Index analysis system based on hospital risk assessment

Abstract

The invention relates to the technical field of hospital risk assessment, and discloses an index analysis system based on hospital risk assessment, comprising: a dynamic data acquisition module configured with a hospital multi-source heterogeneous data interface, the data acquisition module is used for acquiring medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data and medical equipment operation data in real time. According to the system, the dynamic data acquisition module is arranged, hospital multi-dimensional data is acquired in real time through a multi-source heterogeneous data interface, a comprehensive real-time data basis is provided for risk assessment, the standardization processing module performs data cleaning, format standardization and dimension alignment, a cross-department associated data set is constructed, the data quality and availability are improved, and the risk assessment efficiency is improved. A dynamic analysis unit of the intelligent risk assessment engine calculates indexes in real time based on a sliding time window mechanism, generates a multi-dimensional risk evolution trend map, and can monitor risk changes of different time scales.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital risk assessment, and particularly to an index analysis system based on hospital risk assessment. Background Art

[0002] The index analysis system for hospital risk assessment is an intelligent tool that can collect, organize, and analyze various risk-related data during the operation of a hospital. This system can conduct detailed index analysis on multiple dimensions of a hospital, such as medical quality, patient safety, financial status, personnel management, and equipment operation. By deeply exploring and evaluating these indexes, hospital managers can timely discover potential risk factors, formulate corresponding countermeasures in advance, thereby optimizing the hospital management process, improving the quality of medical services, and ensuring the life and health of patients and the stable operation of the hospital.

[0003] However, existing hospital risk assessment technologies mostly rely on manual experience summary and static index analysis, and there are significant defects. For example, traditional methods usually adopt fixed thresholds or periodic sampling inspection modes, which cannot dynamically adapt to the real-time changes of hospital operation data; at the same time, the data sources are scattered and the standardization degree is low, resulting in insufficient cross-department index correlation analysis and difficulty in discovering hidden risk coupling effects. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an index analysis system based on hospital risk assessment, which can realize real-time processing of multi-source data and dynamic risk assessment, overcomes the defects of traditional methods relying on manual experience and being unable to adapt to real-time data changes, can conduct real-time dynamic assessment of hospital risks, and solves the above technical problems.

[0005] To achieve the above object, the present invention provides the following technical solution: An index analysis system based on hospital risk assessment, comprising:

[0006] A dynamic data acquisition module, which is configured with hospital multi-source heterogeneous data interfaces for real-time obtaining medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data, and medical equipment operation data;

[0007] A standardization processing module, which is connected to the dynamic data acquisition module and is used for performing data cleaning, format standardization, and dimension alignment on the multi-source heterogeneous data to construct a cross-department association data set including timestamps;

[0008] Intelligent risk assessment engine, the intelligent risk assessment engine includes a dynamic analysis unit, a coupling effect identification unit and an adaptive threshold unit. The dynamic analysis unit calculates real-time indicators for the standardized data set based on the sliding time window mechanism to generate a multi-dimensional risk evolution trend map; the coupling effect identification unit uses a graph neural network to construct an index association topology model and mines cross-dimensional risk conduction paths through node embedding technology; the adaptive threshold unit establishes a risk probability model based on historical risk events and dynamically adjusts the warning trigger thresholds of each risk indicator.

[0009] Visual warning module, the visual warning module is connected to the intelligent risk assessment engine and is used to generate a three-dimensional risk heat map and warning signals. The warning signals include an interpretability analysis report of the risk coupling path.

[0010] Preferably, the dynamic data acquisition module is provided with a data preprocessing sub-module. The preprocessing sub-module is used to detect anomalies in the real-time acquired data stream. The preprocessing sub-module uses the following formula for anomaly determination:

[0011]

[0012] where n is the number of samples for calculating the standard deviation, x i is the data point at time i, u t is the mean at time t, and σ t is the standard deviation at time t.

[0013] Preferably, the standardization processing module includes a data cleaning unit, a format standardization unit and a dimension alignment unit. The data cleaning unit is used to remove duplicate records, error data and redundant information in the data. The format standardization unit converts data from different sources into a unified format standard; the dimension alignment unit is used to associate and integrate data of different dimensions according to the time stamp and the index system.

[0014] Preferably, when the dynamic analysis unit calculates real-time indicators using the sliding time window mechanism, different time window lengths and sliding step sizes are set according to the characteristics of different risk indicators to monitor risk changes at different time scales.

[0015] Preferably, when the coupling effect identification unit constructs the index association topology model, the following formula is used to calculate the association strength between nodes:

[0016]

[0017] where S ij represents the association strength between node i and node j, x ik and x jkrespectively represent the data values of node i and node j in the k-th feature dimension, which are the means of the data values of node i and node j in all feature dimensions, and m is the number of feature dimensions.

[0018] Preferably, in the three-dimensional risk heat map generated by the visualization warning module, the risk levels are distinguished by color and height, where the depth of the color represents the level of risk, and the height represents the number of risk indicators in this area. For the division of risk levels, the following rules are adopted:

[0019] When the risk value is in the interval [0, 0.3], it is marked as low risk, the color is shown as green, and the height is 1 unit; when the risk value is in the interval [0.3, 0.6], it is marked as medium risk, the color is shown as yellow, and the height is 2 units; when the risk value is in the interval [0.6, 1], it is marked as high risk, the color is shown as red, and the height is 3 units.

[0020] Preferably, the intelligent risk assessment engine further includes a risk trend prediction unit, and the risk trend prediction unit is used to learn and predict the multi-dimensional risk evolution trend map.

[0021] Preferably, the warning signal includes an interpretability analysis report of the risk coupling path, and the report uses a combination of charts, texts and cases to elaborate in detail the formation reasons, influence ranges and potential consequences of the risk coupling path.

[0022] Preferably, when the adaptive threshold unit establishes a risk probability model based on historical risk events, it uses a machine learning algorithm to train historical data and continuously optimize the parameters and performance of the risk probability model.

[0023] Compared with the prior art, the present invention provides an index analysis system based on hospital risk assessment, which has the following beneficial effects:

[0024] The present invention realizes real-time dynamic assessment of hospital risks by setting up a dynamic data acquisition module to obtain multi-dimensional hospital data in real time through a multi-source heterogeneous data interface, providing a comprehensive real-time data basis for risk assessment, a standardized processing module for data cleaning, format standardization and dimension alignment, constructing a cross-departmental associated data set, improving data quality and usability, a dynamic analysis unit of the intelligent risk assessment engine calculating indicators in real time based on a sliding time window mechanism to generate a multi-dimensional risk evolution trend map, which can monitor risk changes at different time scales; a coupling effect identification unit mining cross-dimensional risk conduction paths; an adaptive threshold unit dynamically adjusting the warning threshold according to historical risk events, overcoming the defects of traditional methods that rely on manual experience and static index analysis and cannot adapt to real-time data changes. Description of the Drawings

[0025] Figure 1 This is the block diagram of the system of the present invention. Specific implementation manners

[0026] 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 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.

[0027] Please refer to Figure 1 , an index analysis system based on hospital risk assessment provided by the present invention includes:

[0028] A dynamic data acquisition module, which is configured with a hospital multi-source heterogeneous data interface for real-time acquisition of medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data, and medical device operation data;

[0029] A standardization processing module, which is connected to the dynamic data acquisition module and is used for data cleaning, format standardization, and dimension alignment of multi-source heterogeneous data to construct a cross-departmental association data set including timestamps;

[0030] An intelligent risk assessment engine, which includes a dynamic analysis unit, a coupling effect identification unit, and an adaptive threshold unit. The dynamic analysis unit performs real-time index calculation on the standardized data set based on the sliding time window mechanism to generate a multi-dimensional risk evolution trend map; the coupling effect identification unit uses a graph neural network to construct an index association topology model and mines cross-dimensional risk conduction paths through node embedding technology; the adaptive threshold unit establishes a risk probability model based on historical risk events and dynamically adjusts the warning trigger thresholds of each risk index;

[0031] A visualization warning module, which is connected to the intelligent risk assessment engine and is used for generating a three-dimensional risk heat map and a warning signal. The warning signal includes an interpretability analysis report of the risk coupling path.

[0032] Furthermore, the dynamic data acquisition module is provided with a data preprocessing sub-module, which is used for anomaly detection of the real-time acquired data stream. The preprocessing sub-module uses the following formula for anomaly determination:

[0033]

[0034] where n is the number of samples for calculating the standard deviation, xi is the data point at time i, u t is the mean at time t, and σt is the standard deviation at time t.

[0035] The dynamic data acquisition module is responsible for collecting various key data during the hospital operation process in the whole system, providing basic support for subsequent risk assessment and analysis; the dynamic data acquisition module is configured with hospital multi-source heterogeneous data interfaces, and its main function is to obtain medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data and medical equipment operation data in real time; these multi-dimensional data cover all key aspects of hospital operation, and through the collection of these data, a comprehensive and real-time data basis is provided for hospital risk assessment, which helps to analyze potential risks.

[0036] The dynamic data acquisition module is equipped with a data preprocessing sub-module for detecting anomalies in the real-time acquired data stream, and uses the above formula for anomaly determination. The specific determination process is as follows:

[0037] 1. Calculate the mean: μ in the formula t represents the mean at time t, which is obtained by summing the data points xi from time t - n + 1 to t and then dividing by the sample size n; the mean reflects the average level of this group of data and is an important reference benchmark for judging whether the data is abnormal; for example, for the operating temperature data of a certain type of medical equipment monitored over a period of time, the calculated mean is the average condition of the equipment operating temperature during this period.

[0038] 2. Calculate the sum of squared deviations: (x i - μ t ) 2 represents the square of the deviation of each data point x i from the mean μ t , and is obtained by summing the squared deviations from time t - n + 1 to t; if the data points are all closely distributed around the mean, the sum of squared deviations will be small; conversely, if there are data points far from the mean, the sum of squared deviations will be large.

[0039] 3. Calculate the standard deviation: Divide the sum of squared deviations by n - 1 and then take the square root to get the standard deviation σ t , and the standard deviation quantifies the degree of dispersion of the data.

[0040] 4. Anomaly determination: When the deviation of a certain data point xi from the mean μ t exceeds a certain multiple (such as 2 times or 3 times) of the standard deviation, the data point xi can be determined as an outlier, because in normally distributed data, most data will be concentrated within a certain standard deviation range around the mean, and data points outside this range are likely to be caused by abnormal factors such as equipment failures and data entry errors.

[0041] Further, the standardization processing module includes a data cleaning unit, a format standardization unit, and a dimension alignment unit. The data cleaning unit is used to remove duplicate records, error data, and redundant information in the data. The format standardization unit converts data from different sources into a unified format standard. The dimension alignment unit is used to associate and integrate data with different dimensions according to timestamps and an indicator system.

[0042] Removing duplicate, incorrect, and redundant problematic data through the data cleaning unit can improve data quality, reduce the burden of subsequent processing, and make the analysis results more accurate and reliable. For example, when analyzing the length of a patient's hospital stay, duplicate records will erroneously inflate the average length of stay. If there is incorrect hospital admission date data, it will lead to incorrect conclusions in analyzing the trend of hospital stay time. After cleaning, such situations can be avoided, ensuring the availability of data. After uniformly standardizing the data format, it is more convenient to conduct cross-departmental data comparison and analysis to explore potential connections between data. For example, after unifying the data formats of the patient treatment costs in different departments, the cost differences between departments can be visually compared to identify key points for cost control. Through dimension alignment, comprehensive analysis of multi-dimensional data can be achieved to comprehensively understand the hospital's operation status. Taking the analysis of hospital infection risk as an example, by combining multi-dimensional data such as timestamps, departments, and patient conditions, the infection situation in different time periods and different departments can be clearly seen, and the high-risk factors for infection can be accurately identified, providing a strong basis for prevention and control measures.

[0043] Further, when the dynamic analysis unit uses a sliding time window mechanism to calculate real-time indicators, different time window lengths and sliding step sizes are set according to the characteristics of different risk indicators to monitor risk changes at different time scales.

[0044] Further, when the coupling effect identification unit constructs an indicator association topology model, the following formula is used to calculate the association strength between nodes:

[0045]

[0046] where S ij represents the association strength between node i and node j, x ik and x jk respectively represent the data values of node i and node j in the kth feature dimension, are the mean values of the data values of node i and node j in all feature dimensions respectively, and m is the number of feature dimensions.

[0047] Since hospital risks involve multiple dimensions and the indicators are interrelated, the above formula helps identify the connections between different risk indicators by quantifying the association strength between nodes. In the indicator system composed of multi-dimensional data such as medical quality, patient safety, and financial operation, there is a potential connection between the operation data of medical equipment (such as the number of equipment failures) and the data of patient safety incidents (such as the number of medical accidents caused by equipment problems). Using this formula to calculate the association strength between the two, if the association strength is high, it indicates that an increase in the number of equipment failures may trigger more patient safety incidents, that is, a conduction path from the operation risk of medical equipment to the patient safety risk is found, enabling the hospital to intervene in advance and reduce risks.

[0048] By constructing an indicator association topology model, in the model constructed by the graph neural network, the nodes represent different risk indicators, and the weight of the edge represents the association strength. The weight of the edge is determined by the association strength calculated by this formula, forming a topological structure that accurately reflects the relationship between indicators. Such a model can intuitively present the association of various risk indicators in the hospital, facilitating hospital managers to grasp the risk situation as a whole and quickly discover key risk indicators and potential risk conduction paths.

[0049] Furthermore, in the three-dimensional risk heat map generated by the visualization warning module, the risk levels are distinguished by color and height. Among them, the depth of the color represents the level of risk, and the height represents the number of risk indicators in this area. For the division of risk levels, the following rules are adopted:

[0050] When the risk value is in the interval [0, 0.3], it is marked as low risk, the color is shown as green, and the height is 1 unit; when the risk value is in the interval [0.3, 0.6], it is marked as medium risk, the color is shown as yellow, and the height is 2 units; when the risk value is in the interval [0.6, 1], it is marked as high risk, the color is shown as red, and the height is 3 units.

[0051] The visualization warning module is used to generate a three-dimensional risk heat map, distinguishing risk levels by color and height. The depth of the color represents the degree of risk, and the height represents the number of risk indicators in the area. This enables hospital managers and relevant personnel to quickly and intuitively understand the risk distribution of each area and business link in the hospital. On the heat map, the red area (high risk) and the yellow area (medium risk) are clearly visible. Managers can quickly locate the departments or business processes with higher risks. For example, if a certain department has frequent patient safety incidents resulting in a high risk value, it will be shown in a prominent red color and a relatively high height on the heat map, facilitating timely attention. The visualization warning module is connected to the intelligent risk assessment engine, receiving the risk assessment results and generating warning signals. When the risk indicators reach the warning threshold, an alarm is issued in a timely manner to remind relevant hospital personnel to take measures. This timely warning mechanism helps the hospital intervene at the budding stage of risks and avoid the expansion of risks.

[0052] Furthermore, the intelligent risk assessment engine further includes a risk trend prediction unit, which is used to learn and predict the multi-dimensional risk evolution trend map.

[0053] By setting up the risk trend prediction unit to learn and predict the multi-dimensional risk evolution trend map, it can help the hospital to insight into the development direction of potential risks in advance. For example, by analyzing past financial operation data and current market dynamics, the change trend of the hospital's cash flow in the next few months can be predicted. If the risk of tight funds is predicted, the hospital can adjust the budget in advance, arrange funds reasonably, and avoid financial crises. Another example is that based on medical quality monitoring data and patient safety incident data, the change trend of the probability of a certain type of medical accident is predicted. Accordingly, the hospital strengthens the training of medical staff in advance and optimizes the diagnosis and treatment process to prevent accidents and improve the quality of medical services.

[0054] Furthermore, the warning signal includes an interpretability analysis report of the risk coupling path. The report uses a combination of charts, texts, and cases to elaborate in detail the formation reasons, influence scope, and potential consequences of the risk coupling path.

[0055] The warning signal includes an interpretability analysis report of the risk coupling path, and the report uses a combination of charts, texts, and cases to elaborate in detail the formation reasons, influence scope, and potential consequences of the risk coupling path. In the hospital operation scenario, when the cost in the financial operation data rises significantly and the patient cure rate in the medical quality monitoring data decreases at the same time, the system issues a warning. The report can show the correlation trend between the rising cost and the decreasing cure rate data through charts, and use texts to explain that it may be because high-price medical consumables with unqualified quality are purchased, which affects the treatment effect and then leads to the decrease in the cure rate. It can also combine similar cases, such as other hospitals having increased patient complaints and damaged reputation due to the same problem, so that hospital managers can clearly understand the root cause, influence scope of the risk, including the impact on patient satisfaction and hospital reputation, and potential consequences such as financial losses and market share decline, so as to formulate targeted strategies.

[0056] Furthermore, when the adaptive threshold unit establishes a risk probability model based on historical risk events, it uses machine learning algorithms to train historical data and continuously optimize the parameters and performance of the risk probability model.

[0057] When the adaptive threshold unit establishes a risk probability model based on historical risk events, it uses machine learning algorithms to train historical data to optimize the parameters and performance of the risk probability model. The hospital operation data is complex and variable, and there are differences in risk characteristics in different periods. Through the training of machine learning algorithms, the system can automatically learn the data patterns and rules in historical risk events. When analyzing the risk of patient safety events, using a large amount of historical patient safety event data, including information such as event type, occurrence time, and involved personnel, the machine learning algorithm can continuously adjust the parameters of the risk probability model to make the model more accurately reflect the probability of patient safety events occurring in the current hospital operation environment. As new data is continuously added, the model is continuously optimized, and it can more timely and accurately dynamically adjust the warning trigger thresholds of various risk indicators, improve the accuracy and timeliness of risk assessment, avoid false negatives or false positives caused by unreasonable thresholds, and enable the hospital to more effectively prevent and control risks.

[0058] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An index analysis system based on hospital risk assessment, characterized in that: including, a dynamic data acquisition module, which is configured with hospital multi-source heterogeneous data interfaces for real-time acquisition of medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data, and medical device operation data; a standardization processing module, which is connected to the dynamic data acquisition module and is used for data cleaning, format standardization, and dimension alignment of multi-source heterogeneous data to construct a cross-departmental association dataset containing timestamps; an intelligent risk assessment engine, which includes a dynamic analysis unit, a coupling effect identification unit, and an adaptive threshold unit. The dynamic analysis unit performs real-time index calculation on the standardized dataset based on a sliding time window mechanism to generate a multi-dimensional risk evolution trend map; the coupling effect identification unit uses a graph neural network to construct an index association topology model and mines cross-dimensional risk conduction paths through node embedding technology; the adaptive threshold unit establishes a risk probability model based on historical risk events and dynamically adjusts the warning trigger thresholds of each risk index; a visualization warning module, which is connected to the intelligent risk assessment engine and is used for generating a three-dimensional risk heat map and warning signals. The warning signals include an interpretability analysis report of the risk coupling path.

2. The index analysis system based on hospital risk assessment according to claim 1, wherein: The dynamic data acquisition module is provided with a data preprocessing sub-module, which is used for anomaly detection of the real-time acquired data stream. The preprocessing sub-module uses the following formula for anomaly determination: where n is the number of samples for calculating the standard deviation, and x i is the data point at time i, and u t is the mean at time t, and σ t is the standard deviation at time t.

3. The index analysis system based on hospital risk assessment according to claim 1, characterized in that: The standardization processing module includes a data cleaning unit, a format standardization unit, and a dimension alignment unit. The data cleaning unit is used to remove duplicate records, error data, and redundant information in the data. The format standardization unit converts data from different sources into a unified format standard; the dimension alignment unit is used to associate and integrate data of different dimensions according to timestamps and an index system.

4. The index analysis system based on hospital risk assessment according to claim 1, characterized in that: When the dynamic analysis unit performs real-time index calculation using a sliding time window mechanism, different time window lengths and sliding step sizes are set according to the characteristics of different risk indexes to monitor risk changes at different time scales.

5. The index analysis system based on hospital risk assessment according to claim 1, wherein: When the coupling effect identification unit constructs an index association topology model, the following formula is used to calculate the association strength between nodes: Among them, S ij represents the association strength between node i and node j, x ik and x jk respectively represent the data values of node i and node j in the k-th feature dimension, which are the means of the data values of node i and node j in all feature dimensions, and m is the number of feature dimensions.

6. The index analysis system based on hospital risk assessment according to claim 1, characterized in that: In the three-dimensional risk heat map generated by the visualization warning module, risk levels are distinguished by color and height. The depth of the color represents the level of risk, and the height represents the number of risk indexes in this area. For the division of risk levels, the following rules are adopted: When the risk value is in the interval [0, 0.3], it is marked as low risk, the color is shown as green, and the height is 1 unit; when the risk value is in the interval [0.3, 0.6], it is marked as medium risk, the color is shown as yellow, and the height is 2 units; when the risk value is in the interval [0.6, 1], it is marked as high risk, the color is shown as red, and the height is 3 units.

7. An index analysis system based on hospital risk assessment according to claim 1, characterized in that: The intelligent risk assessment engine further includes a risk trend prediction unit, which is used for learning and predicting the multi-dimensional risk evolution trend map.

8. The index analysis system based on hospital risk assessment according to claim 1, characterized in that: The warning signal includes an interpretability analysis report of the risk coupling path. The report elaborates in detail on the formation reasons, influence scope, and potential consequences of the risk coupling path by combining charts, texts, and cases.

9. The index analysis system based on hospital risk assessment according to claim 1, characterized in that: When establishing the risk probability model based on historical risk events, the adaptive threshold unit uses machine learning algorithms to train historical data for optimizing the parameters and performance of the risk probability model.

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