Hospital operation monitoring analysis method and platform based on artificial intelligence

Abstract

The invention provides a hospital operation monitoring analysis method and platform based on artificial intelligence, and the method comprises the steps: collecting data from a plurality of data sources of a hospital, and integrating the multi-source heterogeneous data into a unified data platform through a data access gateway; performing cleaning, standardization and feature extraction on the acquired data, and extracting key features by using automatic feature engineering; constructing an AI model according to a hospital operation scene, and training and verifying the model by using historical data; inputting the real-time data into an AI model for analysis, generating a prediction result or a classification label, and performing real-time early warning on an abnormal condition; key indexes and analysis results are displayed through a visual instrument panel, and an operation report is automatically generated. Through the artificial intelligence technology platform, hospital resource use conditions can be analyzed in real time, resource allocation can be dynamically optimized, medical staff scheduling and equipment use plans can be automatically adjusted, waiting time of patients is shortened, and scheduling efficiency and response speed are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of medical service technology, and in particular to a hospital operation monitoring and analysis method and platform based on artificial intelligence. Background Art

[0002] With the rapid development of the medical industry, hospital operations management faces increasingly complex challenges. Traditional management models rely on manual experience and decentralized information systems, making it difficult to meet the needs of integrating and analyzing multi-source heterogeneous data. For example, patient flow, drug inventory, equipment status, and financial data are scattered in different systems, lacking a unified management platform, resulting in serious data silos. In addition, resource scheduling relies on manual decision-making, which is inefficient and difficult to respond to emergencies. Drug inventory management relies heavily on manual inventory counting, which is prone to insufficient or excessive inventory, affecting the efficiency of medical services. Medical quality monitoring mostly uses post-analysis, which makes it difficult to detect problems and make improvements in a timely manner. Existing technologies such as traditional statistical methods and rule engines can solve some problems, but they are difficult to handle complex nonlinear relationships and high-dimensional data, and lack real-time and intelligent support.

[0003] Therefore, in order to solve the above problems, a hospital operation monitoring and analysis method and platform based on artificial intelligence is now developed. Summary of the invention

[0004] In order to overcome the shortcomings of existing technologies such as traditional statistical methods and rule engines, which can solve some problems but are difficult to handle complex nonlinear relationships and high-dimensional data, and lack real-time and intelligent support, the present invention provides a hospital operation monitoring and analysis method and platform based on artificial intelligence.

[0005] The technical solution of the present invention is: a hospital operation monitoring and analysis method based on artificial intelligence, comprising: Data collection and integration steps: Collect data from multiple data sources in the hospital, including electronic medical record systems, medical equipment sensors, drug inventory management systems, and financial systems, and integrate multi-source heterogeneous data into a unified data platform through a data access gateway; Data preprocessing steps: Clean, standardize and extract features from the collected data, including removing noise data, filling missing values, unifying data formats and units, and extracting key features using automated feature engineering; AI model construction and training steps: Build AI models based on hospital operation scenarios, including patient flow prediction models, resource scheduling optimization models, drug inventory early warning models, and medical quality monitoring models, and use historical data to train and verify the models; Real-time monitoring and analysis steps: input real-time data into the AI ​​model for analysis, generate prediction results or classification labels, and issue real-time warnings for abnormal situations, display the patient flow of each department in real time, issue warnings for departments that exceed their carrying capacity, monitor the status of ICU monitors and operating room equipment in real time, issue warnings for abnormal situations, display the inventory status of drugs in real time, issue warnings for drugs that are about to be in short supply, monitor medical insurance settlement data in real time, and issue warnings for abnormal settlements; Visualization and report generation steps: Display the real-time patient flow, predicted flow and historical comparison data of each department, display the operating status, utilization rate and fault records of key equipment, display the drug inventory, consumption trend and early warning information, display the real-time data and historical trends of income, expenditure and medical insurance settlement, and generate daily operation reports, including patient flow, equipment utilization rate, drug inventory and financial data.

[0006] As a preferred embodiment of the present invention, in the AI ​​model construction and training steps, the patient flow prediction model adopts a time series algorithm, the resource scheduling optimization model adopts a reinforcement learning algorithm, the drug inventory warning model adopts a regression algorithm, and the medical quality monitoring model adopts a classification algorithm.

[0007] As a preferred embodiment of the present invention, in the real-time monitoring and analysis step, a stream processing framework is used to process real-time data, and a dynamic threshold is set to warn of abnormal situations.

[0008] As a preferred embodiment of the present invention, in the visualization and report generation step, patient flow, equipment status, drug inventory and financial data are displayed through a multi-terminal visualization dashboard, and daily, weekly and monthly operation reports are automatically generated.

[0009] As a preferred embodiment of the present invention, in the data collection and integration step, edge computing technology is used to perform preliminary processing on the medical device sensor data, including noise reduction and data compression, and real-time data collection and integration is achieved through a multi-protocol data access gateway.

[0010] As a preferred embodiment of the present invention, in the data preprocessing step, a missing value completion algorithm based on deep learning is used to clean the data, and AutoML technology is used to automatically extract key features.

[0011] As a preferred embodiment of the present invention, the patient flow prediction model respectively calculates the forgotten state data, input state data, candidate supplementary data, update data, output state data and hidden state data as follows: ; ; ; ; ; ; in, To forget state data, that is, to decide which historical patient flow information needs to be forgotten. For example, after the peak of seasonal flu has passed, the model can forget this part of information to avoid interference with future predictions. To input state data, the system decides which new patient flow information needs to be stored. Supplement data for candidates, that is, generate new patient flow information, for example, generate possible future patient flow trends based on current outpatient data and historical data, To update the data, that is, to combine historical information with current information and update the model's memory, for example, combining seasonal influenza and current outpatient data to update the prediction of future patient flow, To output status data, decide which information to use to predict future patient flow, such as outputting a forecast of outpatient patient flow for the next 24 hours. The hidden state data, that is, the output at the current moment, is used to predict the future patient flow, for example, output the patient flow prediction results for the next week. is the weight matrix used to adjust the input data, that is, the impact of patient flow on the forgetting state data, is the patient flow prediction result at the previous moment, is the current patient flow data, is the bias term for forgetting state data, is the activation function, which maps the output into probability data. Represents the weight matrix, which is used to adjust the impact of input data on input state data. is the bias term of the input state data, is the weight matrix of candidate input data, which is used to adjust the influence of input data on candidate input data. is the bias term of the candidate input data, is the updated data of the previous moment, indicating the historical patient flow information. is the weight matrix of the output state data, is the bias term for the output state data.

[0012] As a preferred embodiment of the present invention, the resource scheduling optimization model is calculated by maximizing the cumulative reward formula and the loss function, and the formula is as follows: ; ; in, Indicates that the scheduling action is executed under the current hospital resource status s (such as operating room occupancy rate, number of ICU beds available) (such as allocating operating rooms and adjusting the shifts of medical staff), It indicates the immediate effect after executing the scheduling action. For example, after the operating room is allocated, the waiting time for surgery is reduced, and the reward value is positive. If the patient's waiting time increases due to unreasonable resource allocation, the reward value is negative. is a discount factor used to balance the importance of current rewards and future rewards, such as giving priority to the current utilization of operating rooms while taking into account the demand for future ICU beds. is the expected value, which represents the average of all possible situations. It represents the expected effect data of selecting the optimal scheduling action after the occupancy rate of the operating room changes. They represent the hospital resource status in the next state and the execution scheduling action, namely The next state of is the parameter of the current Q network, which is used to calculate the value of Q. Represents the target network parameters, which are used to calculate the target Q value. is the loss function, which measures the difference between the current Q value and the target Q value, that is, the gap between the current operating room allocation strategy and the optimal strategy.

[0013] An artificial intelligence-based hospital operation monitoring and analysis platform, including: Data layer, the data layer includes a data acquisition module and a data storage module. The data acquisition module supports multi-source data access, including electronic medical record system, medical equipment sensor, drug inventory management system and financial system. The data storage module uses a distributed data warehouse to store data and supports efficient query and analysis; The processing layer includes a data preprocessing module, an AI model engine, and a real-time stream processing module; The application layer includes a patient flow management module, a resource scheduling optimization module, a drug inventory management module, a medical quality monitoring module and a financial management module. The patient flow management module is used to monitor and predict patient flow in real time and optimize resource allocation. The resource scheduling optimization module is used to dynamically schedule resources including operating rooms and ICU beds. The drug inventory management module is used to monitor drug inventory in real time and provide inventory warnings and replenishment suggestions. The medical quality monitoring module is used to analyze patient satisfaction data and identify medical quality issues. The financial management module is used to monitor income, expenditure and medical insurance settlement data in real time and provide financial analysis reports.

[0014] As a preferred embodiment of the present invention, it also includes a user layer, which includes a visualization dashboard and a report generation module. The visualization dashboard provides a dual-end visualization interface for PC and mobile terminals to display key indicators and analysis results. The report generation module is used to automatically generate daily, weekly and monthly operation reports and supports exporting and sharing.

[0015] By adopting the above technical solution, the present invention has the following advantages: 1. The present invention can analyze the use of hospital resources in real time through an artificial intelligence technology platform, dynamically optimize resource allocation, and automatically adjust the medical staff schedule and equipment use plan when the patient flow surges, thereby reducing patient waiting time and improving resource utilization. Compared with traditional manual scheduling methods, it significantly improves scheduling efficiency and response speed.

[0016] 2. The present invention uses an algorithm to establish a model to predict drug consumption trends, and combines real-time inventory data to provide inventory warnings and replenishment suggestions to avoid drug shortages. At the same time, by optimizing inventory management, it reduces drug expiration and waste, reduces hospital operating costs, and improves the continuity of medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the steps of the hospital operation monitoring and analysis method based on artificial intelligence of the present invention.

[0018] Figure 2 It is a structural schematic diagram of the hospital operation monitoring and analysis platform based on artificial intelligence of the present invention.

[0019] Figure 3 It is a structural schematic diagram of the data layer of the present invention.

[0020] Figure 4 It is a schematic diagram of the structure of the processing layer of the present invention.

[0021] Figure 5 It is a schematic diagram of the structure of the application layer of the present invention.

[0022] Figure 6 It is a structural diagram of the user layer of the present invention. DETAILED DESCRIPTION

[0023] Reference to an embodiment herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] An artificial intelligence-based hospital operation monitoring and analysis method, such as Figure 1 As shown, including: Data collection and integration steps: Extract basic patient information, diagnosis results, and treatment plans from the electronic medical record system, obtain patient flow data from the registration system, collect the operating status and usage of ICU monitors, operating room equipment, and imaging equipment in real time through equipment sensors, obtain drug inbound, outbound, and inventory data from the drug inventory management system, obtain the usage of high-value consumables from the consumables management system, extract income, expenditure, and medical insurance settlement data from the financial system, and integrate the above data into a unified data platform through ETL tools to ensure centralized data management; Data preprocessing steps: remove duplicate registration records, complete and repair missing diagnostic information through historical data, unify the data format of different devices, classify according to drug category and consumable type, and check the accuracy of financial data. The specific inspection method is: compare the bills in the financial system with the registration, diagnosis, drug, consumable and other records to ensure the consistency of the amount, and then check whether the invoices and receipts match the financial data in the system to ensure that each transaction is supported by vouchers. In this process, use data analysis tools to identify excessively high or low amounts, check whether there are input errors or abnormal transactions, and find duplicate financial records to avoid duplicate charges or duplicate payments. Then, verify the rationality of the cost calculation based on the charging standards for medical services, drugs, and consumables, ensure that the time sequence of financial records is reasonable, and avoid errors caused by time confusion. Finally, reconcile the income in the financial system with the bank flow to ensure the consistency of capital flow, check the financial data of different departments or systems, and ensure internal consistency; AI model construction and training steps: Use historical registration data to train a time series model to predict patient flow in the next week or month, use a reinforcement learning model to optimize the allocation of operating rooms and ICU beds, use a regression model to predict drug consumption, set an inventory warning threshold, and trigger an alert when the inventory is less than 10%, use a classification model to analyze patient satisfaction data, and identify key factors affecting medical quality; Real-time monitoring and analysis steps: Real-time display of patient flow in each department, early warning of departments that exceed their carrying capacity, real-time monitoring of the status of ICU monitors and operating room equipment, early warning of abnormal situations, real-time display of drug inventory status, early warning of drugs that are about to be in short supply, real-time monitoring of medical insurance settlement data, and early warning of abnormal settlements; Visualization and report generation steps: Display the real-time patient flow, predicted flow and historical comparison data of each department, display the operating status, utilization rate and fault records of key equipment, display the drug inventory, consumption trend and early warning information, display the real-time data and historical trends of income, expenditure and medical insurance settlement, and generate daily operation reports, including patient flow, equipment utilization rate, drug inventory and financial data.

[0025] It should be noted that in the data collection and integration steps, edge computing technology is used to perform preliminary processing of medical device sensor data, including noise reduction and data compression, and real-time data collection and integration is achieved through a multi-protocol data access gateway.

[0026] It should be noted that in the data preprocessing step, a missing value completion algorithm based on deep learning is used to clean the data, and AutoML technology is used to automatically extract key features.

[0027] It should be noted that in the AI ​​model construction and training steps, the patient flow prediction model uses a time series algorithm, the resource scheduling optimization model uses a reinforcement learning algorithm, the drug inventory warning model uses a regression algorithm, and the medical quality monitoring model uses a classification algorithm.

[0028] It should be noted that the formulas for calculating the forgotten state data, input state data, candidate supplementary data, update data, output state data and hidden state data in the patient flow prediction model are as follows: ; ; ; ; ; ; in, To forget state data, that is, to decide which historical patient flow information needs to be forgotten. For example, after the peak of seasonal flu has passed, the model can forget this part of information to avoid interference with future predictions. To input state data, the system decides which new patient flow information needs to be stored. Supplement data for candidates, that is, generate new patient flow information, for example, generate possible future patient flow trends based on current outpatient data and historical data, To update the data, that is, to combine historical information with current information and update the model's memory, for example, combining seasonal influenza and current outpatient data to update the prediction of future patient flow, To output status data, decide which information to use to predict future patient flow, such as outputting a forecast of outpatient patient flow for the next 24 hours. The hidden state data, that is, the output at the current moment, is used to predict the future patient flow, for example, output the patient flow prediction results for the next week. is the weight matrix used to adjust the input data, that is, the impact of patient flow on the forgetting state data, is the patient flow prediction result at the previous moment, is the current patient flow data, is the bias term for forgetting state data, is the activation function, which maps the output into probability data. Represents the weight matrix, which is used to adjust the impact of input data on input state data. is the bias term of the input state data, is the weight matrix of candidate input data, which is used to adjust the influence of input data on candidate input data. is the bias term of the candidate input data, is the updated data of the previous moment, indicating the historical patient flow information. is the weight matrix of the output state data, is the bias term for the output state data.

[0029] It should be noted that the resource scheduling optimization model uses the maximum cumulative reward formula and the loss function to calculate together. The formula is as follows: ; ; in, Indicates that the scheduling action is executed under the current hospital resource status s (such as operating room occupancy rate, number of ICU beds available) (such as allocating operating rooms and adjusting the shifts of medical staff), It indicates the immediate effect after executing the scheduling action. For example, after the operating room is allocated, the waiting time for surgery is reduced, and the reward value is positive. If the patient's waiting time increases due to unreasonable resource allocation, the reward value is negative. is a discount factor used to balance the importance of current rewards and future rewards, such as giving priority to the current utilization of operating rooms while taking into account the demand for future ICU beds. is the expected value, which represents the average of all possible situations. It represents the expected effect data of selecting the optimal scheduling action after the occupancy rate of the operating room changes. They represent the hospital resource status in the next state and the execution scheduling action, namely The next state of is the parameter of the current Q network, which is used to calculate the value of Q. Represents the target network parameters, which are used to calculate the target Q value. is the loss function, which measures the difference between the current Q value and the target Q value, that is, the gap between the current operating room allocation strategy and the optimal strategy.

[0030] It should be noted that in the real-time monitoring and analysis steps, a stream processing framework is used to process real-time data, and dynamic thresholds are set to warn of abnormal situations.

[0031] It should be noted that in the visualization and report generation steps, patient flow, equipment status, drug inventory and financial data are displayed through multi-terminal visualization dashboards, and daily, weekly and monthly operation reports are automatically generated.

[0032] An artificial intelligence-based hospital operation monitoring and analysis platform, such as Figure 2-Figure 6 As shown, including: Data layer: The data layer includes data acquisition module and data storage module. The data acquisition module supports multi-source data access, including electronic medical record system, medical equipment sensor, drug inventory management system and financial system, and collects patient flow, equipment status, drug inventory and financial data of each department in real time. The data storage module uses distributed data warehouse to store data and supports efficient query and analysis. Processing layer: The processing layer includes data preprocessing module, AI model engine and real-time stream processing module; Application layer: The application layer includes patient flow management module, resource scheduling optimization module, drug inventory management module, medical quality monitoring module and financial management module: The patient flow management module is used to monitor and predict patient flow in real time, optimize resource allocation, and use the patient flow prediction model to predict future patient flow based on combined analysis of historical registration data, department visit data, seasonal factors, weather data, etc. The resource scheduling optimization module is used to dynamically schedule resources such as operating rooms and ICU beds; The drug inventory management module is used to monitor drug inventory in real time, provide inventory warnings and replenishment suggestions, monitor inventory in real time through the drug management system, record each drug entry, exit and consumption, classify and manage according to drug categories, set different inventory strategies, rely on historical consumption data (daily / weekly / monthly drug consumption), patient flow data (number of department visits, number of hospitalizations, etc.), seasonal factors (such as the increase in demand for anti-cold medicines during the flu season), etc. to combine analysis and use regression models for prediction. The formula is as follows: ; ; in, Indicates the consumption of medicines. is the independent variable, indicating the factors that affect drug consumption. The amount of medicine consumed when all independent variables are zero. Indicates the degree of influence of the independent variable on drug consumption, such as the effect of patient flow on drug consumption. is the error term, is the loss function, which measures the difference between the predicted drug consumption and the actual consumption. is the actual drug consumption, The drug consumption predicted by the model.

[0033] In this process, collaborative representation filtering is used to predict the demand for new drugs based on the consumption patterns of similar drugs. During use, safety stock is set according to the consumption rate of drugs and the supplier's delivery cycle. The safety stock of conventional drugs is 7 days of consumption, and the safety stock of emergency drugs is 3 days of consumption. The set warning threshold is 10%. When the threshold state is triggered, measures will be taken automatically to notify the pharmacy administrator and the purchasing department through SMS, email or system messages. For scarce drugs, non-emergency use is restricted, and emergency and inpatient patients are given priority. The medical quality monitoring module is used to analyze patient satisfaction data and identify medical quality issues; The financial management module is used to monitor income, expenditure and medical insurance settlement data in real time, provide financial analysis reports, display income, expenditure and medical insurance settlement data in real time through the financial system, and use line charts or bar charts to show the historical trend of financial data. In this process, the rule engine is used to detect abnormal transactions; User layer: The user layer includes a visual dashboard and a report generation module. The visual dashboard provides a dual-end visual interface for PC and mobile terminals to display key indicators and analysis results. The report generation module is used to automatically generate daily, weekly and monthly operation reports, and supports export and sharing.

[0034] The above embodiments are provided for persons familiar with the art to implement or use the present invention. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. A hospital operation monitoring and analysis method based on artificial intelligence, characterized in that: Included are: Data collection and integration steps: Collect data from multiple data sources in the hospital, including electronic medical record systems, medical equipment sensors, drug inventory management systems, and financial systems, and integrate multi-source heterogeneous data into a unified data platform through a data access gateway; Data preprocessing steps: Clean, standardize and extract features from the collected data, including removing noise data, filling missing values, unifying data formats and units, and extracting key features using automated feature engineering; AI model construction and training steps: Build AI models based on hospital operation scenarios, including patient flow prediction models, resource scheduling optimization models, drug inventory early warning models, and medical quality monitoring models, and use historical data to train and verify the models; Real-time monitoring and analysis steps: input real-time data into the AI ​​model for analysis, generate prediction results or classification labels, and issue real-time warnings for abnormal situations, display the patient flow of each department in real time, issue warnings for departments that exceed their carrying capacity, monitor the status of ICU monitors and operating room equipment in real time, issue warnings for abnormal situations, display the inventory status of drugs in real time, issue warnings for drugs that are about to be in short supply, monitor medical insurance settlement data in real time, and issue warnings for abnormal settlements; Visualization and report generation steps: Display the real-time patient flow, predicted flow and historical comparison data of each department, display the operating status, utilization rate and fault records of key equipment, display the drug inventory, consumption trend and early warning information, display the real-time data and historical trends of income, expenditure and medical insurance settlement, and generate daily operation reports, including patient flow, equipment utilization rate, drug inventory and financial data.

2. A hospital operation monitoring and analysis method based on artificial intelligence according to claim 1, characterized in that: In the AI ​​model construction and training steps, the patient flow prediction model adopts a time series algorithm, the resource scheduling optimization model adopts a reinforcement learning algorithm, the drug inventory warning model adopts a regression algorithm, and the medical quality monitoring model adopts a classification algorithm.

3. The hospital operation monitoring and analysis method based on artificial intelligence according to claim 1, characterized in that: In the real-time monitoring and analysis step, a stream processing framework is used to process real-time data, and dynamic thresholds are set to warn of abnormal situations.

4. The artificial intelligence-based hospital operation monitoring and analysis method according to claim 1, characterized in that: In the visualization and report generation step, patient flow, equipment status, drug inventory and financial data are displayed through a multi-terminal visualization dashboard, and daily, weekly and monthly operation reports are automatically generated.

5. The artificial intelligence-based hospital operation monitoring and analysis method according to claim 1, characterized in that: In the data collection and integration step, edge computing technology is used to perform preliminary processing on the medical device sensor data, including noise reduction and data compression, and real-time data collection and integration is achieved through a multi-protocol data access gateway.

6. The artificial intelligence-based hospital operation monitoring and analysis method according to claim 1, characterized in that: In the data preprocessing step, a missing value completion algorithm based on deep learning is used to clean the data, and AutoML technology is used to automatically extract key features.

7. The artificial intelligence-based hospital operation monitoring and analysis method according to claim 2, characterized in that: The formulas for calculating the forgotten state data, input state data, candidate supplementary data, updated data, output state data and hidden state data of the patient flow prediction model are as follows: ； ； ； ； ； ； in, To forget the state data, that is, to decide which historical patient flow information needs to be forgotten, To input state data, the system decides which new patient flow information needs to be stored. Fill in data for candidates, that is, generate new patient flow information, To update the data, that is, to combine historical information with current information and update the model's memory, To output status data, decide which information to use to predict future patient flow, is the hidden state data, that is, the output at the current moment, used to predict future patient flow, is the weight matrix used to adjust the input data, that is, the impact of patient flow on the forgetting state data, is the patient flow prediction result at the previous moment, is the current patient flow data, is the bias term for forgetting state data, is the activation function, which maps the output to probability data. Represents the weight matrix, which is used to adjust the impact of input data on input state data. is the bias term of the input state data, is the weight matrix of candidate input data, which is used to adjust the influence of input data on candidate input data. is the bias term of the candidate input data, is the updated data of the previous moment, indicating the historical patient flow information. is the weight matrix of the output state data, is the bias term for the output state data.

8. The hospital operation monitoring and analysis method based on artificial intelligence according to claim 2, characterized in that: The resource scheduling optimization model is calculated by maximizing the cumulative reward formula and the loss function. The formula is as follows: ； ； in, Indicates that the scheduling action is executed under the current hospital resource status s The expected effect, Indicates the immediate effect after executing the scheduling action. A discount factor that balances the importance of current rewards and future rewards, is the expected value, which represents the average of all possible situations. It represents the expected effect data of selecting the optimal scheduling action after the operating room occupancy rate changes. They represent the hospital resource status in the next state and the execution scheduling action, namely The next state of is the parameter of the current Q network, which is used to calculate the value of Q. Represents the target network parameters, which are used to calculate the target Q value. is the loss function, which measures the difference between the current Q value and the target Q value, that is, the gap between the current operating room allocation strategy and the optimal strategy.

9. A hospital operation monitoring and analysis platform based on artificial intelligence, characterized in that: Included are: Data layer, the data layer includes a data acquisition module and a data storage module. The data acquisition module supports multi-source data access, including electronic medical record system, medical equipment sensor, drug inventory management system and financial system. The data storage module uses a distributed data warehouse to store data and supports efficient query and analysis; The processing layer includes a data preprocessing module, an AI model engine, and a real-time stream processing module; The application layer includes a patient flow management module, a resource scheduling optimization module, a drug inventory management module, a medical quality monitoring module and a financial management module. The patient flow management module is used to monitor and predict patient flow in real time and optimize resource allocation. The resource scheduling optimization module is used to dynamically schedule resources including operating rooms and ICU beds. The drug inventory management module is used to monitor drug inventory in real time and provide inventory warnings and replenishment suggestions. The medical quality monitoring module is used to analyze patient satisfaction data and identify medical quality issues. The financial management module is used to monitor income, expenditure and medical insurance settlement data in real time and provide financial analysis reports.

10. An artificial intelligence-based hospital operation monitoring and analysis platform according to claim 9, characterized in that: It also includes a user layer, which includes a visual dashboard and a report generation module. The visual dashboard provides a dual-end visual interface for PC and mobile terminals to display key indicators and analysis results. The report generation module is used to automatically generate daily, weekly and monthly operation reports, and supports export and sharing.

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