Multi-source data integration and processing system for doctor seeing process optimization and modeling

Abstract

The invention discloses a multi-source data integration and processing system for doctor seeing process optimization and modeling. The multi-source data integration and processing system comprises a data acquisition module, a preprocessing module, a storage module, an analysis module, a process optimization module and a model construction module. The data acquisition module acquires doctor-seeing related data from a hospital information system, an electronic medical record system, a laboratory information management system, a medical image archiving and communication system, a patient mobile terminal application and hospital sensor equipment; the data preprocessing module receives the data and then performs cleaning, conversion and normalization processing; the data storage module adopts a distributed architecture, the data analysis module is connected with the storage module, the process optimization module constructs a mathematical model according to an analysis result, and the model construction module constructs a mathematical model according to the analysis result; the comprehensiveness of the data is ensured by collecting the data related to the treatment process from a plurality of data sources. And the data acquisition module has a data verification function and performs preliminary integrity and accuracy verification on the acquired data.

Description

Technical Field

[0001] This invention relates to the field of multi-source data integration and processing systems, specifically a multi-source data integration and processing system for optimizing and modeling medical treatment processes. Background Technology

[0002] With the continuous development of medical technology and the increasing demands of people for the quality of medical services, optimizing the medical process and improving medical efficiency have become important issues for hospital management. Currently, there are many problems in the hospital's medical process, such as long patient wait times, unreasonable departmental layouts, and unscientific doctor scheduling. These problems not only affect the patient's medical experience but also reduce the hospital's work efficiency.

[0003] Meanwhile, hospitals possess multiple data sources, including Hospital Information System (HIS), Electronic Medical Record System (EMR), Laboratory Information Management System (LIS), and Picture Archiving and Communication System (PACS), but these data sources are often independent of each other, making data integration and sharing difficult. Furthermore, existing medical data processing methods are relatively simplistic, lacking in-depth analysis and mining of multi-source data, and thus failing to provide strong support for optimizing and modeling the patient care process.

[0004] Therefore, there is a need for a multi-source data integration and processing system for optimizing and modeling the medical process, which can integrate multiple data sources in the hospital, conduct in-depth analysis and processing of the data, and provide a scientific basis for optimizing the medical process and improving medical efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-source data integration and processing system for optimizing and modeling the medical treatment process, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-source data integration and processing system for optimizing and modeling the medical treatment process, comprising data acquisition, preprocessing, storage, analysis, process optimization, and model building modules; The data acquisition module collects relevant medical data from the hospital information system, electronic medical record system, laboratory information management system, medical image archiving and communication system, patient mobile application, and hospital sensor equipment, and has data verification functions; it extracts basic patient information from the hospital information system; collects medical data from the electronic medical record system; obtains test results from the laboratory information management system; collects image data from the medical image archiving and communication system; provides appointment information from the patient mobile application; and collects hospital environment and traffic data from sensor equipment. After receiving the data, the data preprocessing module performs cleaning, transformation, and normalization processes. Cleaning includes removing duplicate data, correcting erroneous data, and handling missing values. Transforming the data into a unified format and normalizing it eliminates the influence of units. The data storage module adopts a distributed architecture, including a relational database for storing structured data, a non-relational database for storing semi-structured and unstructured data, and a distributed file system for storing large-scale image files, and has data backup and recovery functions; The data analysis module is connected to the storage module and uses data mining technology and machine learning algorithms to uncover potential patterns and rules, such as association rule mining to analyze the relationship between medical behavior and disease, cluster analysis of patient groups, regression analysis to predict medical time, and analysis of hospital resource utilization. Based on the analysis results, the process optimization module shortens the consultation time by redesigning procedures, adjusting department layout, and optimizing doctor scheduling, and introduces an intelligent guidance system to help patients quickly find their consultation location. The model building module uses the analysis results to construct mathematical models, simulate the medical treatment process under different circumstances, predict indicators to support hospital decision-making, and continuously adjust and optimize the model based on patient feedback and actual operation.

[0007] Furthermore, the data acquisition module employs an encrypted transmission protocol when collecting data from patients' mobile applications to ensure patient privacy and security. Simultaneously, blockchain technology is introduced for distributed storage and recording of the collected data, making the source and flow of the data traceable and enhancing its credibility. Moreover, an intelligent data filtering algorithm is developed to automatically select key data for priority collection based on the patient's health status and medical history, improving the targeting and efficiency of data collection.

[0008] Furthermore, during data cleaning, the data preprocessing module considers not only the exact sameness of the data when determining duplicate data, but also the timestamp and source channel. Data from the same source and with highly similar content within a certain time range are merged. When correcting erroneous data, an error data type library is established, and targeted corrections are made based on common error types to improve the accuracy and efficiency of data correction. In addition, an artificial intelligence-assisted error correction system is introduced, which automatically identifies and corrects potential erroneous data by learning from a large amount of historical data.

[0009] Furthermore, the non-relational database in the data storage module adopts a distributed architecture with automatic expansion capabilities. As the data volume increases, it can automatically add storage nodes to ensure that storage performance is not affected. At the same time, the stored image data is compressed using an efficient compression algorithm to reduce storage space usage without sacrificing image quality. Data access control is also set, with different levels of access permissions set for different types of data based on their sensitivity. Quantum encryption technology is creatively used to encrypt and store critical medical data, improving data security.

[0010] Furthermore, the data analysis module employs optimized algorithms to improve mining efficiency when performing association rule mining; it visualizes the mined association rules, allowing medical staff to intuitively understand the relationship between patients' medical behavior and diseases; simultaneously, during cluster analysis, it combines multi-dimensional data such as patients' demographic information, disease characteristics, and medical history to improve the accuracy and practicality of clustering; and it creatively introduces a neurofuzzy system to perform more accurate analysis and prediction of complex medical data.

[0011] Furthermore, when adjusting the department layout, the process optimization module comprehensively considers patient flow, interdepartmental relationships, and the hospital's spatial structure; it uses simulated annealing optimization algorithms to find the optimal department layout scheme; and when optimizing doctor scheduling, it fully considers doctors' professional expertise, workload balance, and peak patient demand periods to formulate a scientific and reasonable scheduling plan; it creatively introduces virtual reality technology to simulate and demonstrate the optimized department layout and doctor scheduling, identifying potential problems in advance and making adjustments accordingly.

[0012] Furthermore, the model building module incorporates deep learning algorithms when constructing mathematical models to improve the predictive accuracy of the models; it regularly evaluates and updates the models, continuously adjusting model parameters based on new data and actual operating conditions; it also sets up a model interpretation function to enable medical personnel to understand the decision-making basis of the models, thereby improving the credibility and acceptability of the models; and it creatively uses genetic algorithms to optimize model parameters and find the optimal model configuration.

[0013] Furthermore, a stable data transmission channel is established between the data acquisition module and each data source, and a data verification and retransmission mechanism is adopted to ensure the reliability of data transmission. For critical data, such as patients' urgent medical information, priority transmission is set to ensure that it is received and processed by the system in the shortest possible time. 5G communication technology is creatively adopted to improve the speed and stability of data transmission.

[0014] Furthermore, when dealing with missing values, the data preprocessing module, in addition to using conventional methods such as mean imputation, median imputation, or interpolation, also incorporates machine learning algorithms for predictive imputation; it trains models using existing complete data to predict missing values, thereby improving the completeness and accuracy of the data; and it creatively introduces generative adversarial networks to imput missing values, generating more realistic and reliable data.

[0015] Furthermore, the system includes a user feedback interface, allowing patients and medical staff to evaluate the system's performance and provide suggestions for improvement. Based on this feedback, the system promptly adjusts and optimizes the functions of each module, continuously improving system performance and user satisfaction. Simultaneously, a system operation log is established to record the system's operational status and key events, facilitating troubleshooting and performance analysis. Natural language processing technology is creatively applied to analyze and summarize user feedback, extracting key issues and suggestions to enhance the targeted nature of system optimization.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By collecting data related to the patient care process from multiple data sources, including hospital information systems, electronic medical record systems, laboratory information management systems, medical image archiving and communication systems, patient mobile applications, and various sensor devices within the hospital, the comprehensiveness of the data is ensured. The data acquisition module has a data verification function to perform preliminary integrity and accuracy checks on the collected data, thereby improving data accuracy.

[0017] The data preprocessing module cleans, transforms, and normalizes the collected data, removing duplicate data, correcting erroneous data, handling missing values, standardizing data format, eliminating the influence of data units, and improving the efficiency and quality of data processing.

[0018] The data storage module adopts a distributed storage architecture, including relational databases, non-relational databases, and distributed file systems, to meet the storage needs of different types of data and ensure data security and reliability. The data analysis module employs data mining techniques and machine learning algorithms to uncover potential patterns and regularities in the data. For example, association rule mining analyzes the correlation between patients' medical behavior and diseases; cluster analysis is used to divide patients into different groups; and regression analysis is used to predict patients' consultation and waiting times, providing a basis for optimizing hospital resource allocation and patient flow.

[0019] Based on the results from the data analysis module, the process optimization module streamlines the patient flow by redesigning procedures, adjusting department layouts, and optimizing doctor scheduling to shorten waiting times and improve efficiency. Simultaneously, an intelligent guidance system is introduced to help patients quickly locate their appointments, enhancing their overall healthcare experience.

[0020] The model building module utilizes the results from the data analysis module to construct a mathematical model of the patient visit process. This model can simulate the process under different conditions, predict indicators such as patient consultation time and waiting time, and provide support for hospital decision-making. Simultaneously, based on patient feedback and actual operational data, the model is continuously adjusted and optimized to improve its accuracy and practicality. Attached Figure Description

[0021] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 This invention provides a technical solution: a multi-source data integration and processing system for optimizing and modeling the medical treatment process, including a data acquisition module. For data collection from the Hospital Information System (HIS): First, establish standardized data interface specifications for interfacing with different versions and vendors of HIS systems. This specification should cover common data fields, such as basic patient information (name, age, gender, etc.), registration information (registration time, department, doctor, etc.), department information (department name, location, featured treatments, etc.), and doctor scheduling information (doctor's name, specialty, on-call time, etc.).

[0024] Establish a real-time monitoring mechanism to ensure that changes in data within the HIS system are promptly captured and added to this system, through periodic polling or event triggering. For example, when new patients register or doctor schedules are adjusted, the system can detect the changes and collect the data within minutes.

[0025] To ensure data accuracy, preliminary data verification is performed during the data collection process. For example, checking whether the patient's age is within a reasonable range and whether the department they wish to register with is listed in the hospital's department list.

[0026] Data acquisition for Electronic Medical Record (EMR) systems: Establish a secure and stable connection channel with the EMR system to ensure smooth access to detailed medical data such as patient history, diagnostic records, treatment plans, and medication information. This connection can employ encrypted network protocols to prevent data theft or tampering during transmission.

[0027] The collected electronic medical record data is verified using data validation algorithms. For example, diagnostic records are compared with a medical knowledge base to check the rationality and accuracy of the diagnosis. If a significant discrepancy is found between the diagnosis and the standard diagnosis in the knowledge base, the system can issue an alert to remind medical staff to conduct further verification.

[0028] Regarding medication use, analyze drug interactions to ensure there are no inappropriate drug combinations. A drug interaction database can be established; when new medication records are collected, they can be compared with the database to identify potential risks and promptly alert the doctor.

[0029] Data acquisition for the Laboratory Information Management System (LIS): Establish an efficient data transmission channel with the LIS system to ensure that patient test results are transmitted to this system in a timely manner. Asynchronous transmission can be used to avoid impacting the performance of the LIS system.

[0030] For critical testing indicators, such as blood routine tests and biochemical indicators, data encryption technology is used during transmission to ensure data security. At the same time, to ensure data integrity, data verification can be performed before and after transmission, such as calculating and comparing the hash values ​​of the data.

[0031] The collected test results data should be classified and organized for subsequent analysis and processing. For example, different types of test results can be stored in different data tables for easy querying and statistics.

[0032] Data acquisition for Picture Archiving and Communication Systems (PACS): Due to the large volume of image data, distributed data acquisition technology is employed. The image data is divided into multiple smaller blocks, and acquired in parallel from multiple nodes, improving acquisition efficiency. For example, multiple acquisition servers can simultaneously read image data blocks from the PACS system and then merge them locally.

[0033] Data compression and verification are performed during acquisition to ensure data integrity and accuracy. Efficient image compression algorithms, such as JPEG2000, are employed to reduce storage space usage without sacrificing image quality. Simultaneously, checksums for image data, such as CRC checks, are calculated and verified during storage and transmission; errors are promptly corrected by re-acquiring the data.

[0034] The acquired image data is labeled and categorized to facilitate subsequent querying and analysis. For example, labels can be created based on image type (CT, MRI, etc.), examination site, patient information, etc., to establish an index for rapid retrieval.

[0035] Data collection from patient mobile applications: Connect to the patient's mobile application via a secure API interface. This interface should employ encrypted transmission protocols, such as HTTPS, to ensure the privacy and security of patient data. Furthermore, implement strict access control for the interface, allowing only authorized users to access and transmit data.

[0036] Different data collection strategies are employed for patient appointment information, feedback, and evaluations of their medical experience. For appointment information, changes in the mobile application's database can be monitored in real time, and any new appointment records generated are immediately collected into the system. For feedback and evaluations of the medical experience, a scheduled collection method can be used, such as obtaining new feedback data from the mobile application at set times each day.

[0037] Natural Language Processing (NLP) techniques were employed to analyze patient feedback and evaluations of their medical experience. First, the text underwent preprocessing, including noise removal, word segmentation, and part-of-speech tagging. Then, sentiment analysis algorithms were used to extract patients' emotional states, such as satisfaction and dissatisfaction. Simultaneously, topic modeling algorithms were used to extract key themes from the feedback, such as doctor's attitude, the medical environment, and waiting time, providing insights for optimizing the medical process.

[0038] Data acquisition from sensor devices within the hospital: Real-time monitoring and data collection are performed on various sensor devices within the hospital. For example, temperature sensors can collect temperature data every few minutes; and people flow sensors can collect real-time data on people flow in different areas of the hospital.

[0039] Wireless transmission technology is used to transmit sensor data to this system. Low-power wireless communication technologies such as Bluetooth and ZigBee can be used to ensure the battery life of the sensor devices. Meanwhile, to ensure data stability and reliability, a data retransmission mechanism can be employed to automatically resend data when transmission fails.

[0040] For pedestrian flow sensor data, intelligent data analysis algorithms are used for real-time analysis and prediction. For example, time series analysis algorithms can be used to model pedestrian flow data and predict the trend of pedestrian flow changes in various areas over a future period. Based on the prediction results, a basis is provided for hospital resource allocation and process optimization, such as increasing medical staff in areas with high pedestrian flow and adjusting department layouts.

[0041] Data preprocessing module Data cleaning: Data deduplication: Establish a deduplication mechanism that considers not only data completeness but also timestamps and data sources. Data that is highly similar in content, originates from the same source, and occurs within a certain timeframe should be merged. For example, if the same patient information is collected from different data sources within a short period, only one copy should be retained, and other duplicate data should be marked as processed.

[0042] Correcting erroneous data: Establish a database of erroneous data types and implement targeted corrections based on common error types. For example, for patient age data, if negative ages or ages outside the reasonable range are found, corrections can be made by comparing with data from other data sources or using data validation algorithms. For erroneous diagnostic records in medical data, corrections can be made by comparing with a medical knowledge base.

[0043] Handling missing values: Depending on the characteristics of the data, methods such as mean imputation, median imputation, or interpolation can be used. For continuous data, interpolation is used to handle missing values, and appropriate interpolation is performed based on the data trend and correlation. For example, for patient body temperature data, if data for individual time points is missing, linear interpolation can be performed based on body temperature data from previous and subsequent time points. For discrete data, mode imputation or probability-based imputation methods are used. For example, if patient gender data is missing, it can be imputed based on the gender distribution of the patient's group.

[0044] Data transformation: For structured data, data mapping techniques are used to map fields from different data sources to standard fields. For example, there may be inconsistencies in the names of patient basic information fields in hospital information systems and electronic medical record systems. By establishing a field mapping table, fields from different systems can be uniformly mapped to standard patient basic information fields.

[0045] For semi-structured and unstructured data, data parsing techniques are used to extract key information and convert it into structured data. For example, for text data in electronic medical records, natural language processing techniques are used to parse it, extract key information such as disease diagnosis and treatment plans, and convert it into structured data format. For medical imaging data, feature information such as image type, examination site, and lesion characteristics can be extracted and converted into structured data to facilitate subsequent analysis and processing.

[0046] Normalization process: Methods such as linear normalization and logarithmic normalization are employed, with the appropriate normalization method selected based on the data's distribution characteristics. For numerical data, normalization ensures that the values ​​fall within the range of [0,1] or [-1,1], facilitating subsequent data analysis and model building. For example, linear normalization can be used to map patient body temperature data to the range [0,1]. For discrete data, one-hot encoding and other methods can be used to convert discrete categorical values ​​into numerical vectors, facilitating processing by machine learning algorithms.

[0047] Data storage module Storage in relational databases: Highly available database clustering technology is employed to ensure data security and reliability. For example, master-slave replication and read-write splitting techniques can be used to improve database performance and availability. When the master database fails, the slave database can automatically switch to master, ensuring the system's continued operation.

[0048] For frequently accessed data, caching techniques can be used to improve access speed. For example, memory caching can be used to cache patients' basic information and registration details in memory. When this data is needed, it can be retrieved directly from memory, improving query efficiency. At the same time, a cache update strategy should be set to ensure that the data in the cache remains consistent with the data in the database.

[0049] Design a reasonable table structure for structured data stored in relational databases. For example, create indexes and foreign key constraints based on data relationships and query requirements to improve data query and update efficiency. Simultaneously, partition the data, dividing large tables into partitions according to certain rules, such as by time or department, to improve data management and query performance.

[0050] Storage in NoSQL databases: NoSQL databases employ a distributed architecture, offering high scalability and flexibility to adapt to ever-increasing data volumes and diverse data types. For example, NoSQL databases such as MongoDB and Cassandra can be used, easily scaling storage nodes to meet the storage needs of large-scale data.

[0051] For semi-structured and unstructured data, such as text and image data in electronic medical records, appropriate data storage methods should be adopted. For text data, a document-oriented database can be used, treating each document as an independent storage unit for easy querying and updating. For image data, a binary large object (BLOB) storage method can be used, storing image files in the database and creating indexes for fast retrieval.

[0052] Design appropriate indexes for data stored in non-relational databases. Since the query methods for non-relational databases differ from those for relational databases, suitable indexes need to be created based on the characteristics of the data and query requirements. For example, for text data, a full-text index can be created to facilitate text search; for image data, feature-based indexes can be created to facilitate queries based on image features.

[0053] Storage in a distributed file system: Distributed file systems are used to store large-scale data such as image files. They offer high fault tolerance and high throughput, meeting the storage and access needs of large-scale data. For example, distributed file systems like HDFS can be used to divide image files into multiple smaller blocks and store them on different nodes, improving storage efficiency and access speed.

[0054] For image files, data compression techniques are used to reduce storage space usage. Efficient image compression algorithms, such as JPEG2000 and WebP, can be used to reduce storage space usage without sacrificing image quality. Simultaneously, data encryption techniques are employed to ensure data security. For example, symmetric encryption algorithms can be used to encrypt image files, allowing only users with the correct key to access and decrypt them.

[0055] Backup and recovery of data in distributed file systems are crucial. Because distributed file systems store large amounts of data, data loss or corruption can be difficult to recover. Therefore, a regular backup mechanism is needed to back up data to other storage media, such as tape libraries or cloud storage. Simultaneously, a data recovery strategy should be established to enable rapid data recovery in case of problems, ensuring the system's continued operation.

[0056] Data Analysis Module Association rule mining: Efficient association rule mining algorithms, such as the Apriori algorithm and the FP-Growth algorithm, are employed to uncover strong association rules between patients' medical visit behavior and their diseases. For example, minimum support and minimum confidence thresholds can be set; only association rules that meet these thresholds are considered strong association rules.

[0057] Visualizing the discovered association rules allows medical staff to intuitively understand the relationship between patient visits and diseases. Visualization tools such as charts and graphs can be used to present these rules in a clear and intuitive way. For example, bar charts can be used to show the strength of the association between different diseases and visit behaviors, and network diagrams can be used to show the relationships between diseases.

[0058] Apply association rules to meet actual business needs. For example, based on association rules, provide diagnostic suggestions to doctors, alerting them to potential illnesses when patients exhibit certain medical behaviors; or provide a basis for hospital resource allocation, such as increasing the stock of disease-related examination equipment and medications based on association rules.

[0059] Cluster analysis: Clustering algorithms, such as K-Means clustering and hierarchical clustering, are used to divide patients into different groups based on their condition, medical visit behavior, and other characteristics. Before performing clustering analysis, the data needs to be preprocessed, such as standardizing numerical data and vectorizing text data.

[0060] Clustering can be performed by combining multi-dimensional data such as patient demographics, disease characteristics, and medical history to improve the accuracy and practicality of clustering. For example, factors such as patient age, gender, disease type, and number of visits can be used as clustering features to divide patients into different groups. For each group, personalized treatment processes and service plans can be developed to improve patient satisfaction.

[0061] The clustering results should be evaluated and optimized. Clustering evaluation metrics, such as the silhouette coefficient, can be used to assess the quality of the clustering results. If the clustering results are unsatisfactory, the parameters of the clustering algorithm can be adjusted or new features can be added for re-clustering.

[0062] Regression analysis algorithms, such as linear regression and multiple regression, are used to establish mathematical models relating patient consultation time, waiting time, and various factors. Before building the model, feature selection and preprocessing of the data are necessary, such as removing noisy data and selecting features related to consultation time and waiting time.

[0063] By analyzing factors such as patient conditions, departments visited, and doctor schedules, the system predicts patient consultation and waiting times, providing a basis for hospital resource allocation and process optimization. For example, based on predicted consultation and waiting times, doctor schedules and resource allocation can be rationally arranged to reduce patient waiting times. Furthermore, introducing time series analysis methods, combined with historical consultation data trends, further improves the accuracy of predictions. For peak periods of seasonal illnesses or special timeframes, resource reserves and staffing can be prepared in advance.

[0064] Regression analysis considers not only the impact of individual factors on consultation time and waiting time, but also the interactions of multiple factors. For example, it examines the changes in waiting time for patients with specific conditions in specific departments and under specific doctor schedules. By establishing complex regression models, the relationships between various factors can be captured more comprehensively, providing strong support for precise resource allocation.

[0065] The regression model is visualized to intuitively present the relationship between different factors and consultation time and waiting time. Visualization tools such as heatmaps and scatter plots can be used to help hospital administrators and medical staff quickly understand the model results, thereby enabling them to make better decisions.

[0066] Continuously optimize the parameters and structure of the regression model. Regularly update and adjust the model by collecting new data and patient feedback. For example, if a new factor is found to have a significant impact on consultation time and waiting time, it should be promptly incorporated into the model for analysis. Simultaneously, methods such as cross-validation are used to evaluate the model's stability and generalization ability, ensuring that the model can accurately predict consultation time and waiting time under different scenarios.

[0067] In addition, an early warning mechanism is established using the results of regression analysis. When the predicted consultation time or waiting time exceeds a certain threshold, the system automatically issues an early warning, reminding hospital administrators to take corresponding measures, such as increasing temporary medical staff or adjusting departmental resource allocation, to avoid excessively long patient waiting times and improve the patient experience and hospital service efficiency.

[0068] Resource utilization analysis: Data analytics tools and visualization technologies are used to monitor and analyze hospital resource utilization in real time. For example, dashboards and reports can be used to display resource utilization data such as doctors' workload and equipment utilization rates. Real-time monitoring allows for the timely identification of inefficient resource utilization and the implementation of appropriate adjustments.

[0069] In analyzing physician workload, in addition to the number of patients seen, working hours, and overtime, we can also consider physicians' expertise and efficiency in treating different diseases. Establishing a physician professional skills matrix, based on physicians' past diagnostic accuracy and treatment outcomes for various diseases, allows for the allocation of the most suitable physicians to specific diseases, improving overall medical quality and efficiency. Simultaneously, analyzing the distribution of physicians' workload across different time periods allows for the rational allocation of rest time and personnel, preventing physician overwork.

[0070] Analysis of equipment utilization should not only focus on usage time and frequency, but also consider the impact of equipment maintenance cycles and failures on resource utilization. Establishing an equipment maintenance early warning system can predict potential equipment failure times based on operating time and historical failure data, allowing maintenance personnel to perform preventative maintenance and reduce resource waste caused by equipment failures. Analyzing the differences in equipment needs among different departments allows for reasonable adjustments to equipment allocation, ensuring balanced resource utilization.

[0071] We analyze doctors' workloads, including the number of patients they see, working hours, and overtime. Based on doctors' specialties and workloads, we can rationally adjust their shifts to ensure a balanced workload for each doctor. We can also provide suggestions for improving doctors' efficiency, such as optimizing consultation processes and scheduling rest periods more effectively.

[0072] By analyzing doctors' consultation records and patient feedback, bottlenecks in the consultation process were identified, such as excessively long periods of time spent filling out medical records and waiting times for test results. To address these issues, the intelligent functions of the electronic medical record system were introduced to automatically fill in some routine information, reducing doctors' manual input time. The examination process was optimized to enable rapid transmission and sharing of test results, shortening doctors' waiting time. Personalized suggestions for improving doctors' work efficiency were provided, such as recommending the most suitable consultation process and communication methods based on doctors' work habits and patient types.

[0073] Analyzing equipment utilization, including usage time, frequency, and malfunctions, allows for the rational scheduling of maintenance and upgrades to ensure normal equipment operation. Furthermore, it optimizes equipment allocation and usage, improving overall equipment utilization.

[0074] Establish an equipment sharing platform to enable the sharing and allocation of equipment among different departments. When equipment utilization in one department is low while other departments urgently need equipment, the platform can be used to quickly allocate equipment, improving overall equipment utilization. Analyze equipment failure modes and causes to provide feedback to equipment suppliers, prompting them to improve equipment quality and after-sales service. Evaluate aging equipment and, based on its performance and maintenance costs, decide whether to upgrade it, ensuring the hospital has advanced and reliable medical equipment.

[0075] Process optimization module Redesign of the medical visit process We comprehensively reviewed every step of the medical process, conducting a detailed analysis of the entire journey from when a patient enters the hospital to when they leave. We identified stages that could lead to wasted time and patient dissatisfaction, such as queuing for registration, payment, and appointment scheduling.

[0076] The registration process has been simplified, and online registration platforms have been promoted to allow patients to book appointments in advance, reducing on-site queuing time. At the same time, the interface design of the registration system has been optimized to make it more concise, easy to understand, and convenient for patients to use.

[0077] The payment process is integrated to unify various payment methods, such as cash, bank cards, and mobile payments. Patients can pay at multiple locations during their visit, avoiding long queues at specific payment windows.

[0078] For examination appointments, an intelligent appointment system will be established to automatically schedule the most reasonable examination time based on the patient's condition and the doctor's advice, thereby reducing the patient's waiting time.

[0079] Departmental layout adjustment Analyze patient flow and the correlation between departments to determine the optimal department layout. For example, placing related departments in adjacent locations facilitates patient referrals between different departments.

[0080] By analyzing patient visit paths using data, areas where patients walk longer can be identified, allowing for optimization and adjustments to the department layout. This can be achieved by relocating departments, adding corridors, and other methods to shorten patient walking distances.

[0081] Considering the hospital's spatial structure and future development needs, conduct long-term planning for the department layout. Reserve sufficient space to allow for smooth layout adjustments when adding new departments or equipment in the future.

[0082] During the department layout adjustment process, emphasis was placed on improving the signage system. Clear and unambiguous signs were installed to guide patients to quickly find the departments they need, reducing getting lost and wasting time.

[0083] Doctor scheduling optimization Doctors' shifts should be scheduled reasonably according to peak patient demand. For example, during peak morning and afternoon hours, the number of doctors on duty should be increased to ensure that there are enough doctors available to serve patients.

[0084] A scientific scheduling system should be implemented, taking into account doctors' professional expertise and workload. Doctors specializing in specific diseases should be assigned to relevant departments to improve diagnostic and treatment efficiency. At the same time, overwork should be avoided by ensuring doctors have adequate rest time.

[0085] Establish a dynamic scheduling mechanism to adjust doctors' schedules promptly based on real-time patient flow and changes in patient conditions. For example, when the number of patients in a certain department suddenly increases, doctors from other departments can be temporarily reassigned to provide support.

[0086] By leveraging data analysis to assess physician efficiency and patient satisfaction, we can continuously optimize physician scheduling. Based on feedback, we adjust physician shift times and workload allocation to improve the overall quality of medical services.

[0087] Introduction of intelligent guidance system: Electronic navigation signs are installed within the hospital, utilizing intelligent positioning technology to provide patients with real-time navigation guidance. Patients can quickly locate departments, examination rooms, payment counters, and other locations using the signs.

[0088] Develop a mobile application for patients to provide guidance on their medical visits. Patients can view their medical process, department locations, queue status, and other information on their mobile phones, while also receiving real-time navigation prompts.

[0089] The intelligent guidance system is integrated with the hospital's information system to enable real-time information updates and sharing. For example, when a doctor's consultation progress changes, the system can promptly notify the patient, allowing them to plan their time accordingly.

[0090] Training hospital staff will enable them to skillfully use the intelligent guidance system to provide better service to patients. Simultaneously, the guidance system will be regularly maintained and upgraded to ensure its stable operation and full functionality.

[0091] Model building module Mathematical model construction: A mathematical model of the patient visit process is constructed using the results of the data analysis module. This model can simulate the patient visit process under different circumstances, predict indicators such as patient consultation time and waiting time, and provide support for hospital decision-making.

[0092] Mathematical models are constructed using methods such as system dynamics and discrete event simulation. System dynamics models can consider the dynamic relationships between various factors, such as patient flow, doctor scheduling, and equipment utilization, and provide a reference for long-term hospital planning by simulating the impact of different policies and measures on the patient flow. Discrete event simulation models can simulate patient waiting and processing times at each stage of the patient journey, and evaluate the effectiveness of different optimization schemes by adjusting parameters and assumptions.

[0093] By adjusting the parameters in the model, changes in the patient flow can be simulated under conditions such as increasing the number of doctors and optimizing the department layout, and the effectiveness of these measures can be evaluated.

[0094] Establish a parameter sensitivity analysis mechanism to analyze the degree of influence of different parameters on the model results. For example, the impact of increasing the number of doctors on patient waiting time, and the impact of adjusting the department layout on patient walking distance. Through sensitivity analysis, it is possible to determine which parameters are most critical for optimizing the medical process, providing a scientific basis for hospital decision-making.

[0095] The model building module can also continuously adjust and optimize the model based on patient feedback and actual operation, thereby improving the model's accuracy and practicality.

[0096] Establish channels for collecting patient feedback, such as online surveys and suggestion boxes, to gather patient satisfaction and suggestions regarding the treatment process. Incorporate patient feedback into the model optimization process; for example, adjust model parameters and optimize the treatment process based on patient dissatisfaction with waiting times. Simultaneously, regularly validate and update the model using real-world data to ensure it accurately reflects actual conditions.

[0097] Model interpretation function: The model interpretation function is set up so that medical staff can understand the decision-making basis of the model, thereby improving the credibility and acceptability of the model.

[0098] Visualization techniques are employed to showcase the model's internal structure and decision-making process. For example, flowcharts and diagrams are used to illustrate the relationships and influence paths among the various factors in the model. Detailed model documentation and explanations are provided to medical personnel, clarifying the model's assumptions, parameter settings, and calculation methods. Model training courses are offered to help medical personnel understand the model's fundamental principles and application methods, enhancing their comprehension and trust in the model's results.

[0099] Model optimization methods: Genetic algorithms are creatively used to optimize model parameters and find the optimal model configuration.

[0100] Genetic algorithms are optimization algorithms based on the principles of biological evolution. They find the optimal combination of model parameters by simulating the processes of natural selection and genetic mutation. Model parameters are encoded as chromosomes, and the performance of different parameter combinations is evaluated by defining a fitness function. Then, selection, crossover, and mutation operations are performed to progressively optimize the model parameters. Compared with traditional optimization methods, genetic algorithms have advantages such as strong global search capabilities and less susceptibility to getting trapped in local optima.

[0101] Introducing deep learning algorithms improves the model's prediction accuracy.

[0102] Deep learning algorithms possess powerful feature extraction and pattern recognition capabilities, automatically learning complex relationships and patterns from massive amounts of data. During model building, deep learning algorithms can be used to analyze and predict patient visit data, such as predicting disease type and appointment time. Furthermore, deep learning algorithms can be combined with traditional statistical models to improve model accuracy and generalization ability.

[0103] Model evaluation and updates: The model should be evaluated and updated regularly, and the model parameters should be continuously adjusted based on new data and actual operating conditions.

[0104] Establish a model evaluation index system, such as mean squared error, mean absolute error, and coefficient of determination, and regularly evaluate the model's predictive performance. If the model's performance deteriorates or the actual situation changes, update and adjust the model promptly. Online learning methods can be used to update model parameters in real time as new data accumulates, ensuring that the model always reflects the actual situation.

[0105] A model-based early warning mechanism is set up so that when the model's predictions deviate significantly from the actual situation, an alarm is issued in a timely manner to remind medical staff to conduct checks and make adjustments.

[0106] Define warning thresholds and rules. When the model's predicted consultation time, waiting time, or other indicators differ from the actual situation by more than a certain range, an warning mechanism is triggered. Medical staff can use the warning information to promptly check for problems in data collection, processing, and model building, and take corresponding measures for adjustment and optimization.

Claims

1. A multi-source data integration and processing system for optimizing and modeling the medical treatment process, characterized in that, It includes modules for data acquisition, preprocessing, storage, analysis, process optimization, and model building; The data acquisition module collects relevant medical data from the hospital information system, electronic medical record system, laboratory information management system, medical image archiving and communication system, patient mobile application, and hospital sensor equipment, and has data verification functions; it extracts basic patient information from the hospital information system; collects medical data from the electronic medical record system; obtains test results from the laboratory information management system; collects image data from the medical image archiving and communication system; provides appointment information from the patient mobile application; and collects hospital environment and traffic data from sensor equipment. After receiving the data, the data preprocessing module performs cleaning, transformation, and normalization processes. Cleaning includes removing duplicate data, correcting erroneous data, and handling missing values. Transforming the data into a unified format. Normalization eliminates the influence of dimensions; The data storage module adopts a distributed architecture, including a relational database for storing structured data, a non-relational database for storing semi-structured and unstructured data, and a distributed file system for storing large-scale image files, and has data backup and recovery functions; The data analysis module is connected to the storage module and uses data mining technology and machine learning algorithms to uncover potential patterns and rules, such as association rule mining to analyze the relationship between medical behavior and disease, cluster analysis of patient groups, regression analysis to predict medical time, and analysis of hospital resource utilization. Based on the analysis results, the process optimization module shortens the consultation time by redesigning procedures, adjusting department layout, and optimizing doctor scheduling, and introduces an intelligent guidance system to help patients quickly find their consultation location. The model building module uses the analysis results to construct mathematical models, simulate the medical treatment process under different circumstances, predict indicators to support hospital decision-making, and continuously adjust and optimize the model based on patient feedback and actual operation.

2. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The data acquisition module employs an encrypted transmission protocol to ensure patient privacy and security when collecting data from patients' mobile applications. Simultaneously, blockchain technology is introduced for distributed storage and recording of the collected data, making the source and flow of the data traceable and enhancing its credibility. Furthermore, an intelligent data filtering algorithm is developed to automatically select key data for priority collection based on the patient's health status and medical history, improving the targeting and efficiency of data collection.

3. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, When performing data cleaning, the data preprocessing module determines duplicate data not only based on complete data accuracy but also by considering timestamps and source channels. Data from the same source that are highly similar within a certain time frame and with similar content are merged. When correcting erroneous data, an error data type library is established, and targeted corrections are performed based on common error types to improve the accuracy and efficiency of data correction. In addition, an artificial intelligence-assisted error correction system is introduced, which automatically identifies and corrects potential erroneous data by learning from a large amount of historical data.

4. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The non-relational database in the data storage module adopts a distributed architecture with automatic expansion capabilities. As the data volume increases, it can automatically add storage nodes to ensure that storage performance is not affected. At the same time, the stored image data is compressed using an efficient compression algorithm to reduce storage space usage without sacrificing image quality. Furthermore, data access control is set, with different levels of access permissions set for different types of data based on their sensitivity. Quantum encryption technology is creatively used to encrypt and store critical medical data, improving data security.

5. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The data analysis module employs optimized algorithms to improve mining efficiency when performing association rule mining; it visualizes the mined association rules, allowing medical staff to intuitively understand the relationship between patients' medical behavior and diseases; simultaneously, when performing cluster analysis, it combines multi-dimensional data such as patients' demographic information, disease characteristics, and medical history to improve the accuracy and practicality of clustering; and it creatively introduces a neurofuzzy system to perform more accurate analysis and prediction of complex medical data.

6. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, When adjusting department layouts, the process optimization module comprehensively considers patient flow, interdepartmental relationships, and the hospital's spatial structure; it uses simulated annealing optimization algorithms to find the optimal department layout scheme; and when optimizing doctor scheduling, it fully considers doctors' professional expertise, workload balance, and peak patient demand periods to formulate a scientific and reasonable scheduling plan; it creatively introduces virtual reality technology to simulate and demonstrate the optimized department layouts and doctor scheduling, identifying potential problems in advance and making adjustments accordingly.

7. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The model building module incorporates deep learning algorithms to improve the predictive accuracy of mathematical models; it regularly evaluates and updates models, continuously adjusting model parameters based on new data and actual operating conditions; it also includes a model interpretation function to enable medical personnel to understand the decision-making basis of the model, thereby improving the model's credibility and acceptability; and it creatively uses genetic algorithms to optimize model parameters and find the optimal model configuration.

8. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The data acquisition module establishes a stable data transmission channel with each data source and adopts a data verification and retransmission mechanism to ensure the reliability of data transmission. For critical data, such as patients' urgent medical information, priority transmission is set to ensure that it is received and processed by the system in the shortest possible time. 5G communication technology is creatively adopted to improve the speed and stability of data transmission.

9. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, In handling missing values, the data preprocessing module, in addition to conventional methods such as mean imputation, median imputation, or interpolation, also incorporates machine learning algorithms for predictive imputation; it trains models using existing complete data to predict missing values, thereby improving the completeness and accuracy of the data; and it creatively introduces generative adversarial networks to imput missing values, generating more realistic and reliable data.

10. The multi-source data integration and processing system for optimizing and modeling the medical treatment process according to claim 1, characterized in that, The system includes a user feedback interface, through which patients and medical staff can evaluate the system's performance and provide suggestions for improvement. Based on the feedback, the system adjusts and optimizes the functions of each module in a timely manner, continuously improving system performance and user satisfaction. Meanwhile, a system operation log is established to record the system's operating status and key events, facilitating troubleshooting and performance analysis; natural language processing technology is creatively used to analyze and summarize user feedback, extract key issues and suggestions, and improve the targeting of system optimization.

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