Medical record homepage-driven subject comprehensive evaluation management system

Abstract

The invention provides a medical record homepage driven subject comprehensive evaluation management system, which comprises a medical record homepage data input interface module, a medical record homepage data output interface module, a medical record homepage data input interface module, a medical record homepage data output interface module and a medical record homepage data output interface module, and the data analysis and standardization module is coupled to the medical record home page data input interface module and is configured to perform analysis, cleaning and standardization processing on the received medical record home page data so as to eliminate data inconsistency and errors. Automation and intelligentization of a multidisciplinary evaluation process are realized through medical record home page data driving, medical work efficiency and evaluation timeliness are remarkably improved, the system can monitor change of the medical record home page data in real time, the evaluation process is automatically triggered once a preset rule is met, and experts and resources of related disciplines are intelligently scheduled, so that the medical record home page data change can be monitored in real time. A traditional low-efficiency mode which depends on manual initiation is thoroughly changed, and medical staff are liberated from tedious coordination and communication affairs.

Description

Technical Field

[0001] This invention relates to the field of medical auxiliary assessment technology, and in particular to a subject-based comprehensive assessment management system driven by a medical record homepage. Background Technology

[0002] Traditional hospital subject assessment management relies heavily on post-hoc manual statistics and reporting, which is inefficient and time-consuming. Specifically, the value of the patient's medical record cover sheet, as the core carrier of patient diagnosis and treatment information, is usually limited to medical statistics, cost settlement, and archiving. It fails to fully leverage its real-time driving role in clinical quality management. When multidisciplinary comprehensive assessments of complex cases are required, the attending physician usually makes a subjective judgment and then manually contacts relevant subject experts through traditional methods such as telephone and email to coordinate meeting times and venues. The entire process consumes a lot of manpower and resources, has high communication costs, and heavily relies on individual experience and initiative, which can easily lead to delays or omissions in assessment timing. In addition, the patient information used by experts in different disciplines may be different versions or incomplete. The assessment results are mostly fragmented text records, which are difficult to effectively integrate into a structured comprehensive treatment plan, and cannot form a closed loop with the data on the medical record cover sheet. This results in inconsistent assessment quality, and valuable clinical data assets cannot be transformed into a continuous driving force for improving medical quality.

[0003] Therefore, in order to address the above issues, a comprehensive subject assessment management system driven by the medical record homepage is now being developed. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a comprehensive subject assessment management system driven by a medical record homepage.

[0005] The technical solution of this invention is: a subject-based comprehensive assessment management system driven by a medical record homepage, comprising: The medical record front page data input interface module is configured to receive and extract medical record front page data from the hospital information system or electronic health record system; The data parsing and standardization module is coupled to the medical record front page data input interface module and is configured to parse, clean and standardize the received medical record front page data to eliminate data inconsistencies and errors. An assessment trigger engine module, coupled to the data parsing and standardization module, is configured to automatically determine whether to initiate a multidisciplinary comprehensive assessment based on the parsed medical record front page data. The assessment trigger engine module includes a configurable rule base that stores multiple assessment trigger rules. Each rule defines one or more trigger conditions, which are based on specific thresholds or events in the medical record front page data. A multidisciplinary assessment scheduling module, coupled to the assessment triggering engine module, is configured to automatically schedule and coordinate multiple subject experts to participate in a comprehensive assessment in response to the assessment triggering signal. The multidisciplinary assessment scheduling module includes a subject selection submodule, a time scheduling submodule, and a resource allocation submodule. An assessment execution and integration module, coupled to the multidisciplinary assessment scheduling module, is configured to provide a collaborative work platform for multidisciplinary experts to perform assessments and integrate assessment results. The assessment execution and integration module includes an assessment form generation submodule, a real-time data sharing submodule, and a result integration submodule. An output and reporting module, coupled to the assessment execution and integration module, is configured to output a comprehensive assessment report to relevant medical personnel or systems, wherein the output and reporting module supports multiple output formats; The system control and monitoring module is configured to monitor the overall system operation status, including data flow, processing logs, and error handling.

[0006] As a preferred embodiment of the present invention, the medical record homepage data input interface module further includes a data verification submodule, which is configured to perform real-time verification on the input medical record homepage data to ensure the integrity and accuracy of the data. The data verification submodule uses a rule engine to check the data format, value range, and logical consistency.

[0007] As a preferred embodiment of the present invention, the rule base of the evaluation trigger engine module supports dynamic updates and customization, allowing administrators to add, modify, or delete trigger rules through a graphical user interface. Each trigger rule includes a condition part and an action part, wherein the condition part uses logical expressions to define a combination of conditions based on the medical record homepage data.

[0008] As a preferred embodiment of the present invention, the subject selection submodule of the multidisciplinary assessment and scheduling module adopts a subject mapping table, which defines the correspondence between different disease types or diagnostic codes and recommended subjects.

[0009] As a preferred embodiment of the present invention, the evaluation form generation submodule of the evaluation execution and integration module dynamically creates evaluation forms based on a template library. The template library stores various subject-specific evaluation templates, each template including standardized evaluation items, scoring criteria, and input fields. Furthermore, the evaluation form generation submodule allows experts to add custom evaluation items in real time during the evaluation process. The real-time data sharing submodule uses WebSocket technology to achieve real-time collaboration among multiple users, supports simultaneous editing and version control, and ensures data synchronization. The result integration submodule uses a rule engine or a simple weighted algorithm to aggregate evaluation results and includes a conflict resolution mechanism. When evaluation results from different subjects are inconsistent, conflicts are automatically marked and a coordination meeting is recommended.

[0010] As a preferred embodiment of the present invention, the output and reporting module further includes a data visualization submodule configured to present the comprehensive evaluation report in a graphical form.

[0011] As a preferred embodiment of the present invention, the system control and monitoring module includes a performance analysis submodule, which is configured to collect system operation indicators, such as the number of triggers, processing time and user activity logs, and use statistical analysis tools to generate performance reports to help administrators optimize system configuration. The user authentication and authorization submodule implements role-based access control, defines the permission levels of different user roles to ensure data security and compliance, and integrates audit trail functions to record all data access and modification operations.

[0012] As a preferred embodiment of the present invention, the system further includes a mobile extension module configured to provide remote access to evaluation functions via a mobile application. The mobile extension module includes an offline data synchronization function, allowing experts to perform some evaluation operations in a network-free environment and automatically synchronize data when the network is restored. The mobile extension module also supports voice input and handwriting recognition to facilitate rapid data entry and integrates geolocation services to track expert locations for optimized scheduling.

[0013] As a preferred embodiment of the present invention, the system further integrates a predictive analysis module, which is configured to predict the patient's condition development trend or assessment needs based on historical medical records and assessment data using a machine learning model. The predictive analysis module employs a regression algorithm or a classification algorithm to output a predictive score and feeds the prediction results back to the assessment triggering engine module to trigger preventive assessments in advance, thereby achieving proactive medical management.

[0014] As a preferred embodiment of the present invention, the system further includes an interoperability interface module configured to exchange data with an external medical system. The interoperability interface module supports standard medical data protocols to achieve seamless data integration. The module also includes a data mapping engine that automatically converts external data into the system's internal format, ensuring that comprehensive and multi-source patient information can be obtained during the evaluation process, thereby improving the accuracy and completeness of the evaluation.

[0015] By adopting the above technical solution, the present invention has the following advantages: 1. This invention automates and automates the multidisciplinary assessment process by driving the data on the medical record homepage, significantly improving the efficiency and timeliness of medical work. The system can monitor changes in the medical record homepage data in real time, and automatically triggers the assessment process once the preset rules are met. It also intelligently dispatches relevant discipline experts and resources, completely changing the inefficient traditional model that relies on manual initiation and freeing medical staff from tedious coordination and communication tasks.

[0016] 2. This invention significantly improves the consistency and scientific rigor of multidisciplinary assessments through an integrated collaborative platform and standardized data processing. The system provides experts with a collaborative working environment based on unified, real-time, and comprehensive patient data, ensuring information synchronization among all participants and avoiding judgment biases caused by fragmented information.

[0017] 3. This invention provides strong data support for refined hospital management and continuous improvement of medical quality through in-depth data mining and closed-loop management. It fully digitizes the key medical activity of multidisciplinary assessment, and the resulting structured assessment results form a closed-loop association with the original data on the medical record front page. This provides a high-quality big data source for clinical research, disease management, discipline development, and hospital performance evaluation, which is conducive to discovering diagnosis and treatment patterns, optimizing clinical pathways, and realizing forward-looking resource allocation. Ultimately, it promotes the transformation of medical institutions from experience-based management to data-driven scientific management. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the first partial structure of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of the multidisciplinary evaluation and scheduling module of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of the evaluation execution and integration module of the present invention.

[0021] Figure 4 This is a schematic diagram of the second partial structure of the present invention. Detailed Implementation

[0022] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] A multidisciplinary comprehensive assessment management system driven by hospital medical record homepage data is configured to automatically trigger and execute multidisciplinary comprehensive assessment processes based on hospital medical record homepage data, in order to achieve comprehensive, real-time, and collaborative management of patient conditions, such as... Figures 1-3As shown, the system includes: a medical record front page data input interface module, configured to receive and extract medical record front page data from a hospital information system or electronic health record system. The medical record front page data includes at least the patient's basic information, primary diagnosis code, complication information, treatment records, admission date, discharge date, and key vital signs. A data parsing and standardization module, coupled to the medical record front page data input interface module, is configured to parse, clean, and standardize the received medical record front page data to eliminate data inconsistencies and errors. The data parsing and standardization module uses natural language processing algorithms to identify and extract key medical terms and convert unstructured data into structured data. The system includes a format, such as encoding diagnostic information using the International Classification of Diseases (ICD) code; an assessment trigger engine module, coupled to the data parsing and standardization module, configured to automatically determine whether to initiate a multidisciplinary assessment based on the parsed medical record front page data. This assessment trigger engine module includes a configurable rule base storing multiple assessment trigger rules, each defining one or more trigger conditions. These trigger conditions are based on specific thresholds or events in the medical record front page data, such as when the patient's primary diagnosis falls into a high-risk disease category, when the number of complications exceeds a preset value, or when treatment records indicate a worsening condition. The system automatically generates an assessment trigger signal. A multidisciplinary assessment scheduling module, coupled to the assessment... The assessment trigger engine module is configured to automatically schedule and coordinate experts from multiple disciplines to participate in comprehensive assessments in response to assessment trigger signals. The multidisciplinary assessment scheduling module includes a discipline selection submodule, a scheduling submodule, and a resource allocation submodule. The discipline selection submodule is configured to select relevant disciplines from a preset discipline library, such as internal medicine, surgery, nursing, nutrition, and rehabilitation, based on diagnostic and treatment information in the patient record homepage data, and determine the type of assessment expert required for each discipline. The scheduling submodule is configured to automatically generate an assessment schedule based on the hospital's work schedule and expert availability, and send notifications to the relevant experts. The resource allocation submodule is configured to allocate the physical resources required for the assessment. Such as meeting rooms or medical equipment; the assessment execution and integration module, coupled to the multidisciplinary assessment scheduling module, is configured to provide a collaborative work platform for multidisciplinary experts to perform assessments and integrate assessment results. The assessment execution and integration module includes an assessment form generation submodule, a real-time data sharing submodule, and a result integration submodule. The assessment form generation submodule is configured to dynamically generate standardized assessment forms based on medical record front page data and disciplinary requirements. The real-time data sharing submodule is configured to allow experts to access and update patient data in real time during the assessment process. The result integration submodule is configured to summarize the assessment results of various disciplines, perform conflict detection and consistency checks, and generate a unified comprehensive assessment report.The output and reporting module, coupled to the assessment execution and integration module, is configured to output comprehensive assessment reports to relevant medical personnel or the system. This module supports multiple output formats, including electronic documents, printed reports, and API interface transmission, and includes a report customization submodule, allowing users to customize report content according to their needs. The system control and monitoring module is configured to monitor the overall system operation, including data flow, processing logs, and error handling. This module includes a user authentication and authorization submodule, ensuring that only authorized personnel can access the system, and that all operations comply with medical data privacy regulations. The system's innovation lies in its data-driven approach to multidisciplinary assessment, achieving automation and intelligence, reducing manual intervention, and improving assessment efficiency and accuracy. Furthermore, its integrated design ensures the synergy of the assessment process and data consistency.

[0024] It should be noted that the medical record front page data input interface module further includes a data validation submodule. This submodule is configured to perform real-time validation of the input medical record front page data to ensure data integrity and accuracy. The data validation submodule uses a rule engine to check data format, value range, and logical consistency. For example, it verifies whether the diagnostic code conforms to ICD standards, whether the date field is reasonable, and whether any required fields are missing. When data errors are detected, the data validation submodule automatically generates an error report and triggers a data correction process. This prompts the operator to manually correct the data via the user interface or automatically corrects it by calling an external system via API, thereby preventing invalid data from entering the system and affecting the evaluation results. The rule base of the engine module supports dynamic updates and customization, allowing administrators to add, modify, or delete trigger rules through a graphical user interface. Each trigger rule includes a condition part and an action part. The condition part uses logical expressions to define combinations of conditions based on the medical record front page data, such as "trigger assessment if the primary diagnosis code is I10 (essential hypertension) and the systolic blood pressure reading is greater than 180 mmHg". The action part specifies the operation after triggering the assessment, such as setting the assessment priority or specifying the participating disciplines. Furthermore, the rule base integrates machine learning algorithms, which can automatically optimize trigger rules based on historical assessment data to improve the accuracy and adaptability of triggering. The discipline selection submodule of the multidisciplinary assessment scheduling module uses a discipline mapping table, which defines... The system defines the correspondence between different disease types or diagnostic codes and recommended disciplines. For example, for cardiovascular diseases, the mapping table automatically associates cardiology, nursing, and nutrition. The discipline selection submodule supports weight allocation, assigning weights to each discipline based on the severity index in the medical record homepage data to determine the priority of discipline participation. Simultaneously, the scheduling submodule integrates with the hospital calendar system, using greedy or genetic algorithms for automatic scheduling to minimize assessment delays and resource conflicts, and pushes notifications to experts via SMS, email, or mobile application. The assessment execution and integration module's assessment form generation submodule dynamically creates assessment forms based on a template library, which stores various discipline-specific assessment templates, each including standard... The system includes standardized assessment items, scoring criteria, and input fields. The assessment form generation submodule allows experts to add custom assessment items in real-time during the assessment process. The real-time data sharing submodule uses WebSocket technology to enable multi-user real-time collaboration, supporting simultaneous editing and version control to ensure data synchronization. The results integration submodule uses a rule engine or simple weighted algorithm to aggregate assessment results and includes a conflict resolution mechanism that automatically flags conflicts and suggests coordination meetings when assessment results from different disciplines are inconsistent. The output and reporting module further includes a data visualization submodule, configured to present the comprehensive assessment report graphically, such as using charts, dashboards, or heatmaps to display assessment result trends and key indicators.Furthermore, the report customization submodule allows users to select report elements via drag-and-drop interface, such as which disciplines to include assessment details, time ranges, or comparative data. The output and reporting module supports automatic report export to electronic medical record systems or hospital management platforms, ensuring secure data transmission through encrypted channels. The system control and monitoring module includes a performance analysis submodule, configured to collect system performance metrics such as assessment trigger counts, processing times, and user activity logs, and generate performance reports using statistical analysis tools to help administrators optimize system configurations. The user authentication and authorization submodule implements role-based access control, defining permission levels for different user roles (e.g., doctors, nurses, administrators) to ensure data security and compliance, while also integrating audit trail functionality to record all data access and modification operations.

[0025] like Figure 4 As shown, the system also includes a mobile extension module configured to provide remote access to assessment functions via a mobile application. This module includes offline data synchronization, allowing experts to perform partial assessments in offline environments and automatically synchronizing data when the network is restored. The mobile extension module supports voice input and handwriting recognition for rapid data entry and integrates geolocation services to track expert locations and optimize scheduling. The system further integrates a predictive analytics module configured to predict patient condition trends or assessment needs based on historical medical records and assessment data using machine learning models. This predictive analytics module employs regression or classification algorithms, such as random forests or neural networks, to output predicted scores and feeds the results back to the assessment triggering engine module for proactive preventative assessments, thus enabling forward-looking medical management. The system also includes an interoperability interface module configured to exchange data with external medical systems (such as laboratory information systems and image archiving systems). This interoperability interface module supports standard medical data protocols, such as HL7. FHIR enables seamless data integration, and the module includes a data mapping engine that automatically converts external data into the system's internal format, ensuring that comprehensive, multi-source patient information is available during the assessment process, thereby improving the accuracy and completeness of the assessment.

[0026] It should be noted that after the system starts, the medical record homepage data input interface module continuously monitors or periodically polls the HIS / EMR database to capture newly generated or updated medical record homepage records. This module doesn't simply passively receive data; instead, it actively initiates an initial handshake and verification. For example, when a patient is admitted, their basic information and preliminary diagnosis are entered into the system, and an initial medical record cover sheet begins to form. As treatment progresses, surgical records, complication information, and key test results continuously update this cover sheet. The input interface module retrieves this data through predefined APIs or database connectors. Its built-in data verification submodule immediately activates the first line of defense, performing real-time cleaning of the data stream: checking the existence of required fields (such as patient ID and diagnosis code), verifying whether the code format conforms to ICD-10 or local specifications, and judging whether the date logic is reasonable (e.g., the surgery date cannot be earlier than the admission date). If obvious errors or omissions are found, such as an empty diagnosis code, the module temporarily stores the data and immediately sends an anomaly alert to the source system or a designated administrator, requesting data completion or correction. This ensures that only qualified data is allowed to proceed to the next stage. The verified original medical record cover sheet data package is then seamlessly transmitted to the data parsing and standardization module, which receives data that may be structured database data. The data may be in the form of unstructured text notes (such as a summary of a patient's condition by the attending physician). The parsing module uses an integrated natural language processing engine to perform deep analysis of the text information. For example, from the text "The patient complains of chest pain, suspected acute myocardial infarction," it accurately identifies and extracts the key entity "acute myocardial infarction" and maps it to a standardized ICD-10 code. At the same time, it converts data from different source systems and potentially different formats (such as dates that may be YYYY-MM-DD or DD / MM / YYYY) into a standardized format defined within the system. The essence of this process is to transform the chaotic raw information into a highly structured and coded data object that the subsequent logic of the system can directly process. This purified and structured medical record front page data object is then sent to the evaluation trigger engine module. The core of this module is the configurable rule base. The engine matches the parsed data (such as primary diagnosis = "I21.9", age = 65, systolic blood pressure on admission = 90 mmHg) with each rule in the rule base. The rules may be very specific, such as: "IF (Primary diagnosis falls under the category of cardiovascular emergency) AND (Age > 60) AND (Hypotension history) THEN Trigger a multidisciplinary assessment for high-risk cardiovascular events, with priority set to "High". The matching process is real-time and efficient. Once one or more rules are met, the engine immediately generates a strong assessment trigger signal. This signal not only includes a Boolean "yes / no" trigger indication but also encapsulates rich contextual information, such as the triggered rule ID, the urgency of the assessment, and the suggested assessment time window. After the assessment trigger signal is generated,The trigger signal received by the multidisciplinary assessment scheduling module is first passed to its subject selection submodule. This submodule queries the built-in "disease-subject" mapping matrix (for example, for acute myocardial infarction, the mapping matrix will indicate that a multidisciplinary expert, such as a cardiologist, a cardiac surgeon (for revascularization assessment), an intensive care unit (ICU) doctor (if the condition is critical), a clinical pharmacist (to review medication), a rehabilitation therapist, and a nutritionist, needs to participate). The mapping is not static; it is fine-tuned by referring to the priority and specific parameters in the trigger signal (such as automatically including ICU doctors when the priority is high). After determining the subject list, the scheduling submodule first seamlessly integrates with the hospital's HR system or expert scheduling calendar to obtain the real-time available time slots for each expert in the list. Then, using optimization algorithms (such as constraint satisfaction algorithms), with the primary goal of "completing the assessment as quickly as possible," while considering constraints such as expert geographical location and the availability of special equipment required for the assessment (such as meeting rooms and video conferencing systems), it calculates one or more optimal assessment meeting time proposals. The resource allocation submodule simultaneously reserves the necessary physical or virtual resources. The entire process is highly automated. Once a feasible solution is found, the scheduling module immediately sends assessment invitations to all relevant experts through an integrated event notification system (such as email, SMS, and hospital internal communication software API), including basic patient information, triggering reasons, suggested time, and meeting link, and requests confirmation. Experts' confirmation feedback is collected in real time. If conflicts occur, the system automatically reschedules. At the preset assessment time, experts log in to the collaborative platform provided by this module through secure authentication. The platform interface is dynamically rendered by the assessment form generation submodule, displaying standardized data pre-filled based on patient medical record homepage data and assessment type. In the assessment form, each expert sees the section relevant to their own discipline. For example, cardiologists focus on ECG and cardiac enzyme changes, while pharmacists focus on the drug list and potential interactions. A real-time data sharing submodule ensures all experts are on the same page during the assessment. When one expert enters a new observation or accesses a recent echocardiogram report, other online experts' interfaces update almost in real time. This low-latency collaboration is achieved through WebSocket long-connection technology, avoiding information silos. Experts can engage in text discussions, voice communication, and even video consultations on this platform. All input data, comments, and conclusions are saved in real time. After the assessment, the results integration submodule begins collecting the assessment sub-reports submitted by each discipline. These sub-reports may have differing or even conflicting recommendations (e.g., a surgeon recommends surgery, while an internist prefers conservative treatment). The integration submodule uses predefined rules (such as weights based on the level of evidence) or initiates a simple conflict detection algorithm to mark inconsistencies and may automatically generate a "points for discussion" list to prompt the expert panel leader for a final decision.Finally, the uncontested or agreed-upon opinions are integrated into a well-structured final comprehensive assessment report that includes a comprehensive diagnosis, treatment plan, and follow-up recommendations. This final report is submitted to the output and reporting module. Upon receiving the comprehensive assessment report, the module first formats and renders the content according to a preset template or the recipient's needs (e.g., the attending physician prefers a concise summary, while the medical record requires a complete record). The data visualization module transforms key indicators (such as the expected recovery timeline and risk score trends) into intuitive charts. The report is then output in parallel through multiple channels: first, it is automatically archived into the patient's electronic medical record, becoming part of their official medical record; second, it is pushed to the attending physician's workbench for quick access and execution; and third, it may be sent to the hospital's quality management department or research database via a secure API interface for subsequent analysis. All output processes adhere to data security protocols to ensure patient privacy throughout the entire process. The workflow, system control, and monitoring modules provide comprehensive support and assurance. Starting with data input, the user authentication and authorization submodule verifies identity and checks permissions for every access request, ensuring only authorized personnel can access the relevant data. During data processing, the performance analysis submodule continuously collects metrics from each stage, including data reception volume, rule triggering frequency, evaluation completion time, and user login records. This data is used for real-time monitoring of system health (e.g., detecting excessive load due to frequent rule triggering) and for long-term performance analysis and optimization. The audit trail submodule meticulously records "who performed what operation on what data, when, and what." The entire system's workflow thus forms a highly automated, closed-loop collaborative workflow from data input to intelligent output, with each step interconnected. Its core driving force is always the constantly evolving medical record homepage data, achieving a leap from passive recording to proactive, forward-looking, multidisciplinary collaborative medical management.

[0027] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A subject-based comprehensive assessment management system driven by a medical record homepage, characterized in that, Including: The medical record front page data input interface module is configured to receive and extract medical record front page data from the hospital information system or electronic health record system; The data parsing and standardization module is coupled to the medical record front page data input interface module and is configured to parse, clean and standardize the received medical record front page data to eliminate data inconsistencies and errors. An assessment trigger engine module, coupled to the data parsing and standardization module, is configured to automatically determine whether to initiate a multidisciplinary comprehensive assessment based on the parsed medical record front page data. The assessment trigger engine module includes a configurable rule base that stores multiple assessment trigger rules. Each rule defines one or more trigger conditions, which are based on specific thresholds or events in the medical record front page data. A multidisciplinary assessment scheduling module, coupled to the assessment triggering engine module, is configured to automatically schedule and coordinate multiple subject experts to participate in a comprehensive assessment in response to the assessment triggering signal. The multidisciplinary assessment scheduling module includes a subject selection submodule, a time scheduling submodule, and a resource allocation submodule. An assessment execution and integration module, coupled to the multidisciplinary assessment scheduling module, is configured to provide a collaborative work platform for multidisciplinary experts to perform assessments and integrate assessment results. The assessment execution and integration module includes an assessment form generation submodule, a real-time data sharing submodule, and a result integration submodule. An output and reporting module, coupled to the assessment execution and integration module, is configured to output a comprehensive assessment report to relevant medical personnel or systems, wherein the output and reporting module supports multiple output formats; The system control and monitoring module is configured to monitor the overall system operation status, including data flow, processing logs, and error handling.

2. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The medical record homepage data input interface module further includes a data verification submodule, which is configured to perform real-time verification on the input medical record homepage data to ensure the integrity and accuracy of the data. The data verification submodule uses a rule engine to check the data format, value range, and logical consistency.

3. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The rule base of the assessment trigger engine module supports dynamic updates and customization, allowing administrators to add, modify, or delete trigger rules through a graphical user interface. Each trigger rule includes a condition part and an action part. The condition part uses logical expressions to define combinations of conditions based on the medical record homepage data.

4. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The subject selection submodule of the multidisciplinary assessment and scheduling module uses a subject mapping table, which defines the correspondence between different disease types or diagnostic codes and recommended subjects.

5. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The evaluation form generation submodule of the evaluation execution and integration module dynamically creates evaluation forms based on a template library. The template library stores evaluation templates for various disciplines. Each template includes standardized evaluation items, scoring criteria, and input fields. The evaluation form generation submodule allows experts to add custom evaluation items in real time during the evaluation process. The real-time data sharing submodule uses WebSocket technology to enable real-time collaboration among multiple users, supports simultaneous editing and version control, and ensures data synchronization. The result integration submodule uses a rule engine or a simple weighted algorithm to aggregate evaluation results and includes a conflict resolution mechanism. When evaluation results from different disciplines are inconsistent, conflicts are automatically marked and a coordination meeting is recommended.

6. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The output and reporting module further includes a data visualization submodule configured to present the comprehensive evaluation report in a graphical format.

7. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The system control and monitoring module includes a performance analysis submodule, which is configured to collect system operation metrics, such as the number of triggers, processing time, and user activity logs, and use statistical analysis tools to generate performance reports to help administrators optimize system configuration. The user authentication and authorization submodule implements role-based access control, defines the permission levels of different user roles, ensures data security and compliance, and integrates audit trail functionality to record all data access and modification operations.

8. The subject-based comprehensive assessment management system driven by the medical record homepage according to claim 1, characterized in that, The system also includes a mobile extension module configured to provide remote access to evaluation functions via a mobile application. The mobile extension module includes an offline data synchronization function, allowing experts to perform some evaluation operations in environments without a network and automatically synchronizing data when the network is restored. The mobile extension module also supports voice input and handwriting recognition for convenient and rapid data entry, and integrates geolocation services to track expert locations for optimized scheduling.

9. A subject-based comprehensive assessment management system driven by a medical record homepage according to claim 1, characterized in that, The system further integrates a predictive analysis module, which is configured to use a machine learning model to predict the patient's condition development trend or assessment needs based on historical medical records and assessment data. The predictive analysis module uses regression or classification algorithms to output a predictive score and feeds the prediction results back to the assessment triggering engine module to trigger preventive assessments in advance, thereby achieving proactive medical management.

10. A subject-based comprehensive assessment management system driven by a medical record homepage according to claim 1, characterized in that, The system also includes an interoperability interface module configured to exchange data with external medical systems. This interoperability interface module supports standard medical data protocols, enabling seamless data integration. Furthermore, the module includes a data mapping engine that automatically converts external data into the system's internal format, ensuring that comprehensive and multi-source patient information can be obtained during the assessment process, thereby improving the accuracy and completeness of the assessment.

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