardiology department workload, and calculate the workload of each doctor according to the calculation rules. The workload covers multiple dimensions including outpatient, hospitalization, surgery, and auxiliary examinations;

[0116] Quality evaluation module: used to integrate medical quality indicators and conduct a comprehensive assessment of doctors' work quality, serving as an important dimension coefficient for performance calculation;

[0117] Department operation analysis module: used to monitor and analyze the indicator data of the overall operation of the cardiology department;

[0118] Decision Support Module: This module provides intelligent decision-making recommendations based on the results of indicator data monitoring and analysis, optimizing resource allocation. This module uses a linear programming algorithm to generate optimal physician scheduling based on historical outpatient volume and seasonal variation patterns. It also takes into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization while ensuring medical quality. It also monitors key indicators using a statistical anomaly detection algorithm, automatically triggering risk warnings when indicators exceed preset thresholds.

[0119] Visual display module: used to present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display.

[0120] The clinical department performance panoramic intelligent monitoring and evaluation management system of the present invention has the following advantages in practical application compared with existing systems:

[0121] 1. Compared with general HIS statistical modules and third-party performance management systems, this invention is designed specifically for cardiology departments, fully considering the business characteristics and data needs of cardiology departments. It implements data caching, distributed locking, and real-time data processing based on Redis (BSD protocol), which can better meet the needs of refined management of cardiology departments and has strong specialty targeting.

[0122] 2. Existing systems can only process data from specific sources and cannot provide a comprehensive data view. This invention can automatically collect and integrate data from different systems, solving the problem of data silos and demonstrating outstanding data integration capabilities.

[0123] 3. The workload assessment model designed in this invention is based on the work characteristics of cardiology departments. It comprehensively considers multiple dimensions and adopts JWT (open standard) to implement role-based user authentication and authorization management. The assessment model is more scientific and objective than the existing system's assessment method based on simple counting.

[0124] 4. Existing systems mainly provide basic statistical functions, but lack analytical depth. This invention provides rich data analysis functions, including trend analysis, forecast analysis, correlation analysis, etc., with more powerful analysis functions.

[0125] 5. Existing systems typically rely on regular manual updates, resulting in poor real-time data performance. This invention utilizes automated data collection and processing technology, based on SignaIR (MIT protocol) to enable real-time push and update of cardiology department performance data, and based on Zabbix (GPL protocol) to enable dynamic monitoring of system operation status and performance analysis, resulting in improved real-time performance.

[0126] 6. The visualization display module of the present invention is intuitively designed and highly interactive. It builds a performance dashboard and configuration interface based on Ant Design (MIT protocol) and supports personalized settings. Compared with the static reports and simple charts of existing systems, it can provide a better user experience.

[0127] 7. Existing systems are usually rigid in architecture and difficult to expand and adjust. The present invention adopts a modular and scalable system architecture that can adapt to the changes and development of cardiology services and has stronger system scalability.

[0128] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for panoramic intelligent monitoring and evaluation management of clinical department performance.

[0129] The present invention also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the steps of the method for panoramic intelligent monitoring and evaluation management of clinical department performance as described above are implemented.

[0130] Compared with the prior art, the present invention has the following beneficial effects:

[0131] The present invention provides a panoramic intelligent monitoring and evaluation management method and system for clinical department performance, which integrates multi-source heterogeneous data such as outpatient, inpatient, surgical, and auxiliary examination data through a unified data acquisition and storage mechanism to achieve integrated data management and analysis, thus breaking down data silos. Automatic data acquisition and processing enable automated statistics, reduce manual operations, and achieve real-time data updates and dynamic monitoring, thereby improving the efficiency and accuracy of data statistics. A dedicated data model and analysis algorithm are designed based on the specialized business characteristics and data requirements of cardiology departments to meet the cardiology department's needs for special data processing such as coronary and electrophysiology groups. Based on the characteristics and complexity of cardiology work, a scientific and objective physician workload assessment model is constructed to achieve fair and transparent performance management. Real-time data acquisition and processing technology ensures timely data updates and dynamic display, providing real-time decision support for department management. Key indicator monitoring of department operations is provided, and in-depth data analysis provides more effective support for department decision-making. A modular and scalable system architecture is adopted to enhance system scalability, adapt to changes and developments in cardiology business, and meet the growing demand for data management and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0133] In the attached figure:

[0134] Figure 1 This is a flow chart of a method for comprehensive intelligent monitoring and evaluation management of clinical department performance according to an embodiment of the present invention;

[0135] Figure 2 This is a flow chart of the method of step S1 in an embodiment of the present invention;

[0136] Figure 3 This is a flow chart of the method of step S3 in an embodiment of the present invention;

[0137] Figure 4 This is a method flow chart of step S4 in an embodiment of the present invention;

[0138] Figure 5 Schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0139] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of systems and products consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0140] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0141] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining."

[0142] The embodiments of the present invention are described in further detail below.

[0143] The embodiment of the present invention provides a method for comprehensive intelligent monitoring and evaluation management of clinical department performance. Figure 1 As shown, the following steps are included:

[0144] S1. Develop a data interface to connect the HIS system and the performance management system, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion and integration to achieve medical data standardization and build a unified data view, including the following steps (such as Figure 2 shown):

[0145] S11. Develop data interfaces for connecting to the hospital's HIS, LIS, PACS, and EMR systems based on the HL7 standard, and extract cardiology-related data from multiple medical data source systems using Web Services, database links, and file transfers.

[0146] S12. Incremental synchronization of the cardiology-related data is achieved using a dual mechanism of timestamp and data version number, extracting only data that has been added or modified since the last data synchronization, reducing network transmission volume and processing time; and performing full synchronization on a daily basis to ensure data consistency.

[0147] The timestamp mechanism achieves incremental synchronization by recording the last update time of each data, and the data version number maintains a version counter for each data record.

[0148] In this embodiment, the dual mechanism of timestamp mechanism and data version number is as follows:

[0149] 1. Add the last_updated_time (timestamp) and version_number (version number) fields to the patient table;

[0150] 2. Each time data is synchronized, execute the query: SELECT * FROM patient_table WHERE last_updated_time > '2023-05-10 15:30:00' OR (last_updated_time = '2023-05-10 15:30:00' AND version_number > 35);

[0151] 3. When data is updated but the time accuracy is not enough to distinguish the order (such as multiple updates within a millisecond), the version number ensures that the data update is not lost;

[0152] 4. Perform full synchronization verification at 23:00 every day: Ensure data consistency by comparing the number of records and key field checksums in the source and target systems.

[0153] S13. Perform data cleaning and processing, using regular expressions to identify and repair malformed data (including irregular date formats and special characters); use an outlier detection algorithm based on business rules to mark test results outside the normal range as abnormal and await manual confirmation; apply fuzzy matching algorithms to handle information differences of the same patient in different medical data source systems and build a unified patient index;

[0154] S14. Convert disease codes in different medical data source systems to the ICD-10 standard and surgical codes to the ICD-9-CM-3 standard to ensure consistency and comparability of data analysis.

[0155] S2. Design a calculation rule for the cardiology department's workload, and calculate each doctor's workload based on the calculation rule. The workload covers multiple dimensions including outpatient care, hospitalization, surgery, and auxiliary examinations, including the following steps:

[0156] Design an outpatient workload algorithm to calculate outpatient workload. The outpatient workload algorithm includes: calculating the actual number of patients seen by each doctor based on data extracted from the outpatient clinic; setting different weight coefficients for specialist clinics and general clinics (the weight of specialist clinics is 1.5, and the weight of general clinics is 1.0); setting different workload coefficients for initial visits and follow-up visits (the initial visit coefficient is 1.2, and the follow-up visit coefficient is 0.8);

[0157] Designing an inpatient workload algorithm to calculate the inpatient workload, the algorithm includes: using the formula: responsibility coefficient × bed days × bed weight = inpatient workload to calculate the inpatient workload; using the Charlson comorbidity index to assess and dynamically adjust the workload coefficient based on the complexity of the patient's condition, with the maximum workload coefficient for complex cases being 2.0; the attending physician receives 100% of the workload score, and the deputy attending physician receives 60% of the workload score;

[0158] Design a surgical workload algorithm to calculate the surgical workload. The algorithm includes: setting a base score based on the difficulty level of the surgery, with level I-IV surgeries corresponding to base scores of 10, 20, 30, and 40, respectively; for every 30 minutes of surgery exceeding the average duration, the workload increases by 10%; the lead surgeon receives 100% of the workload, the first assistant surgeon receives 80%, and the second assistant surgeon receives 50%;

[0159] A workload calculation rule for cardiology-specific examination items was designed to calculate the workload of auxiliary examinations. The basic score for echocardiography was 15 points, and 5 points were added for each additional special function examination (including tissue Doppler and strain rate analysis). The 12-lead electrocardiogram was 5 points, the Holter examination was 15 points, and the basic score for electrophysiological examination was 30 points, with 20 points added for ablation treatment.

[0160] The present invention can obtain key indicator data through an interface or manual input by setting annual performance scoring dimensions and scoring standards and calculation rules for each dimension, and realize performance scoring calculation in conjunction with an algorithm. For example, the medical scoring algorithm: based on the data extracted from the outpatient, surgical, and inpatient modules, obtain the numerical values ​​of each doctor's surgical volume, outpatient volume, CMI, turnover rate, consumption ratio, drug ratio and other indicators, assign a weight to each indicator, and calculate the final performance score of the medical dimension; scientific research scoring algorithm: based on the entered topics, articles, patents, awards and other indicators, assign scoring standards and calculation rules to each indicator, such as 10 points / item for national invention patents, 10 points / item for national awards, and 5 points / item for provincial and ministerial awards, and calculate the scientific research score. The scores of each dimension are weighted and calculated to obtain the annual performance score.

[0161] S3. Integrate medical quality indicators and conduct a comprehensive evaluation of doctors’ work quality as an important dimension coefficient for performance calculation, including the following steps (such as Figure 3 shown):

[0162] S31. Integrate hospital satisfaction survey and patient complaint data, calculate physician satisfaction index, and conduct patient satisfaction analysis;

[0163] A satisfaction index of 90 or higher is considered excellent, and the coefficient for performance calculation is 1.1;

[0164] A satisfaction index of 80-89 is considered good, and the coefficient for performance calculation is 1.0;

[0165] A satisfaction index of 70-79 is classified as average, and the coefficient for performance calculation is 0.9;

[0166] The satisfaction index <70 is considered poor, and the coefficient of performance calculation is 0.8;

[0167] S32. The three indicators of outcome assessment, document quality and satisfaction are weighted in the ratio of 5:3:2 to obtain the comprehensive quality score of the doctor, which is used as the workload adjustment coefficient.

[0168] S4. Monitor and analyze the indicator data of the overall operation of the cardiology department to provide data support for department management decisions, including the following steps (such as Figure 4 shown):

[0169] S41. Calculate average length of stay, bed turnover rate, and bed occupancy rate indicators, set warning values ​​to highlight data exceeding the standards, analyze key factors affecting bed efficiency, including patient composition and physician allocation, and conduct bed efficiency analysis.

[0170] S42. Calculate department CMI values ​​based on DRG grouping to assess the severity of patient diseases and resource consumption levels; use Activity-Based Costing (activity-based cost calculation) methods to accurately calculate the costs and benefits of different diseases; integrate drug, consumable, and examination fee data, calculate the economic indicators of drug ratio and consumable ratio, conduct economic indicator analysis, monitor indicator changes through year-on-year and month-on-month analysis, and highlight data exceeding the standard by setting early warning values.

[0171] S5. Provide intelligent decision-making recommendations based on the results of indicator data monitoring and analysis to optimize resource allocation, including: using linear programming algorithms to generate optimal physician scheduling plans based on historical outpatient volume and seasonal variation patterns; taking into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization efficiency while ensuring medical quality; monitoring key indicators based on statistical anomaly detection algorithms, and automatically triggering risk warnings when indicators exceed preset thresholds;

[0172] S6. Present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display, including:

[0173] Dynamically generate personalized, adaptive dashboards based on user roles and preferences. Use Ant Design to implement highly interactive data visualization components, supporting drag-and-drop adjustments, drill-down analysis, and conditional filtering. Preset data views for common analysis scenarios (including physician workload rankings, monthly department trends, etc.) can be switched with one click.

[0174] Use a variety of visual chart types to display multi-dimensional data, including line charts (displaying time trends), bar charts (displaying category comparisons), heat maps (displaying multi-dimensional cross-analysis), radar charts (displaying comprehensive scores), etc., and support interactive linkage between charts. Click an element in a chart, and the related charts will automatically update to display related data.

[0175] Provides a variety of report templates and supports generating standard reports based on different cycles such as daily, weekly, monthly, quarterly, and annually. Reports can be exported to multiple formats including Excel, PDF, and Word to meet the needs of different scenarios.

[0176] This comprehensive intelligent monitoring and evaluation management method for clinical department performance also provides system configuration, permission management, and log auditing functions to ensure the safe and stable operation of the system, including:

[0177] Provides role-based permission management, adopts the RBAC (Role-Based Access Control) model to achieve refined permission control, sets different roles for administrators, department directors, doctors, and data analysts, and each role has different functional permissions and data access scopes; supports custom roles and permission matrices, and flexible configuration of system functions;

[0178] Provide data security, use AES-256 encryption algorithm to protect sensitive data storage security, implement data access audit function, and record all access operations of sensitive data, including visitor, access time, and access content;

[0179] A unified configuration management interface is provided to support administrators in adjusting system parameters, performance calculation rules, and key indicator warning value settings. Configuration modifications support version control, enabling the review of historical configurations and rollback when necessary. Key configuration changes require multi-level approval to prevent misoperations from affecting the normal operation of the system.

[0180] The embodiment of the present invention further provides a comprehensive intelligent monitoring and evaluation management system for clinical department performance, which is used to implement the comprehensive intelligent monitoring and evaluation management method for clinical department performance as described above, including:

[0181] Data collection and integration module: used to develop data interfaces for connecting to HIS systems and performance management systems, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion, and integration to achieve medical data standardization and build a unified data view;

[0182] Doctor workload and performance score calculation module: used to design calculation rules for cardiology department workload, and calculate the workload of each doctor according to the calculation rules. The workload covers multiple dimensions including outpatient, hospitalization, surgery, and auxiliary examinations;

[0183] Quality evaluation module: used to integrate medical quality indicators and conduct a comprehensive assessment of doctors' work quality, serving as an important dimension coefficient for performance calculation;

[0184] Department operation analysis module: used to monitor and analyze the indicator data of the overall operation of the cardiology department;

[0185] Decision Support Module: This module provides intelligent decision-making recommendations based on the results of indicator data monitoring and analysis, optimizing resource allocation. This module uses a linear programming algorithm to generate optimal physician scheduling based on historical outpatient volume and seasonal variation patterns. It also takes into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization while ensuring medical quality. It also monitors key indicators using a statistical anomaly detection algorithm, automatically triggering risk warnings when indicators exceed preset thresholds.

[0186] Visual display module: used to present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display.

[0187] In actual use of this embodiment of the comprehensive intelligent monitoring and evaluation management system for clinical department performance, users access the system login page through a browser and enter their username and password for authentication. After verifying the user's identity, the system displays a functional interface with corresponding permissions based on the user's role (administrator, department director, general practitioner, etc.). Upon system startup, the system automatically executes pre-set data synchronization tasks to obtain the latest data from the hospital information system. Users can view an overview of key performance indicators on the main interface and navigate to detailed functional modules through the menu.

[0188] This embodiment innovatively designs multi-source heterogeneous data integration technology for cardiology, which can automatically collect and integrate data from different systems such as outpatient, inpatient, surgical, and auxiliary examinations (echocardiography, electrocardiography, electrophysiology), build a unified cardiology data view, and solve the data silo problem.

[0189] This embodiment designs an intelligent workload assessment model based on the characteristics of cardiology, which comprehensively considers multiple dimensions such as diagnosis and treatment volume, technical difficulty, time consumption, and academic contribution. It can objectively and impartially evaluate the workload of doctors and provide a scientific basis for performance management.

[0190] This embodiment develops a key operational indicator analysis system specifically for the cardiology department, which can automatically calculate and monitor indicators such as average length of stay, bed occupancy rate, profit and loss rate, consumption ratio, and drug ratio, providing comprehensive support for department operation management.

[0191] This embodiment proposes an algorithm for optimizing the allocation of cardiology department resources. Based on historical data and prediction results, it provides optimization suggestions for doctor scheduling, bed allocation, etc., thereby improving resource utilization efficiency.

[0192] This embodiment designs a data visualization display system tailored for cardiology, which can present complex medical data in an intuitive and easy-to-understand manner and support multi-dimensional data display and interaction.

[0193] An embodiment of the present invention further provides a computer device, Figure 5 This is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention; see the accompanying drawings Figure 5 As shown, the computer device includes: an input system 23, an output system 24, a memory 22 and a processor 21; the memory 22 is used to store one or more programs; when the one or more programs are executed by the one or more processors 21, the one or more processors 21 implement the clinical department performance panoramic intelligent monitoring and evaluation management method provided in the above embodiment; wherein the input system 23, the output system 24, the memory 22 and the processor 21 can be connected by a bus or other means, Figure 5 The bus connection is taken as an example.

[0194] The memory 22 is a readable and writable storage medium of a computing device and can be used to store software programs and computer executable programs, such as program instructions corresponding to the method for comprehensive intelligent monitoring and evaluation management of clinical department performance according to an embodiment of the present invention. The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the device, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some examples, the memory 22 may further include a memory remotely located relative to the processor 21, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] The input system 23 may be used to receive input digital or character information, and generate key signal input related to user settings and function control of the device; the output system 24 may include display devices such as a display screen.

[0196] The processor 21 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 22, thereby realizing the above-mentioned panoramic intelligent monitoring and evaluation management method of clinical department performance.

[0197] The computer device provided above can be used to execute the panoramic intelligent monitoring and evaluation management method of clinical department performance provided in the above embodiment, and has corresponding functions and beneficial effects.

[0198] An embodiment of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute the method for intelligent monitoring and evaluation management of the performance of a clinical department as provided in the above embodiment. The storage medium is any of various types of memory devices or storage devices, including: installation media, such as CD-ROM, floppy disk or tape system; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements; the storage medium may also include other types of memory or a combination thereof; in addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, the second computer system being connected to the first computer system via a network (such as the Internet); the second computer system may provide program instructions to the first computer for execution. The storage medium includes two or more storage media that can reside in different locations (for example, in different computer systems connected via a network). The storage medium can store program instructions (for example, specifically implemented as a computer program) that can be executed by one or more processors.

[0199] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present invention is not limited to the panoramic intelligent monitoring and evaluation management method for clinical department performance as described in the above embodiment, and can also execute relevant operations in the panoramic intelligent monitoring and evaluation management method for clinical department performance provided in any embodiment of the present invention.

[0200] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0201] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for comprehensive intelligent monitoring and evaluation of clinical department performance, characterized by: The following steps are involved: S1. Develop data interfaces for connecting to HIS systems and performance management systems, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion, and integration to achieve medical data standardization and build a unified data view; S2. Design a calculation rule for the cardiology department's workload, and calculate each doctor's workload based on the calculation rule. The workload covers multiple dimensions including outpatient care, hospitalization, surgery, and auxiliary examinations; S3. Integrate medical quality indicators to comprehensively evaluate the quality of doctors' work and use them as an important dimension coefficient for performance calculation; S4. Monitor and analyze the indicator data of the overall operation of the cardiology department; S5. Provide intelligent decision-making recommendations based on the results of indicator data monitoring and analysis to optimize resource allocation, including: using linear programming algorithms to generate optimal physician scheduling plans based on historical outpatient volume and seasonal variation patterns; taking into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization efficiency while ensuring medical quality; monitoring key indicators based on statistical anomaly detection algorithms, and automatically triggering risk warnings when indicators exceed preset thresholds; S6. Present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display.

2. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1 is characterized in that: The S1 step includes: S11. Develop data interfaces for connecting to the hospital's HIS, LIS, PACS, and EMR systems based on the HL7 standard, and extract cardiology-related data from multiple medical data source systems using Web Services, database links, and file transfers. S12. Incremental synchronization of the cardiology-related data is achieved using a dual mechanism of timestamp and data version number, extracting only data that has been added or modified since the last data synchronization, reducing network transmission volume and processing time; and performing full synchronization on a daily basis to ensure data consistency. S13. Perform data cleaning and processing, using regular expressions to identify and repair malformed data, and employing an outlier detection algorithm based on business rules to mark test results outside the normal range as abnormal for manual confirmation. Fuzzy matching algorithms are used to process information differences of the same patient in different medical data source systems and construct a unified patient index. S14. Convert disease codes in different medical data source systems to the ICD-10 standard and surgical codes to the ICD-9-CM-3 standard to ensure consistency and comparability of data analysis.

3. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1 is characterized in that: The S2 step includes: Design an outpatient workload algorithm to calculate the outpatient workload. The outpatient workload algorithm includes: calculating the actual number of patients seen by each doctor based on the data extracted from the outpatient clinic; setting different weight coefficients for specialist clinics and general clinics, and setting different workload coefficients for initial visits and follow-up visits; Designing an inpatient workload algorithm to calculate the inpatient workload, the inpatient workload algorithm comprising: using the formula: responsibility system coefficient × bed day × bed weight = inpatient workload to calculate the inpatient workload; dynamically adjusting the workload coefficient based on the complexity of the patient's condition, with the maximum workload coefficient for complex cases being 2.0; Design a surgical workload algorithm to calculate the surgical workload, which includes: setting a base score based on the surgical difficulty level; if the surgical duration exceeds the average time, the workload is increased by 10% for every 30 minutes of additional time; A workload calculation rule is specially designed for cardiology-specific examination items to calculate the workload of auxiliary examinations. The basic score of echocardiography is 15 points, 5 points are added for each additional special function examination, 5 points are added for 12-lead electrocardiogram, 15 points for dynamic electrocardiogram, and 30 points for the basic score of electrophysiological examination, including 20 points for ablation treatment.

4. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1, characterized in that: The S3 step includes: S31. Integrate hospital satisfaction survey and patient complaint data, calculate physician satisfaction index, and conduct patient satisfaction analysis; A satisfaction index of 90 or higher is considered excellent, and the coefficient for performance calculation is 1.1; A satisfaction index of 80-89 is considered good, and the coefficient for performance calculation is 1.0; A satisfaction index of 70-79 is classified as average, and the coefficient for performance calculation is 0.9; The satisfaction index <70 is considered poor, and the coefficient of performance calculation is 0.8; S32. The three indicators of outcome assessment, document quality and satisfaction are weighted in the ratio of 5:3:2 to obtain the comprehensive quality score of the doctor, which is used as the workload adjustment coefficient.

5. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1 is characterized in that: The S4 step includes: S41. Calculate the average length of stay, bed turnover rate, and bed occupancy rate indicators, set warning values ​​to highlight data exceeding the standards, analyze the key factors affecting bed efficiency, and conduct bed efficiency analysis; S42. Calculate department CMI values ​​based on DRG grouping to assess the severity of patient diseases and resource consumption levels; use the Activity-Based Costing method to accurately calculate the costs and benefits of different diseases; integrate drug, consumable, and examination fee data to calculate the economic indicators of drug ratio and consumable ratio, conduct economic indicator analysis, monitor indicator changes through year-on-year and month-on-month analysis, and highlight data exceeding the standard by setting early warning values.

6. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1 is characterized in that: The S6 step includes: Dynamically generate personalized, adaptive dashboards based on user roles and preferences; use Ant Design to implement highly interactive data visualization components, supporting drag-and-drop adjustments, drill-down analysis, and conditional filtering; preset data views for common analysis scenarios, with one-click switching; Use a variety of visual chart types to achieve multi-dimensional data display, support chart interaction and linkage, click an element in a chart, and the associated chart will automatically update to display related data; Provides a variety of report templates and supports generating standard reports based on different cycles such as daily, weekly, monthly, quarterly, and annually. Reports can be exported to multiple formats including Excel, PDF, and Word to meet the needs of different scenarios.

7. The method for comprehensive intelligent monitoring and evaluation of clinical department performance according to claim 1 is characterized in that: Also includes: Provides system configuration, permission management, and log auditing functions to ensure safe and stable system operation, including: Provides role-based permission management, adopts RBAC model to achieve refined permission control, sets different roles for administrators, department directors, doctors, and data analysts, and each role has different functional permissions and data access scope; supports customized roles and permission matrix, and flexible configuration of system functions; Provide data security, use AES-256 encryption algorithm to protect sensitive data storage security, implement data access audit function, and record all access operations of sensitive data, including visitor, access time, and access content; A unified configuration management interface is provided to support administrators in adjusting system parameters, performance calculation rules, and key indicator warning value settings. Configuration modifications support version control, enabling the review of historical configurations and rollback when necessary. Key configuration changes require multi-level approval to prevent misoperations from affecting the normal operation of the system.

8. A panoramic intelligent monitoring and evaluation management system for clinical department performance, used to implement the panoramic intelligent monitoring and evaluation management method for clinical department performance according to any one of claims 1 to 7, characterized in that: include: Data collection and integration module: used to develop data interfaces for connecting to HIS systems and performance management systems, obtain cardiology source data from multiple medical data source systems, and perform data cleaning, conversion, and integration to achieve medical data standardization and build a unified data view; Doctor workload and performance score calculation module: used to design calculation rules for cardiology department workload, and calculate the workload of each doctor according to the calculation rules. The workload covers multiple dimensions including outpatient, hospitalization, surgery, and auxiliary examinations; Quality evaluation module: used to integrate medical quality indicators and conduct a comprehensive assessment of doctors' work quality, serving as an important dimension coefficient for performance calculation; Department operation analysis module: used to monitor and analyze the indicator data of the overall operation of the cardiology department; Decision Support Module: This module provides intelligent decision-making recommendations based on the results of indicator data monitoring and analysis, optimizing resource allocation. This module uses a linear programming algorithm to generate optimal physician scheduling based on historical outpatient volume and seasonal variation patterns. It also takes into account physician expertise, workload balance, and vacation arrangements to maximize resource utilization while ensuring medical quality. It also monitors key indicators using a statistical anomaly detection algorithm, automatically triggering risk warnings when indicators exceed preset thresholds. Visual display module: used to present monitoring and analysis results to users through an intuitive and interactive interface, supporting multi-dimensional data display.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the steps of the method for panoramic intelligent monitoring and evaluation management of clinical department performance described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for panoramic intelligent monitoring and evaluation management of clinical department performance as described in any one of claims 1 to 7 are implemented.

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