Automatic generation and closed-loop management system for medical record quality brief report

Abstract

The invention discloses an automatic generation and closed-loop management system for a medical record quality brief report, which belongs to the field of medical record quality management and comprises an automatic generation subsystem and a closed-loop management subsystem. The automatic generation subsystem is used for automatically collecting data from a multi-source data platform, processing the collected data and then automatically generating a medical record quality brief report based on a preset template; and the closed-loop management subsystem is used for automatically distributing the medical record quality problem in the medical record quality brief report to a corresponding responsibility subject, tracking a problem rectification process according to rectification information fed back by the responsibility subject, auditing objection proposed in the rectification process, checking a rectification effect based on an auditing result and a rectification completion condition, and sending the medical record quality brief report to the corresponding responsibility subject. And a medical record quality management closed loop is formed. The medical record quality management and control can be ensured to form a complete closed loop, and the medical record quality management level of a hospital is continuously improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of medical record quality management, and particularly relates to a medical record quality report automatic generation and closed-loop management system. BACKGROUND

[0002] In the field of medical record quality management, the production and management process of traditional medical record quality reports highly depends on manual operation, and the specific implementation path is as follows: medical record department staff needs to manually extract key data from multiple independent platforms such as medical record management systems and audit systems, covering core contents such as patient admission, medical record home page filling information and medical record archiving records; then, the collected data is manually sorted by means of Excel and the like, and medical record quality key indicators such as error rate, rework rate and archiving rate are calculated item by item; after the data processing is completed, the report is manually produced by means of office software such as Word, including operations such as inserting statistical charts and tables and adjusting layout formats; after the report production is completed, the report is distributed to relevant departments such as clinical departments and medical departments by means of email sending or offline delivery; after the clinical department receives the report, the medical record quality problems existing in the department are manually sorted out and rectification schemes are formulated, and after the rectification work is completed, rectification reports are manually written and submitted, and finally the rectification implementation is manually checked by medical record department staff, forming a complete management process.

[0003] However, the above traditional technical solution has many significant deficiencies, and it is difficult to meet the modern medical record quality management requirements: firstly, the production efficiency is low, and it takes 3-5 days to complete a medical record quality report from data collection to final delivery, and the cycle is long, and in the face of sudden needs such as obtaining medical record quality data of a certain department at a certain time period, it is difficult to respond quickly and support real-time management decision-making of the hospital; secondly, the data accuracy is poor, and all links such as data collection, sorting and calculation rely on manual operation, which is prone to data entry errors and calculation deviations, and at the same time, different staff have subjective differences in understanding and statistical standards of quality indicators, which reduces the reliability of the generated report data; thirdly, the standardization degree is low, and the manually produced report lacks unified standards in aspects such as document format, indicator definition and time dimension division, and the reports of different periods and different departments lack comparability, which is not conducive to medical record quality analysis and comparison across departments and time periods; fourthly, the management closed loop is broken, after the report is distributed, there is no effective real-time monitoring means for the progress of problem rectification of the clinical department, the medical record department cannot timely grasp the rectification dynamics, and the rectification result feedback is not timely and not standardized, which is difficult to form a complete management closed loop of "finding problems-rectifying-checking-improving", resulting in poor medical record quality improvement effect and easy to fall into an ineffective cycle of "rectification-rebound-rectification again"; fifthly, the labor cost is high, and full-time staff are needed to be responsible for data collection, report production and rectification tracking and the like, which seriously occupies human resources and has low work efficiency, causing great waste of human resources. SUMMARY

[0004] To solve the above technical problems, the present application provides a medical record quality report automatic generation and closed-loop management system to solve the problems existing in the prior art.

[0005] To achieve the above purpose, in the first aspect, the present application provides a medical record quality report automatic generation and closed-loop management system, comprising: an automatic generation subsystem and a closed-loop management subsystem; The automatic generation subsystem is used for automatically collecting data from a multi-source data platform, processing the collected data, and automatically generating a medical record quality report based on a preset template. The closed-loop management subsystem is used for automatically distributing medical record quality problems in the medical record quality report to corresponding responsible subjects, tracking the problem rectification process according to the rectification information fed back by the responsible subjects, auditing objections raised in the rectification process, checking the rectification effect based on the audit result and the rectification completion condition, and forming a medical record quality management closed loop.

[0006] Preferably, the automatic generation subsystem comprises a data source layer for automatically collecting raw data; the raw data includes patient admission information, medical record home page data, medical record archiving records, and quality control defect data.

[0007] Preferably, the automatic generation subsystem further comprises a data processing layer for data cleaning, conversion processing and feature extraction of the raw data, and output of structured effective data; the data cleaning includes removing duplicate data, correcting error data and supplementing missing data; the conversion processing includes converting unstructured data into structured data and unifying data format and unit; the feature extraction is used for calculating medical record quality key indicators.

[0008] Preferably, the automatic generation subsystem further comprises a template definition layer for providing template design functions, supporting user-defined report structure, style and filling rules, and presetting report templates of different management dimensions; the report structure includes directory and chapter division, the style includes font, layout and chart type, the filling rule is the corresponding relationship between data and tables and charts, and the different management dimensions include hospital level, hospital area level and department level.

[0009] Preferably, the automatic generation subsystem further comprises a content filling layer for automatically injecting data of corresponding dimensions and time ranges into designated positions of the template based on the effective data and the report template through an intelligent matching algorithm, generating text content, tables and charts, and generating a preliminary report.

[0010] Preferably, the automatic generation subsystem further comprises an output optimization layer for format beautification and quality improvement of the preliminary report, supporting multiple format outputs and providing a preview function, generating a medical record quality report; the format beautification includes adjusting the layout and unifying the chart style, and the quality improvement includes checking the data consistency and logical rationality, and the multiple formats include Word format and PDF format.

[0011] Preferably, the closed-loop management subsystem comprises a problem distribution module for automatically distributing medical record quality problems in the medical record quality report to corresponding terminals according to the department, diagnosis and treatment group, and individual dimensions, and pushing the problem details through a system pop-up window and a message notification; the problem details include the problem type, associated medical records, and determination basis, and the system pop-up window is triggered when a doctor logs in the system.

[0012] Preferably, the closed-loop management subsystem further comprises a rectification implementation module for providing a rectification report uploading function for the clinical department terminal, supporting the diagnosis and treatment group quality control personnel to write a rectification plan based on the problem details and a rectification report template provided by the system and upload it; the rectification plan includes problem cause analysis, improvement measures, responsible person, and completion time limit.

[0013] Preferably, the closed-loop management subsystem further comprises a complaint and audit module for supporting the individual terminal and the department terminal to initiate a complaint on the quality control problem with objections, allowing the user to fill in the complaint reason and upload supporting materials; the supporting materials include diagnosis and treatment specification basis and special condition explanation. The complaint and audit module is also used for providing a complaint request receiving and auditing function for the supervision terminal, supporting the supervision terminal to feed back the audit result and opinion; the supervision terminal includes a medical affairs department and a medical record department; and the audit result includes pass and fail.

[0014] Preferably, the closed-loop management subsystem further comprises a rectification verification module for allowing the medical record department to check the rectification report uploaded by the clinical department through the system, and verifying the rectification effect in combination with the latest data in the medical record management system; marking the problems that meet the rectification requirements in a closed loop, and reinitiating the rectification process for the problems that do not meet the rectification requirements; the rectification effect is the change of the occurrence rate of the corresponding problem after rectification.

[0015] In the second aspect, the present application further provides a medical record quality report automatic generation and closed-loop management system for realizing the method of the first aspect.

[0016] Compared with the prior art, the present application has the following advantages and technical effects: The automatic generation subsystem of the present application replaces the traditional manual data collection, index arrangement and report making process by automatically collecting and processing multi-source data and generating reports based on preset templates, shortens the report generation cycle from 3-5 days to minutes, and can quickly respond to the demand for temporary acquisition of specific medical record quality data, efficiently supports management decision-making, and improves the efficiency of medical record quality report making.

[0017] The automatic collection and processing function of the automatic generation subsystem of the present application avoids manual input errors and calculation deviations, and generates reports based on preset templates, unifies report formats, index definitions and time dimension division, eliminates subjective differences in understanding of indexes by different personnel, makes reports of different periods and different dimensions comparable, and improves data reliability and standardization level.

[0018] The closed-loop management subsystem of the present application realizes the whole-process management and control of "finding problems, rectifying, checking and improving" by automatically distributing problems, tracking rectification process, auditing objections and checking rectification effect, so that the supervision end can master the rectification progress in real time, avoid the problem of untimely and non-standard rectification feedback, and effectively break the invalid cycle of "rectification-rebound-rectification again".

[0019] The automatic functions of the two subsystems in the present application replace the traditional manual mechanical work such as data collection, report making and rectification tracking, reduce the investment of full-time personnel, reduce labor costs, release human resources which can be diverted to higher value work such as medical record quality analysis and process optimization, and improve the utilization efficiency of human resources.

[0020] The present application can support multi-dimensional medical record quality data analysis, and provide accurate quality control basis for hospitals through the accumulation of data in the automatic generation report and closed-loop management process, assist in formulating targeted improvement strategies, and improve the overall medical record quality management level and medical service quality of hospitals. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A system diagram of the embodiment of the present application; Figure 2 A system overall architecture diagram of the embodiment of the present application; Figure 3 An automatic generation subsystem flowchart of the embodiment of the present application; Figure 4 A closed-loop management subsystem flowchart of the embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0024] Example 1 like Figure 1 As shown, this embodiment provides an automated generation and closed-loop management system for medical record quality reports, including: an automated generation subsystem and a closed-loop management subsystem; The automated generation subsystem is used to automatically collect data from a multi-source data platform, process the collected data, and automatically generate a medical record quality report based on a preset template. Furthermore, the automated generation subsystem includes a data source layer for automatically collecting raw data; the raw data includes: patient admission information, medical record cover page data, medical record archive records, and quality control defect data.

[0025] Specifically, the data source layer connects to multiple data platforms such as the medical record management system, the review system, and the Lianzhong system through interfaces, automatically collecting raw data (such as patient admission information, medical record homepage data, medical record archive records, quality control defect data, etc.), breaking down information barriers and ensuring data comprehensiveness.

[0026] Furthermore, the automated generation subsystem also includes a data processing layer, used to perform data cleaning, transformation processing, and feature extraction on the raw data, and output structured and effective data; the data cleaning includes removing duplicate data, correcting erroneous data, and completing missing data; the transformation processing includes converting unstructured data into structured data and unifying the data format and units; the feature extraction is used to calculate key indicators of medical record quality.

[0027] Specifically, the data processing layer is used to automatically process the collected raw data, including data cleaning (removing duplicate data, correcting erroneous data, and completing missing data), transformation processing (converting unstructured data such as medical record text into structured data, and unifying data format and units), and feature extraction (calculating key indicators such as CMI contribution value, CN-DRG non-enrollment rate, archiving rate, and revision rate), outputting structured, high-quality, and effective data to provide data support for the preparation of presentations.

[0028] In this embodiment, the alternative solution for the data processing layer is as follows: In the data cleaning stage, in addition to the conventional automated cleaning rules, machine learning algorithms can be introduced to automatically identify abnormal data (such as archiving time that significantly exceeds the normal range) by training the model, thereby improving the accuracy of data cleaning; at the same time, it supports manual intervention, allowing users to manually mark and correct abnormal data, thus balancing automation and flexibility.

[0029] Furthermore, the automated generation subsystem also includes a template definition layer, which provides template design functionality, supports user-defined briefing structure, style and filling rules, and presets briefing templates for different management dimensions; the briefing structure includes a table of contents and chapter division, the style includes font, layout and chart type, the filling rules are the correspondence between data and tables and charts, and the different management dimensions include hospital-wide, district-level and department-level.

[0030] Specifically, the template definition layer provides standardized template design functions, allowing users to customize the briefing structure (such as table of contents and chapter division), style (font, layout, chart type) and filling rules (correspondence between data and tables and charts). It also provides preset briefing templates for different dimensions such as the whole college level, the district level, and the department level, ensuring that the briefing format and indicators are consistent and improving the degree of standardization.

[0031] Furthermore, the automated generation subsystem also includes a content filling layer, which is used to automatically inject data of corresponding dimensions and time ranges into the specified positions of the template based on the effective data and the briefing template through an intelligent matching algorithm, thereby generating text content, tables and charts, and generating a preliminary briefing.

[0032] Specifically, the content filling layer, based on the valid data output from the data processing layer and the standardized templates from the template definition layer, achieves automated content filling. Through intelligent matching algorithms, data of corresponding dimensions and time ranges are automatically injected into designated positions in the template to generate text content (such as "In the first half of 2025, 13.90% of the discharged medical records in XX hospital had content quality control issues"), tables (such as a table showing the percentage of issues filled in on the homepage of each department), and charts (such as a trend chart of content quality control issues), thus completing the generation of a preliminary summary report.

[0033] In this embodiment, the alternative to the content filling layer is as follows: in addition to the system automatically and intelligently matching data and templates, users can manually adjust the filling content, such as modifying text descriptions, replacing chart types, and adding special instructions to meet personalized needs; at the same time, a batch generation function is provided, which can generate briefings for multiple departments and multiple time periods at one time, improving batch processing efficiency.

[0034] Furthermore, the automated generation subsystem also includes an output optimization layer, which is used to beautify the format and improve the quality of the preliminary report, supports multiple output formats and provides a preview function, and generates a medical record quality report; the format beautification includes adjusting the layout and unifying the chart style, and the quality improvement includes verifying the consistency of data and the rationality of logic, and the multiple formats include Word format and PDF format.

[0035] Specifically, the output optimization layer automatically optimizes the initially generated briefing, including format beautification (adjusting layout and unifying chart styles) and quality improvement (verifying data consistency and logical rationality, such as whether the indicator calculation results are within a reasonable range). It supports output in multiple formats such as Word and PDF, and also provides a preview function, allowing users to adjust according to their needs and export with one click.

[0036] The closed-loop management subsystem is used to automatically distribute medical record quality issues in the medical record quality briefing to the corresponding responsible parties, track the rectification process based on the rectification information provided by the responsible parties, review the objections raised during the rectification process, and verify the rectification effect based on the review results and the completion status of the rectification, thus forming a closed loop for medical record quality management.

[0037] Furthermore, the closed-loop management subsystem includes a problem distribution module, which automatically distributes medical record quality problems in the medical record quality briefing to the corresponding terminals according to the dimensions of department, treatment group, and individual, and pushes problem details through system pop-ups and message notifications; the problem details include problem type, associated medical records, and judgment criteria, and the system pop-up is triggered when the doctor logs into the system.

[0038] Specifically, the issue distribution module automatically distributes medical record quality issues involved in the automatically generated briefings to the corresponding terminals according to the department, treatment group, and individual. It pushes issue details (such as issue type, associated medical records, and judgment criteria) through system pop-ups (such as pop-up reminders when doctors log in to the system daily) and message notifications to ensure that responsibility is assigned to the person.

[0039] Furthermore, the closed-loop management subsystem also includes a rectification execution module, which provides a rectification report upload function for clinical departments, supporting the quality control personnel of the diagnosis and treatment group to write and upload rectification plans based on the problem details and the rectification report template provided by the system; the rectification plan includes problem cause analysis, improvement measures, responsible persons, and completion deadlines.

[0040] Specifically, the rectification execution module provides clinical departments with a function to upload rectification reports. It allows quality control personnel in the treatment team to write rectification plans (such as problem cause analysis, improvement measures, responsible persons, and completion deadlines) based on the problem list and the rectification report template provided by the system, and then upload them. Group departments can view the problem overview of each department under their jurisdiction, coordinate and promote the rectification work, and upload the overall rectification report.

[0041] In this embodiment, the closed-loop management subsystem also includes a progress monitoring module: the supervisory end (medical affairs department, medical records department) dynamically and visually displays the core indicators of the entire process through a global monitoring dashboard, including the number of problems, the number of rectified problems, the rectification rate, the appeal rate, the reporting timeliness rate, etc., and supports drilling down from the chart to detailed data (such as the specific rectification status of a certain type of problem in a certain department); at the same time, it counts the operation logs of each link (login, appeal, report download, etc.), analyzes the system usage and rectification progress, and automatically issues warnings for problems that have not been rectified within the time limit.

[0042] Furthermore, the closed-loop management subsystem also includes an appeal and review module, which supports individuals and departments to file appeals against disputed quality control issues, allowing users to fill in the reasons for the appeal and upload supporting materials; the supporting materials include the basis of the diagnosis and treatment guidelines and explanations of special conditions; The appeal and review module is also used to provide the regulatory authorities with functions for receiving and reviewing appeal requests, and to support the regulatory authorities in providing feedback on review results and opinions; the regulatory authorities include the medical affairs department and the medical records department; the review results include pass and fail.

[0043] Specifically, in the appeals and review module, both the individual and departmental ends support appeals against disputed quality control issues. Users can fill in the reasons for the appeal and upload supporting materials (such as the basis for diagnosis and treatment guidelines, explanations of special conditions). After receiving the appeal request, the regulatory end reviews the appeal content and provides feedback on the review results (pass / fail) and opinions to ensure that the issue determination is fair and reasonable.

[0044] Furthermore, the closed-loop management subsystem also includes a rectification verification module, which allows the medical records department to view the rectification reports uploaded by clinical departments through the system and verify the rectification effect in conjunction with the latest data in the medical records management system; to mark qualified rectification issues as closed-loop, and to re-initiate the rectification process for unqualified issues; the rectification effect is to judge the change in the incidence rate of the corresponding issues after rectification.

[0045] In this embodiment, the closed-loop management alternative is as follows: In the rectification and verification stage, in addition to manual verification, it can be connected to the AI ​​pre-inspection system. The AI ​​can automatically compare the medical record quality data before and after rectification, judge the rectification effect, generate a verification report, and reduce the workload of manual work. At the same time, it supports linking the rectification results with the performance appraisal system, and incorporate the medical record quality rectification status into the department and individual performance appraisal to improve the enthusiasm for rectification.

[0046] The beneficial effects of this embodiment: The automated generation subsystem in this embodiment automates the entire process of data collection, processing, and report production, reducing report generation time from the traditional 3-5 days to minutes. It supports real-time / one-click output and can quickly respond to sudden needs (such as generating a department's monthly report on an ad-hoc basis), significantly improving the efficiency of medical record quality report production.

[0047] This embodiment achieves automated data collection and processing, avoiding errors from manual entry and calculation. The system also incorporates built-in data verification rules to ensure data consistency and logical rationality, improving data accuracy by over 95% compared to traditional manual methods. Unified indicator definitions and statistical standards ensure comparability of briefing data from different periods and dimensions, providing a reliable basis for medical record quality analysis.

[0048] The closed-loop management subsystem in this embodiment realizes a closed-loop process of "problem distribution - rectification execution - progress monitoring - appeal review - rectification verification". The supervisory end can keep track of the rectification progress in real time and automatically issue warnings for overdue problems. The rectification rate has increased from 76.88% in the traditional model to 94.63%. The rectification results are linked to performance evaluation, which further enhances the enthusiasm of clinical departments for rectification and effectively breaks the "rectification-rebound" cycle.

[0049] The automated system in this embodiment replaces traditional manual tasks such as data collection, report preparation, and progress tracking, reducing the need for dedicated personnel and lowering labor costs by more than 60%. The freed-up human resources can be invested in higher-value tasks such as medical record quality analysis and process optimization, thereby improving the efficiency of human resource utilization.

[0050] This embodiment of the system supports multi-dimensional analysis of medical record quality data, including the entire hospital, hospital areas, departments, and medical teams. Through trend tracking (such as changes in the proportion of intrinsic quality control issues) and early warning (such as an abnormal increase in the rate of problems filled in on the homepage of a certain department), it provides data support for medical record management decisions, helps hospitals achieve precise and refined medical record quality management, and improves the overall medical quality and management level of the hospital.

[0051] Example 2 This embodiment also provides a method for medical record quality control based on conventional processes, used to implement the system described in Embodiment 1, such as... Figures 2-4 As shown, the entire process begins with the patient's discharge and ends with the formation of a complete closed loop for medical record quality control. Each step is closely linked, as detailed below: A. Patient discharge trigger process; When the hospital's HIS system detects that a patient has completed treatment and been discharged, it sends the discharged patient's unique identifier to the medical record quality control system via a system interface. Upon receiving this information, the medical record quality control system automatically triggers the medical record quality control process, creates a medical record processing task for the patient, marks the task status as "awaiting homepage submission," and then proceeds to the homepage submission and medical record transfer stage.

[0052] B. Homepage Submission and Review; After the doctor completes the medical record cover sheet in the electronic medical record system, they click the submit button. The system then sends the cover sheet data to the medical record quality control system. The medical record quality control system calls the medical records department's review module, which includes the following sub-functions: Coding compliance check: Diagnostic and surgical codes are verified by connecting to a standard coding library. If a code is not in the standard library, or if the code does not match the entered diagnosis or surgical description, the code is deemed non-compliant.

[0053] Content completeness check: Based on the pre-set rules for required fields (such as hospital number, patient name, gender, age, primary diagnosis code, primary surgery code, etc., which must be filled in), check whether there are any missing fields on the first page of the medical record.

[0054] If problems are found during the review process, the system generates a detailed review failure report (including the type of problem, the specific location of the error, and the reason for the error), and sends the failure prompt to the doctor's terminal in the form of a pop-up window and an in-system message. The doctor then modifies and resubmits the electronic medical record homepage according to the prompt. If the review is successful, the status of the medical record homepage data is marked as "Review passed", and the system proceeds to the next step of medical record transfer and AI pre-inspection.

[0055] C. Medical record transfer and AI pre-screening; After the doctor completes all the writing work of the medical record in the electronic medical record system, they click the transfer button. The system then sends the entire medical record (including the medical record cover, progress notes, examination and test reports, surgical records, and all other relevant documents) to the medical record quality control system. Upon receiving the medical record data, the system initiates an AI pre-screening program. The AI ​​pre-screening module processes the medical record mainly through the following two steps: C1. Medical Record Text Analysis: This involves word segmentation and semantic parsing of textual content such as medical records, including progress notes and examination / test results, to extract key information points, such as disease symptoms, diagnostic criteria, treatment measures, surgical names, and surgical times.

[0056] C2. Key Element Verification: The extracted key information points are compared with the preset quality control rules (stored in the system's quality control rule database; for example, for surgical medical records, the quality control rules stipulate that "the surgical record must include surgical indications, a detailed description of the surgical procedure, special circumstances that occurred during the operation, and the measures taken to handle them") to determine whether the content of the medical record meets the requirements.

[0057] If the AI ​​pre-screening detects problems with the medical record, the system generates an AI pre-screening failure report (including a description of the problem, the relevant medical record section, and the quality control rules). The system sends a pre-screening failure notification to the doctor's terminal via pop-up window and in-system message. The doctor then supplements and improves the medical record in the electronic medical record system and resubmits the transfer application. If the pre-screening is successful, the status of the medical record data is marked as "AI pre-screening passed" and it enters the routine quality control process of the medical records department.

[0058] D. Pop-up reminder from the treatment group; The system automatically executes a scheduled task at 2:00 AM daily to query the medical record quality issue database for relevant data from the previous day for each treatment group (such as the number of medical records with incorrectly filled-out pages, the number of medical records with substandard content quality control, and the number of medical records that have exceeded the archiving time limit). Based on the query results, a reminder message is generated according to a fixed template, containing information such as the number of issues, the type of issue, and typical cases (randomly selected summaries of 1-2 problematic medical records). When a doctor in a treatment group logs into the system for the first time that day, the system automatically pops up a reminder window displaying this information, reminding the doctor to pay attention to the medical record quality issues in their department.

[0059] E. Data integration and briefing generation; The system retrieves data from multiple data sources via API interfaces, including the medical record front page database, the internal quality control database, the hospital management system database, and the medical affairs office notice and announcement database. The integrated data covers multiple dimensions, including group departments, patient information, and issue types, specifically including: Group Department Dimension: Names, codes, and group affiliations of each hospital campus and department.

[0060] Patient information dimensions: basic patient information (name, gender, age, hospital number, etc.), disease diagnosis information, surgical information, hospitalization cost information, etc.

[0061] Problem type dimensions: Errors in filling out the medical record cover page (such as coding errors, missing information, etc.), quality control issues (such as non-standard medical records, unreasonable treatment plans, etc.), and medical record archiving issues (such as archiving timeouts, missing archives, etc.).

[0062] After acquiring the data, the system performs calculations such as composition ratios, proportions, and year-on-year changes, and generates charts such as bar charts, line charts, and pie charts through visualization tools to intuitively display the data results. Finally, combined with a pre-set result description template, the system automatically generates the text content of a medical record quality report. After integrating the charts and text content, a complete medical record quality report is formed.

[0063] F. Briefing distribution and rectification initiation; The system generates a quality report, which is saved in PDF format and distributed to relevant units such as treatment teams, hospital management departments, and clinical departments via the hospital's internal messaging system. Upon receiving the report, each clinical department, led by its deputy director of medical affairs, organizes an internal meeting to discuss the medical record quality issues listed in the report and develop a detailed rectification plan (including an analysis of the causes of the problems, specific improvement measures, responsible persons, and completion deadlines). Once the rectification plan is finalized, the deputy director of medical affairs uploads it to the medical record management system. The system automatically assigns the rectification task to the relevant responsible persons and marks the task status as "pending rectification," officially initiating the problem rectification process.

[0064] G. Continuous improvement and supervision; The hospital administration regularly reports the rectification progress of each clinical department to the Medical Affairs Department. The Medical Affairs Department tracks and supervises the rectification progress through the supervision module of the medical record management system. The Medical Records Department conducts a comprehensive inspection every quarter. Inspectors log into the medical record management system to view the rectification reports of each department, the comparison of medical record data before and after rectification, and other information. They also conduct on-site inspections to verify the implementation of rectification measures (such as randomly checking some rectified medical records to verify whether the problems have been truly resolved). Based on the inspection results, departments with significant rectification results are commended and rewarded, while departments with poor rectification performance are publicly criticized and required to revise their rectification plans to ensure continuous improvement in medical record quality and form a complete management loop.

[0065] Example 3 This embodiment provides a medical record quality control method that includes appeals and secondary verification. Based on Embodiment 2, this embodiment adds appeals and secondary verification steps, making the medical record quality control process more rigorous. The specific steps are as follows: A. Patient discharge trigger process; Similar to Example 2, when the hospital's HIS system detects a patient's discharge event, it sends the discharged patient's identification information to the medical record quality control system. The medical record quality control system then automatically triggers the medical record quality control process, entering the homepage submission and medical record transfer stage.

[0066] B. Homepage Submission and Review; Similar to Example 2, after the doctor submits the medical record cover page, the system calls the medical records department's review module for review. If the review fails, the doctor resubmits; if the review succeeds, the process proceeds to the next step of medical record transfer and AI pre-screening.

[0067] C. Medical record transfer and AI pre-screening; Similar to Example 2, after the doctor hands over the medical records, the system initiates an AI pre-screening program to analyze and verify key elements of the records. If the AI ​​pre-screening fails, the doctor supplements and improves the records before handing them over again; if the pre-screening passes, the records enter the routine quality control process of the medical records department.

[0068] D. Pop-up reminder from the treatment group; Similar to Example 2, the system queries the medical record quality problem data related to the treatment group at regular intervals every day, generates reminder content, and sends pop-up reminders when the doctors in the treatment group log in to the system.

[0069] E. Data integration and briefing generation; The operation process of this step is the same as that in Example 2. The system integrates data from multiple data sources, performs data analysis, generates charts through the visualization module, and generates a medical record quality report by combining the result description template.

[0070] F. Briefing distribution and appeal initiation; After the system distributes quality reports to relevant units such as treatment teams, hospital campuses, and clinical departments, if a treatment team or clinical department has objections to certain quality control issues in the report, they can initiate an appeal within the system. Users fill in the reasons for the appeal (detailing why they disagree with the quality control issues) through the appeal initiation module and upload supporting materials (such as relevant treatment guidelines, authoritative medical literature, detailed descriptions of special conditions, etc., supporting PDF, Word, and other formats). Upon receiving the appeal request, the system stores the appeal information (including the appellant, appeal time, description of the appeal issue, supporting materials, etc.) and pushes it to the supervisory end (Medical Affairs Department, Medical Records Department), while simultaneously marking the appeal issue as "Pending Review."

[0071] G. Appeal review and feedback; Supervisory staff (Medical Affairs Department, Medical Records Department) log into the system's appeal review module to view the appeal request and related supporting materials. Reviewers assess the appeal content based on medical expertise, hospital quality control standards, and relevant laws and regulations. Upon completion of the review, the system provides feedback to the appellant on the review result ("Approved" or "Disapproved") and detailed comments (e.g., approved, indicating the basis for acknowledging the appeal's grounds; disapproved, indicating the reasons for the appeal's invalidity and related evidence). If the appeal is approved, the system marks the corresponding issue as "Appeal Approved, No Rectification Required"; if the appeal is disapproved, the issue status is remarked as "Pending Rectification," and the issue is reintroduced into the rectification process.

[0072] H. Rectification Implementation and Secondary Verification; Based on the approved list of issues or those requiring revision after appeals were rejected, the clinical department's deputy director of medical affairs will organize the development or adjustment of a new rectification plan, which will then be uploaded to the medical record management system. The person responsible for rectification will carry out the rectification work according to the plan and submit a rectification completion report in the system upon completion (including a description of the rectification process, links to relevant medical records after rectification, and a self-evaluation of the rectification effectiveness).

[0073] After receiving the rectification completion report, the medical records department reviews the report through the system and conducts a second verification based on the latest data in the medical records management system. The second verification combines manual spot checks with system data comparison. Manual spot checks: A certain percentage (e.g., 10%) of the rectified medical records are randomly selected and the medical records department's quality control personnel conduct a detailed inspection according to the quality control standards to determine whether the problems have been truly resolved.

[0074] System data comparison: The system automatically extracts relevant data before and after rectification (such as the error rate of the homepage and the compliance rate of the quality control content), conducts comparative analysis, and evaluates the rectification effect.

[0075] For issues that pass rectification, the system marks them as "rectified and completed" in a closed loop; for issues that fail rectification, the system restarts the rectification process, requiring the clinical department to rectify them again.

[0076] I. Continuous improvement and supervision; Similar to Example 2, the hospital will report the rectification progress to the Medical Affairs Department, which will then monitor the progress. The Medical Records Department will conduct quarterly inspections to check the implementation of rectification measures in each department and the effectiveness of medical record quality improvement, ensuring a complete closed loop for medical record quality control and continuously improving the hospital's medical record quality management level.

[0077] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automated generation and closed-loop management system for medical record quality reports, characterized in that, This includes an automated generation subsystem and a closed-loop management subsystem; The automated generation subsystem is used to automatically collect data from a multi-source data platform, process the collected data, and automatically generate a medical record quality report based on a preset template. The closed-loop management subsystem is used to automatically distribute medical record quality issues in the medical record quality briefing to the corresponding responsible parties, track the rectification process based on the rectification information provided by the responsible parties, review the objections raised during the rectification process, and verify the rectification effect based on the review results and the completion status of the rectification, thus forming a closed loop for medical record quality management.

2. The system according to claim 1, characterized in that, The automated generation subsystem includes a data source layer for automatically collecting raw data; the raw data includes: patient admission information, medical record cover page data, medical record archive records, and quality control defect data.

3. The system according to claim 2, characterized in that, The automated generation subsystem also includes a data processing layer, used to clean, transform, and extract features from the raw data, and output structured, valid data. The data cleaning includes removing duplicate data, correcting erroneous data, and completing missing data. The transformation process includes converting unstructured data into structured data and unifying the data format and units. The feature extraction is used to calculate key indicators of medical record quality.

4. The system according to claim 3, characterized in that, The automated generation subsystem also includes a template definition layer, which provides template design functions, supports users to customize the briefing structure, style and filling rules, and presets briefing templates for different management dimensions; the briefing structure includes a table of contents and chapter division, the style includes font, layout and chart type, the filling rules are the correspondence between data and tables and charts, and the different management dimensions include hospital-wide, district-level and department-level.

5. The system according to claim 4, characterized in that, The automated generation subsystem also includes a content filling layer, which is used to automatically inject data of corresponding dimensions and time ranges into the specified positions of the template based on the effective data and the briefing template through an intelligent matching algorithm, thereby generating text content, tables and charts, and generating a preliminary briefing.

6. The system according to claim 5, characterized in that, The automated generation subsystem also includes an output optimization layer, which is used to beautify the format and improve the quality of the preliminary report, supports multiple output formats and provides a preview function, and generates a medical record quality report; the format beautification includes adjusting the layout and unifying the chart style, and the quality improvement includes verifying the consistency of data and the rationality of logic, and the multiple formats include Word format and PDF format.

7. The system according to claim 1, characterized in that, The closed-loop management subsystem includes an issue distribution module, which automatically distributes medical record quality issues in the medical record quality briefing to the corresponding terminals according to the dimensions of department, treatment group, and individual, and pushes issue details through system pop-ups and message notifications. The issue details include issue type, associated medical records, and judgment criteria. The system pop-up is triggered when the doctor logs into the system.

8. The system according to claim 7, characterized in that, The closed-loop management subsystem also includes a rectification execution module, which provides a rectification report upload function for clinical departments. It supports the quality control personnel of the diagnosis and treatment group to write and upload rectification plans based on the problem details and the rectification report template provided by the system. The rectification plan includes problem cause analysis, improvement measures, responsible persons, and completion deadlines.

9. The system according to claim 8, characterized in that, The closed-loop management subsystem also includes an appeal and review module, which supports individuals and departments to file appeals against quality control issues that are disputed. It allows users to fill in the reasons for the appeal and upload supporting materials, including the basis of the diagnosis and treatment guidelines and explanations of special conditions. The appeal and review module is also used to provide the regulatory authorities with functions for receiving and reviewing appeal requests, and to support the regulatory authorities in providing feedback on review results and opinions; the regulatory authorities include the medical affairs department and the medical records department; the review results include pass and fail.

10. An automated generation and closed-loop management system for medical record quality reports, characterized in that, Used to implement the method according to any one of claims 1-9.

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