Hospital economic risk early warning and prevention and control system

Abstract

The invention relates to the technical field of economic early warning, prevention and control, and particularly discloses a hospital economic risk early warning, prevention and control system, which comprises a data acquisition and preprocessing module, an intelligent risk prediction module, a digital twinborn simulation decision module, a decision fusion module and a user interaction module, according to the method, the long-short-term memory network is adopted for time sequence modeling, the long-term dependency relationship in the data can be captured, the accuracy of future risk prediction is improved, and the system can continuously optimize the prediction model, adapt to data changes and ensure the timeliness and reliability of the prediction result; the digital twinborn simulation decision-making module can construct a digital twinborn model of hospital economic operation based on the risk prediction value, simulate system states under different management measures and risk scenes and provide visual decision-making support for managers, and the decision-making fusion module carries out fusion processing on the risk prediction value and a scene analysis result to generate a comprehensive decision-making index. The hospital economic risk condition is comprehensively reflected, and a scientific decision basis is provided for managers.

Description

Technical Field

[0001] The present invention belongs to the technical field of economic early warning and prevention and control, and particularly relates to a hospital economic risk early warning and prevention and control system. Background Art

[0002] In the current medical environment, hospital economic risk management is a key link in hospital operation management. With the continuous progress of medical technology and the increasing openness of the medical market, hospitals are facing more and more economic risks, such as cost overruns, revenue fluctuations, policy changes, etc. To effectively respond to these risks, many hospitals have begun to introduce economic risk early warning and prevention and control systems.

[0003] However, the existing hospital economic risk early warning and prevention and control systems have some limitations in many aspects. The existing systems mostly use traditional statistical methods or machine learning algorithms for modeling. These methods may have limitations in processing time series data, and it is difficult to capture the long-term dependence relationships in the data. Moreover, the existing systems usually can only provide a single risk prediction value, lacking the simulation and analysis of the system state under different management measures and risk scenarios. This makes it difficult for managers to make intuitive decision-making bases when formulating risk prevention and control strategies and difficult to make scientific and reasonable decisions.

[0004] In view of this, the inventor proposes a hospital economic risk early warning and prevention and control system to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a hospital economic risk early warning and prevention and control system to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A hospital economic risk early warning and prevention and control system, comprising:

[0008] A data collection and preprocessing module, configured to collect data from hospital internal systems and external data sources, obtain a data set, and preprocess the data set to obtain unified economic risk index data;

[0009] An intelligent risk prediction module, configured to perform time series data analysis on the economic risk index data to obtain a risk prediction value, and perform self-learning processing on the risk prediction value to obtain an optimized future risk prediction value;

[0010] A digital twin simulation decision-making module, configured to use a digital twin model of hospital economic operation combined with the Monte Carlo simulation method of risk scenario probability distribution to simulate the system state under different management measures and risk scenarios, and obtain a scenario analysis result;

[0011] A decision fusion module for fusing the risk prediction value and the scenario analysis result to generate a comprehensive decision-making index;

[0012] A user interaction module for displaying the comprehensive decision-making index through a graphical interface, a dynamic dashboard, and virtual reality to obtain relevant risk prevention and control information.

[0013] Preferably, the time series data analysis is implemented using a long short-term memory network algorithm, and the expression of the long short-term memory network algorithm is:

[0014] f t = σ(W f · [h t-1 , x t + b f )

[0015] i t = σ(W i · [h t-1 , x t + b i )

[0016]

[0017] o t = σ(W o · [h t-1 , x t + b o )

[0018] B t = o t * tanh(C t )

[0019] Where At is the risk index input at time t;

[0020] Bt is the predicted output;

[0021] xt: The input vector at time t, which is mainly the risk index R and other related variables here;

[0022] ht-1: The hidden state at the previous moment, reflecting historical information;

[0023] Wf, Wi, WC, Wo: Weight matrices corresponding to each gate control;

[0024] bf, bi, bC, bo: Bias vectors corresponding to each gate control;

[0025] σ and tanh are the Sigmoid function and the hyperbolic tangent function respectively;

[0026] ⊙: Represents element-wise multiplication.

[0027] Preferably, the formula for the self-learning process is:

[0028]

[0029] The actual risk index at time t;

[0030] The risk index predicted by the model;

[0031] θ: The set of all trainable parameters in the model;

[0032] N: The total number of samples;

[0033] λ: The regularization parameter, used to prevent overfitting.

[0034] Preferably, the formula for the digital twin model is:

[0035]

[0036] X(t): The system state vector, including key economic and operational indicators of the hospital;

[0037] U(t): The control input vector, representing the intervention measures taken by the management;

[0038] A: The state transition matrix, describing the inherent dynamic evolution law of the system;

[0039] B: The control influence matrix, describing the impact of management measures on the system state.

[0040] Preferably, the formula for the Monte Carlo simulation is:

[0041]

[0042] R i: The risk index obtained from the i-th simulation;

[0043] N: The total number of simulations;

[0044] δ(): The Dirac delta function, which is an indicator function in the discrete scenario and is used to count the frequency of the occurrence of the risk index.

[0045] Preferably, the formula for the fusion process is:

[0046]

[0047] The predicted value of the future risk index from the LSTM prediction model;

[0048] S: The comprehensive result of the scenario analysis from the digital twin simulation module, estimating the change in the operating state;

[0049] α: The fusion weight, with a value range of 0 < α < 1, which is determined by the management layer or data-driven methods to balance the contributions of prediction and simulation.

[0050] D: The final decision support metric, used to determine whether risk prevention and control measures need to be taken and their intensity.

[0051] Preferably, the preprocessing includes a data cleaning unit and a data standardization unit.

[0052] The data cleaning unit is used to remove noise and outliers to obtain the cleaned data metrics.

[0053] The data standardization unit is used to normalize the cleaned data metrics to obtain accurate economic risk metrics.

[0054] Preferably, the hospital internal system includes HIS, EMR, and the financial management system; the external data sources include macroeconomic data, policies and regulations, and market dynamics.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] The present invention uses a long short-term memory network for time series modeling, which can capture long-term dependencies in the data, improve the accuracy of future risk prediction. The system has self-learning ability and can continuously optimize the prediction model to adapt to data changes, ensuring the timeliness and reliability of the prediction results. The digital twin simulation decision module can build a digital twin model of the hospital's economic operation based on the risk prediction value, simulate the system state under different management measures and risk scenarios, and provide intuitive decision support for managers. The decision fusion module fuses the risk prediction value and the scenario analysis result to generate a comprehensive decision metric, comprehensively reflecting the hospital's economic risk status and providing a scientific decision basis for managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a block diagram of a hospital economic risk early warning and prevention and control system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1:

[0060] Please refer to Figure 1As shown in the figure, a hospital economic risk early warning and prevention and control system includes:

[0061] A data collection and preprocessing module, which is used to collect data from the hospital internal system and external data sources, obtain a data set, and preprocess the data set to obtain unified economic risk index data;

[0062] The hospital internal system includes HIS, EMR, and financial management systems; the external data sources include macroeconomic data, policies and regulations, and market dynamics;

[0063] The preprocessing includes a data cleaning unit and a data standardization unit;

[0064] The data cleaning unit is used to remove noise and outliers to obtain each cleaned data index;

[0065] The data standardization unit is used to convert each of the cleaned data indexes into a unified standard or range, that is, normalization processing, to obtain accurate economic risk indexes;

[0066] Among them, the ETL (Extract, Transform, Load) tool is used for data collection and preprocessing.

[0067] The data processing libraries (such as pandas) in programming languages such as Python and R are used to implement data cleaning and normalization;

[0068] An intelligent risk prediction module, which is used to analyze the economic risk index data by time series data analysis to obtain a risk prediction value, and perform self-learning processing on the risk prediction value to obtain an optimized future risk prediction value;

[0069] Time series data analysis: Analyze the data arranged in chronological order to reveal the trends and laws of the data changing over time.

[0070] Risk prediction: Based on historical data and analysis results, predict the possible risks in the future;

[0071] The time series data analysis is implemented by using the long short-term memory network algorithm, and the expression of the long short-term memory network algorithm is:

[0072] f t =σ(W f ·[h t-1 ,x t +b f )

[0073] i t =σ(W i ·[h t-1 ,x t +bi )

[0074]

[0075] o t = σ(W o ·[h t-1 ,x t +b o )

[0076] B t = o t *tanh(C t )

[0077] where At is the risk index input at time t;

[0078] Bt is the predicted output;

[0079] xt: the input vector at time t, mainly the risk index R and other related variables here;

[0080] ht-1: the hidden state at the previous time, reflecting historical information;

[0081] Wf, Wi, WC, Wo: the weight matrices corresponding to each gate control;

[0082] bf, bi, bC, bo: the bias vectors corresponding to each gate control;

[0083] σ and tanh are the Sigmoid function and the hyperbolic tangent function respectively;

[0084] ⊙: represents element-wise multiplication;

[0085] Deep learning frameworks such as TensorFlow and PyTorch can be used to build and train the LSTM model;

[0086] Combined with techniques such as cross-validation and grid search to optimize the model parameters;

[0087] This formula is used to improve the accuracy of future risk prediction. Through the LSTM unit, the system can capture the temporal characteristics of risk indicators, realize the dynamic prediction of future risk indices, and the self-learning mechanism enables the model to automatically update the weights when new data arrives, ensuring the continuous accuracy of the prediction model and obtaining the optimized future risk prediction value;

[0088] The digital twin simulation decision-making module is used to adopt the digital twin model of hospital economic operation combined with the Monte Carlo simulation method of the risk scenario probability distribution to simulate the system state under different management measures and risk scenarios and obtain the scenario analysis results;

[0089] A digital twin refers to creating a virtual copy of a hospital's economic operation for simulating and analyzing its operating status;

[0090] Build a digital twin model of the hospital's economic operation based on the risk prediction value.

[0091] Use simulation software to conduct simulation analysis on the digital twin model under different management measures and risk scenarios;

[0092] Furthermore, use game engines or simulation software such as Unity and Unreal Engine to create a digital twin model and a VR environment;

[0093] The formula for the digital twin model is as follows:

[0094]

[0095] X(t): System state vector, including key economic and operating indicators of the hospital (such as cash flow, resource utilization rate, debt ratio);

[0096] U(t): Control input vector, representing the intervention measures taken by the management (such as budget adjustment, cost control strategy);

[0097] A: State transition matrix, describing the inherent dynamic evolution law of the system;

[0098] B: Control influence matrix, describing the impact of management measures on the system state.

[0099] Through dynamic modeling of the overall operating state of the hospital, the digital twin platform can real-time simulate the evolution process of each indicator after the occurrence of risk events, help managers preview the effects under different scenarios, and provide an intuitive basis for prevention and control measures;

[0100] The formula for the Monte Carlo simulation is as follows:

[0101]

[0102] R i: Risk index obtained from the i-th simulation;

[0103] N: Total number of simulations;

[0104] δ(): Dirac delta function, which is an indicator function in discrete scenarios and is used to count the frequency of the occurrence of the risk index;

[0105] Through a large number of scenario simulations, the system can obtain the probability distribution of the risk index, identify the possibility of high-risk scenarios, and thus assist managers in formulating more targeted risk prevention and control strategies;

[0106] The decision fusion module is used to fuse the risk prediction value and the scenario analysis result to generate a comprehensive decision-making index;

[0107] Decision fusion is to integrate and analyze information or prediction results from multiple sources to generate a more comprehensive decision-making basis;

[0108] Conduct comprehensive analysis and fusion processing on the risk prediction value and the scenario analysis result;

[0109] Specifically, data analysis libraries in programming languages such as Python and R are used to implement the decision fusion algorithm;

[0110] The formula for the fusion processing is:

[0111]

[0112] The predicted value of the future risk index from the LSTM prediction model;

[0113] S: The comprehensive result of the scenario analysis from the digital twin simulation module, estimating the change in the operation state;

[0114] α: The fusion weight, with a value range of 0 < α < 1, determined by the management or data-driven method, balancing the contributions of prediction and simulation;

[0115] D: The final decision support index, used to determine whether risk prevention and control measures need to be taken and their intensity;

[0116] This fusion formula comprehensively considers the short-term prediction driven by data and the long-term simulation results of scenario simulation, making the decision more comprehensive and reliable. Managers can make timely and accurate prevention and control responses based on the comprehensive decision-making index;

[0117] The user interaction module is used to display the comprehensive decision-making index through a graphical interface, a dynamic dashboard, and virtual reality VR to obtain relevant risk prevention and control information;

[0118] User interaction refers to the information exchange and operation process between the user and the system;

[0119] Feedback is the opinions, suggestions, or evaluations provided by the user after using the system, used for system optimization and improvement;

[0120] Display the comprehensive decision-making index and relevant risk information through a graphical interface, a dynamic dashboard, and VR technology;

[0121] Collect user feedback and conduct analysis, used to drive the continuous optimization and improvement of each module of the system;

[0122] Specifically, use frameworks such as Qt and Electron to develop the graphical interface and the dynamic dashboard;

[0123] Create an immersive user interaction experience by combining VR technology;

[0124] Use databases and data analysis tools to store and analyze user feedback data.

[0125] As can be seen from the above, the present invention uses a long short-term memory network (LSTM) for time series modeling, which can capture long-term dependencies in the data, improve the accuracy of future risk prediction, the system has the ability of self-learning, and can continuously optimize the prediction model to adapt to data changes, ensuring the timeliness and reliability of the prediction results.

[0126] The digital twin simulation decision-making module can build a digital twin model of the hospital's economic operation based on the risk prediction value, simulate the system state under different management measures and risk scenarios, and provide intuitive decision-making support for managers. The decision fusion module fuses the risk prediction value and the scenario analysis result to generate a comprehensive decision-making index, comprehensively reflecting the hospital's economic risk status and providing a scientific decision-making basis for managers.

[0127] Embodiment 2:

[0128] Early warning and optimization decision-making for abnormal fluctuations in hospital income:

[0129] Background:

[0130] In recent years, due to the adjustment of medical insurance payments, changes in patients' medical behavior, and intensified market competition in a certain tertiary hospital, the income has fluctuated greatly. The hospital management hopes to achieve early warning of abnormal income fluctuations and timely adjust the operation strategy by introducing an economic risk early warning and prevention and control system to ensure the stability and sustainable development of the hospital's finances.

[0131] System modules and detailed parameter data:

[0132] Data collection and preprocessing module:

[0133] Content to be collected:

[0134] Internal data: historical income data, number of outpatient and inpatient visits, medical insurance and self-paying ratios, patient satisfaction, drug and consumable sales data, etc.

[0135] External data: macroeconomic indicators, changes in medical insurance policies, operation data of regional competing hospitals.

[0136] Risk index calculation formula:

[0137]

[0138] Set indicators:

[0139] X1: Fluctuation of income growth rate (normalized value, 0 - 1);

[0140] X2: Rate of change in the number of patients (normalized);

[0141] X3: Change in the proportion of medical insurance payments (normalized);

[0142] X4: Fluctuation in operating costs (normalized);

[0143] Index weights (determined by expert experience combined with historical data):

[0144] w1 = 0.30, w2 = 0.25, w3 = 0.25, w4 = 0.20

[0145] Output: Comprehensive risk index (the calculated risk index is 0.65, range 0 - 1, the higher the value, the greater the risk).

[0146] Intelligent risk prediction module:

[0147] Model adopted: LSTM time series prediction network:

[0148] Model parameters:

[0149] Network structure: 2 - layer LSTM, with 64 hidden units in each layer;

[0150] Time step: Data from the past 12 months is used as input;

[0151] Batch size: 32;

[0152] Learning rate: 0.001;

[0153] Number of iterations: 500 times;

[0154] Core formula (LSTM core calculation):

[0155] f t = σ(W f · [h t-1 , x t + b f )

[0156] i t = σ(W i · [h t-1 , x t + b i )

[0157]

[0158] o t = σ(W o · [h t-1 , x t + b o )

[0159] B t = o t *tanh(C t )

[0160] Output: The predicted risk indices for the next 3 months are 0.68, 0.70, and 0.66 in sequence.

[0161] Digital twin simulation decision-making module:

[0162] Digital twin model: Simulate the overall operation status and revenue change scenarios of the hospital;

[0163] State space model parameters:

[0164] State variable X(t): Include indicators such as revenue, patient flow, cost, etc.;

[0165] Control variable U(t): Include budget adjustment, marketing investment, reallocation of department resources, etc.;

[0166] Model parameters:

[0167] State transition matrix A: Obtained by fitting historical data:

[0168]

[0169] Control matrix B:

[0170]

[0171] Monte Carlo simulation:

[0172] Number of simulations: 1000 times;

[0173] Scenario analysis results (Under different intervention strategies, the average predicted risk value is adjusted to 0.60, the risk interval is reduced, and the risk reduction probability is increased by 15%).

[0174] Decision fusion and user interaction module:

[0175] Decision fusion formula:

[0176]

[0177] Fusion weight: α = 0.6

[0178] Output: The comprehensive decision-making index is 0.64.

[0179] User display: Using a dynamic dashboard and VR immersive display, present the comprehensive risk index D and the scenario simulation results to the management in an intuitive graph and 3D simulation scenario.

[0180] Feedback mechanism: Collect management adjustment measures and actual operation data, and feed them back to the system for further self-learning and model update.

[0181] As can be seen from the above, the early warning effect: The risk of revenue fluctuation can be detected 1 - 2 months in advance, and the prediction accuracy rate reaches 85%.

[0182] Decision support: Based on the risk indicators and scenario simulation results provided by the system, the management adjusted the marketing promotion and resource allocation plans.

[0183] Within 6 months after implementation, the revenue volatility decreased by about 20%, and the overall revenue of the hospital increased steadily.

[0184] The risk response cycle is shortened, and the decision-making efficiency is significantly improved.

[0185] Example 3:

[0186] Early warning of hospital cost overrun risk and optimization of resource allocation:

[0187] Due to the rising raw material prices, increasing labor costs, and unstable equipment maintenance costs in a large general hospital, the overall operating cost has been continuously rising. To ensure the financial health of the hospital, the hospital management decided to adopt an economic risk early warning and prevention and control system to give real-time early warning of the cost overrun risk and determine the optimal resource allocation plan through simulation, so as to take cost control measures in advance.

[0188] Furthermore, the data collection and risk indicator calculation module:

[0189] Data collection:

[0190] Increase rate of raw material prices (X1): Monitored through the supply chain management system, the recent average value after normalization is 0.55.

[0191] Growth rate of labor costs (X2): Collected from the human resources system, the normalized value is 0.65.

[0192] Abnormal ratio of equipment maintenance costs (X3): Based on the equipment maintenance records, the normalized value is 0.45.

[0193] Weight setting:

[0194] The weights are w1 = 0.35, w2 = 0.40, and w3 = 0.25 respectively

[0195] Comprehensive risk indicator calculation formula:

[0196] A = w1×X1 + w2×X2 + w3×X3 = 0.35×0.55 + 0.40×0.65 + 0.25×0.45

[0197] The calculation result is: A = 0.1925 + 0.26 + 0.1125 = 0.565

[0198] That is, the unified cost risk index A = 0.565 is obtained, providing a quantitative basis for subsequent risk prediction.

[0199] Intelligent risk prediction module:

[0200] Model setting:

[0201] Adopt the long short-term memory network (LSTM) and train it using the daily cost risk index sequence of the past 60 days.

[0202] Model parameters:

[0203] Hidden layer dimension: 128;

[0204] Batch size: 32;

[0205] Learning rate: 0.0005;

[0206] Number of training epochs: 150 epochs;

[0207] Loss function: mean squared error (MSE), and the target MSE is controlled below 0.015.

[0208] Prediction result: Based on the current input A = 0.565, the model predicts that the cost risk trend within the next week is B ≈ 0.63.

[0209] Effect: The system gives an early warning indicating that the cost may further increase in the future, and the management can formulate cost control measures in a timely manner according to the prediction results.

[0210] VR / Digital twin simulation decision-making module:

[0211] Build a model:

[0212] Construct a digital twin model of hospital costs and resource allocation to simulate the changes in hospital operation status under different intervention measures.

[0213] Core formula:

[0214] Use the linear state space model for dynamic simulation:

[0215]

[0216] Among them:

[0217] X(t) includes key indicators such as hospital costs, cash flows, and resource utilization rates.

[0218] U(t) represents intervention measures, such as budget adjustment, supply chain optimization, or manpower allocation.

[0219] Parameter data:

[0220] The state transition matrix A is:

[0221] The control matrix B is:

[0222] For the intervention measure of "optimizing the supply chain and internal allocation", U(t) is 0.8 (after normalization).

[0223] Simulation results:

[0224] The scenario analysis result C≈0.58 is obtained after simulation, that is, after implementing the intervention measure, it is expected that the cost risk can be reduced to about 0.58.

[0225] The digital twin module intuitively shows the improvement effect of the intervention measure on the operation indicators, and helps the management evaluate the feasibility of the optimization plan.

[0226] Decision fusion module:

[0227] Fusion formula:

[0228] Among them, the fusion weight α is set to 0.6;

[0229] Then: D = 0.6×0.63 + 0.4×0.58 = 0.378 + 0.232 = 0.61;

[0230] The comprehensive decision-making index D = 0.61 is obtained, indicating that under the current risk warning, the cost risk is basically controlled after implementing the intervention measure, which can be used as the basis for hospital decision-making support.

[0231] User interaction and feedback module:

[0232] By displaying the comprehensive decision-making index value and various operation data through a dynamic dashboard and a VR scenario, the management can intuitively view the risk warning situation and give real-time feedback information after operation for continuous optimization of the system.

[0233] As can be seen from the above, warning timeliness: The system pre-warned the upward trend of future cost risks, giving the management at least one week of buffer time for regulation.

[0234] Cost control: Through simulation, the plan of "optimizing the supply chain and internal allocation" was determined, reducing the predicted risk from 0.63 to 0.58, and the integrated risk index after fusion was reduced to 0.61.

[0235] Decision-making scientificity: The data-based warning and simulation results have greatly improved the scientificity and accuracy of decision-making, and reduced the financial risk caused by cost overruns.

[0236] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0237] In the drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0238] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hospital economic risk early warning and prevention and control system, characterized in that, Including: A data collection and preprocessing module, which is used to collect data from the hospital's internal system and external data sources, obtain a data set, and preprocess the data set to obtain unified economic risk index data; An intelligent risk prediction module, which is used to analyze the economic risk index data by time series data analysis to obtain a risk prediction value, and perform self-learning processing on the risk prediction value to obtain an optimized future risk prediction value; A digital twin simulation decision-making module, which is used to adopt a Monte Carlo simulation method combining the digital twin model of hospital economic operation and the risk scenario probability distribution to simulate the system state under different management measures and risk scenarios, and obtain a scenario analysis result; A decision fusion module, which is used to fuse the risk prediction value and the scenario analysis result to generate a comprehensive decision-making index; A user interaction module, which is used to display the comprehensive decision-making index through a graphical interface, a dynamic dashboard, and virtual reality to obtain relevant risk prevention and control information.

2. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that The time series data analysis is implemented by using a long short-term memory network algorithm, and the expression of the long short-term memory network algorithm is: f t = σ(W f · [h t-1 , x t + b f ) i t = σ(W i · [h t-1 , x t + b i ) o t = σ(W o · [h t-1 , x t + b o ) B t = o t *tanh(C t ) Where At is the risk index input at time t; Bt is the prediction output; xt: The input vector at time t, which is mainly the risk index R and other relevant variables here; ht-1: The hidden state at the previous moment, reflecting historical information; Wf, Wi, WC, Wo: Weight matrices corresponding to each gate control; bf, bi, bC, bo: Bias vectors corresponding to each gate control; σ and tanh are the Sigmoid function and the hyperbolic tangent function respectively; ⊙: Represents element-wise multiplication.

3. The hospital economic risk early warning and prevention and control system according to claim 1, wherein, The formula for the self-learning processing is: The actual risk index at time t; Risk index predicted by the model; θ: The set of all trainable parameters in the model; N: The total number of samples; λ: The regularization parameter, which is used to prevent overfitting.

4. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that, The formula for the digital twin model is: X(t): The system state vector, which includes key economic and operation indicators of the hospital; U(t): The control input vector, which represents the intervention measures taken by the management; A: The state transition matrix, which describes the inherent dynamic evolution law of the system; B: The control influence matrix, which describes the influence of management measures on the system state.

5. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that The formula for the Monte Carlo simulation is: Ri: The risk index obtained from the i-th simulation; N: The total number of simulations; δ(): The Dirac delta function, which is an indicator function in the discrete scenario and is used to count the frequency of the risk index occurrence.

6. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that The formula for the fusion processing is: Future risk index prediction value from the LSTM prediction model; S: The comprehensive result of the scenario analysis from the digital twin simulation module, estimating the change in the operation state; α: The fusion weight, with a value range of 0 < α < 1, which is determined by the management or the data-driven method to balance the contributions of prediction and simulation; D: The final decision support index, which is used to judge whether risk prevention and control measures need to be taken and their intensity.

7. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that, The preprocessing includes a data cleaning unit and a data standardization unit; The data cleaning unit is used to remove noise and outliers to obtain each cleaned data index; The data standardization unit is used to normalize each cleaned data index to obtain accurate economic risk indicators.

8. The hospital economic risk early warning and prevention and control system according to claim 1, characterized in that The hospital's internal system includes HIS, EMR, and the financial management system; The external data sources include macroeconomic data, policies and regulations, and market dynamics.

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