Hospital operation management decision system and method based on artificial intelligence

Abstract

The invention relates to the technical field of medical informatization management, and discloses a hospital operation management decision-making system and method based on artificial intelligence, and the system comprises a data collection and integration module which collects the operation data of outpatient service, inpatient department, pharmacy and the like in real time, and integrates the external data of patient behaviors, market trends and the like; the intelligent analysis module performs deep analysis based on a machine learning algorithm, and covers cost optimization, process efficiency and resource allocation optimization; and the decision support module generates a visual report containing a chart and an instrument panel and a suggestion scheme. According to the system and method, intelligent analysis and decision support of hospital operation data are realized, and the management efficiency and resource allocation rationality are improved. The system has the advantages that internal and external data resources of a hospital are integrated, deep analysis and optimization are carried out by utilizing a machine learning algorithm, and managers are helped to realize precise and intelligent operation decisions.

Description

Technical Field

[0001] This invention relates to the field of medical information management technology, specifically to a hospital operation management decision-making system and method based on artificial intelligence. Background Technology

[0002] Against the backdrop of digital transformation in the healthcare industry, hospital operations management faces challenges such as data fragmentation and low decision-making efficiency. Traditional hospital management models rely heavily on manual statistical reports and experience-based judgment, making it difficult to integrate data from multiple departments, including outpatient clinics, inpatient wards, and pharmacies, in real time, resulting in prominent information silos. For example, fluctuations in outpatient traffic and inpatient bed utilization rates cannot be linked for analysis, and drug inventory turnover rates lack dynamic matching with patient medication needs, leading to persistent problems such as irrational resource allocation and cost waste. Simultaneously, data on external market trends and patient behavior habits are not effectively integrated into operational decisions, making it difficult for managers to cope with the impact of external variables such as seasonal peaks in patient visits and adjustments to medical insurance policies, thus hindering improvements in hospital operational efficiency.

[0003] While some hospitals have implemented information management systems, these systems often remain at the level of data recording and basic statistics, lacking in-depth analytical capabilities based on artificial intelligence. For example, traditional cost analysis can only generate expense reports and cannot automatically identify hidden cost waste; process optimization relies on manual surveys, making it difficult to accurately pinpoint service bottlenecks; and resource allocation models lack predictive capabilities, failing to adapt to future demand changes. Furthermore, decision support tools are mostly static reports, lacking visual interaction and dynamic adjustment suggestions, making it difficult for managers to quickly formulate optimal strategies. Therefore, there is an urgent need for a hospital operation management system that integrates artificial intelligence technology to achieve end-to-end intelligent data integration, intelligent analysis, and decision support, thereby improving the hospital's lean management level. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes an AI-based hospital operation management decision-making system and method. This system integrates internal and external hospital data resources, utilizes machine learning algorithms for in-depth analysis and optimization, and helps managers achieve precise and intelligent operational decisions. It solves problems such as information silos, delayed analysis, and crude decision-making in traditional management models, thereby improving the overall operational efficiency and management level of hospitals.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is: a hospital operation management decision-making system based on artificial intelligence, comprising:

[0006] The data acquisition and integration module is used to collect operational data from various departments of the hospital in real time and integrate external data.

[0007] The intelligent analysis module performs in-depth analysis of the integrated data based on machine learning algorithms.

[0008] The decision support module generates visual reports and suggested solutions based on the results of intelligent analysis.

[0009] Preferably, the in-depth analysis of the intelligent analysis module includes cost optimization analysis, identifying cost waste points and unreasonable expenditures, and proposing optimization suggestions.

[0010] Preferably, the in-depth analysis of the intelligent analysis module includes process efficiency analysis, which identifies bottlenecks in the medical service process and provides improvement suggestions.

[0011] Preferably, the in-depth analysis of the intelligent analysis module includes resource allocation optimization, which optimizes the allocation of medical resources based on historical data and predictive models.

[0012] Preferably, the visualization report generated by the decision support module includes charts and dashboards.

[0013] Preferably, the data collected by the data acquisition and integration module includes operational data from outpatient departments, inpatient departments, and pharmacies.

[0014] Preferably, the external data integrated by the data acquisition and integration module includes patient behavior data and market trend data.

[0015] An AI-based hospital operations management decision-making method, characterized by the following steps:

[0016] Data collection and integration: Real-time data collection from various departments of the hospital is achieved through interface technology, and the data is then integrated with external data sources.

[0017] Intelligent analysis utilizes machine learning algorithms to perform in-depth analysis of the integrated data;

[0018] Decision support: Based on intelligent analysis results, it generates visual reports and suggested solutions and presents them to managers through a user-friendly interface.

[0019] Preferably, the in-depth analysis in the intelligent analysis includes at least one of cost optimization analysis, process efficiency analysis, or resource allocation optimization.

[0020] The advantages of this invention compared to the prior art are:

[0021] Data integration and efficiency improvement: Breaking down information silos within hospitals, achieving real-time integration and unified management of multi-source data, and improving decision-making efficiency by more than 60%;

[0022] Intelligent analysis and precise decision-making: By mining the deep value of data through machine learning algorithms, the accuracy rate of cost waste identification reaches 90%, and the error of process bottleneck location is less than 10%.

[0023] Resource optimization and cost control: Based on predictive models, dynamic allocation of medical resources is achieved, increasing bed utilization by 15%-20% and drug inventory turnover by 25%;

[0024] Visualized interaction and agile response: Key operational metrics are displayed in real time through dashboards, enabling managers to make decisions within 30 minutes, reducing time by 80% compared to traditional methods. Detailed Implementation

[0025] Example 1

[0026] Specific implementation of system architecture

[0027] I. Data Collection and Integration

[0028] The system collects real-time operational data from departments such as outpatient clinics, inpatient wards, and pharmacies via standard interfaces, including patient flow, bed usage, and drug consumption. It also integrates external patient behavior data, market trend data, and medical insurance policy data. Data cleaning techniques are used to handle missing and outlier values, and anonymization techniques are employed to protect patient privacy, resulting in a multi-dimensional unified dataset.

[0029] II. Intelligent Analysis Applications

[0030] In-depth analysis of integrated data based on machine learning algorithms: Identifying major cost items such as operating room equipment depreciation and high-value consumable consumption through ABC costing, and proposing procurement optimization strategies; using process mining algorithms to locate bottlenecks in the outpatient examination appointment process, and simulating optimization to shorten waiting time from 4.2 hours to 2.5 hours; using LSTM models to predict bed demand, combined with genetic algorithms to dynamically allocate resources, reducing waiting time for elective surgical procedures by 45%.

[0031] III. Decision Support Implementation

[0032] Develop and operate a dashboard-style visual interface that displays key indicators such as bed utilization and drug cost trends in real time, supporting multi-dimensional drill-down analysis. It automatically generates suggested documents such as "High-Value Consumables Management Plan" and "Outpatient Process Optimization Gantt Chart," allowing managers to simulate resource allocation plans through interactive dashboards, improving decision-making efficiency by over 60%.

[0033] Example 2

[0034] Optimization of the implementation process

[0035] I. Data Collection and Governance

[0036] Data is synchronized with the hospital's HISLIS system via a customized interface, collecting internal data such as registration records, medical orders, and surgery schedules, while also incorporating external variables such as weather and traffic data. A star schema is used to construct the data warehouse, combining daily full updates with real-time incremental data collection to ensure data latency is controlled within 5 minutes, providing a real-time and accurate data foundation for subsequent analysis.

[0037] II. Intelligent Analysis and Modeling

[0038] A cost anomaly detection model was built using the random forest algorithm, achieving an accuracy of 91% and identifying an anomaly where the drug ratio in a certain department exceeded the standard by 12%. By modeling the outpatient process using Petri nets, a bottleneck problem was found where the time spent in medical technology examinations accounted for 35%. An equipment utilization prediction model was built based on the XGBoost algorithm, with an accuracy of 85% in predicting the probability of idle time in the next hour, providing a scientific basis for equipment scheduling.

[0039] III. Closed-Loop Management of Decision-Making

[0040] The system generates a "Weekly Operational Analysis Report" containing trend predictions and anomaly alerts for key indicators, which is then pushed to managers via a B / S architecture interface. A decision-making task allocation mechanism has been established, such as automatically assigning inspection process optimization tasks to the IT and laboratory departments. The system tracks task progress in real time and automatically reminds users before deadlines, ensuring a 100% decision implementation rate and reducing the average decision-making cycle from 24 hours to 30 minutes.

[0041] Example 3

[0042] Application scenarios for bed resource optimization

[0043] I. Data Integration and Forecasting

[0044] By collecting bed occupancy data and surgical scheduling information from the inpatient department over the past year, and combining this data with winter climate data and regional referral demand, we found that the number of hospitalizations for cardiovascular and cerebrovascular diseases increases by 40% in winter, and that the referral demand from orthopedic departments of surrounding hospitals is positively correlated with the hospital's bed utilization rate. Using the Prophet algorithm, we predicted the department's bed demand for the next 7 days, achieving a prediction accuracy of 92% for the cardiology department, outputting a demand range of 28-32 beds per day.

[0045] II. Resource Allocation and Optimization

[0046] Based on a mixed-integer programming model, bed allocation was optimized with patient satisfaction and operating costs as dual objectives. During the winter, the proportion of internal medicine beds was increased from 35% to 42%, and 5 spare beds were reserved for orthopedics. The operating room schedule was adjusted to prioritize day surgeries, increasing the bed turnover rate from 1.2 times / month to 1.5 times / month and shortening the average length of stay by 1.3 days.

[0047] III. Implementation Results and Benefits

[0048] After optimization, the waiting time for internal medicine patients to be admitted to the hospital decreased from 4.3 days to 2.1 days, and the waiting time for orthopedic referral surgeries was shortened from 7 days to 3 days, with patient satisfaction increasing to 92%. The overall hospital bed utilization rate increased from 78% to 89%, saving approximately 3 million yuan in annual operating costs. At the same time, a regional bed-sharing mechanism was established, which is expected to generate an additional 2 million yuan in annual revenue.

[0049] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A hospital operation management decision-making system based on artificial intelligence, characterized in that, include: The data acquisition and integration module is used to collect operational data from various departments of the hospital in real time and integrate external data. The intelligent analysis module performs in-depth analysis of the integrated data based on machine learning algorithms. The decision support module generates visual reports and suggested solutions based on the results of intelligent analysis.

2. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis module's in-depth analysis includes cost optimization analysis, identifying cost waste points and unreasonable expenditures, and proposing optimization suggestions.

3. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis module's in-depth analysis includes process efficiency analysis, identifying bottlenecks in the medical service process and providing improvement suggestions.

4. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis module's in-depth analysis includes resource allocation optimization, which optimizes the allocation of medical resources based on historical data and predictive models.

5. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The decision support module generates visual reports including charts and dashboards.

6. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The data collection and integration module collects data from various departments of the hospital, including operational data from outpatient clinics, inpatient wards, and pharmacies.

7. The hospital operation management decision-making system based on artificial intelligence according to claim 1, characterized in that: The external data integrated by the data acquisition and integration module includes patient behavior data and market trend data.

8. The hospital operation management decision-making method based on artificial intelligence as described in claim 1, characterized in that: Includes the following steps: Data collection and integration: Real-time data collection from various departments of the hospital is achieved through interface technology, and the data is then integrated with external data sources. Intelligent analysis utilizes machine learning algorithms to perform in-depth analysis of the integrated data; Decision support: Based on intelligent analysis results, it generates visual reports and suggested solutions and presents them to managers through a user-friendly interface.

9. The hospital operation management decision-making method based on artificial intelligence according to claim 8, characterized in that: The in-depth analysis in the intelligent analysis includes at least one of cost optimization analysis, process efficiency analysis, or resource allocation optimization.