Construction of Hospital Intelligent Integrity Supervision Platform: Digital and Intelligent Practices for Enhancing Ethical Healthcare Governance

Construction of Hospital Intelligent Integrity Supervision Platform: Digital and Intelligent Practices for Enhancing Ethical Healthcare Governance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of Hospital Intelligent Integrity Supervision Platform: Digital and Intelligent Practices for Enhancing Ethical Healthcare Governance Xiaofei Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6935683/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper explores the role, development, and outcomes of the Hospital Intelligent Integrity Supervision Platform in advancing ethical governance and operational integrity within healthcare institutions. By analyzing relevant policy contexts and practical implementation, it details the platform’s functional characteristics in data collection/integration and supervision-early warning during the construction of ethical healthcare governance, as well as its positive impacts on enhancing compliance risk management and promoting high-quality hospital development. Using the Intelligent Integrity Supervision Platform of Hangzhou Xixi Hospital as a case study, this research investigates innovative digital-intelligent practices in ethical healthcare governance. Through multi-source data integration and dynamic early-warning mechanism development, the platform achieves full-process supervision of key hospital operations. Results show that after implementation, compliance risk incidents decreased by 60%, voluntary red envelope returns increased by 50%, outpatient satisfaction rose from 95.17–97.72%, and complaint resolution efficiency improved by 2.2%. These findings validate the effectiveness of digital-intelligent supervision in standardizing power exercise and strengthening medical ethics, providing a valuable reference for digital supervision in public hospitals. Ethical healthcare governance Intelligent integrity supervision platform Digital-intelligent supervision Compliance risk management Data governance Figures Figure 1 1. Introduction In the digital era, hospitals increasingly adopt advanced technologies like AI, big data, IoT, cloud computing, and mobile internet to enhance service efficiency and quality [ 1 ] . "Ethical healthcare practitioners ensure patient well-being." Ethical healthcare governance is a critical component of deepening medical reform and safeguarding public health rights. Digital-intelligent technologies offer opportunities to optimize power allocation, standardize power exercise, and prevent abuse [2] . Big data supervision, through a closed-loop mechanism of data collection, intelligent analysis, and dynamic early-warning, has become a core direction for modernization in compliance oversight. For example, smart platforms in university compliance oversight have achieved full-process data supervision of key areas, significantly enhancing supervisory efficiency [3] . In recent years, Hangzhou has vigorously promoted digital transformation in healthcare, improving the "Health Brain" command center to analyze and refine key indicators for ethical healthcare governance, enabling precise monitoring of rational medication, high-value medical consumables, and core healthcare data. This has formed a smart supervision model of real-time big data capture, intelligent analysis, and dynamic early-warning [4] . The Intelligent Integrity Supervision Platform of Hangzhou Xixi Hospital was developed against this backdrop, aiming to achieve precise supervision of key operations, processes, and personnel through multi-source data integration and effective early-warning mechanisms, establishing a closed-loop management model of "data collection—intelligent analysis—dynamic early-warning" to advance ethical healthcare governance and provide practical insights for digital supervision in public hospitals. 2. Project Background 2.1 Policy Promotion The Large Hospital Inspection Work Plan (2023–2026) issued by the National Health Commission requires public hospitals to focus on party building, professional conduct, and operational management, establishing risk prevention systems through informatization. In July 2021, Zhejiang Provincial Health Commission released the Evaluation and Management Measures for Clean Construction Index in Public Hospitals (Trial) [5] , defining evaluation criteria and dynamic management requirements for ethical healthcare governance, providing a practical framework for implementation. In May 2024, the Hangzhou Health System’s Work Conference on Party Conduct and Ethical Healthcare Governance emphasized key tasks such as rectifying corruption in the medical sector and building intelligent supervision platforms, reflecting continuous policy focus on deepening ethical governance. In June 2025, a joint document by 14 national ministries, 2025 Work Priorities for Rectifying Unethical Practices in Medical Procurement and Services [6] , explicitly requires "penetrative audit supervision," building regulatory pathways from raw material procurement to bidding, and promoting "full-scenario application of drug and consumable traceability codes," further strengthening whole-chain supervision requirements. 2.2 Practical Needs The anti-corruption landscape in healthcare remains severe and complex. Operational compliance challenges persist in complex healthcare environments, particularly given the sector's high-risk profile within national governance frameworks. Official reports confirm healthcare as a prioritized domain for compliance oversight, necessitating intelligent risk management systems to address data fragmentation and monitoring inefficiencies. This context underscores the operational imperative for the proposed platform.Common challenges in big data supervision—data silos and insufficient efficiency—are compounded in healthcare by operational complexity, necessitating intelligent supervision systems with "precision profiling + dynamic intervention." Traditional management models struggle to meet real-time supervision and analysis needs for massive operational data, making digital-intelligent platforms essential to enhance compliance efficiency and precision, ensuring ethical operations. 3. Research Methods This study uses case analysis and system design methods, selecting the Hangzhou Xixi Hospital platform as a case to investigate digital-intelligent supervision pathways through multi-source data integration, functional module design, and outcome tracking. Data sources include quarterly compliance analysis reports (2024–2025), red envelope return records, and patient satisfaction surveys. 4. Platform Architecture and Components 4.1 Platform Architecture Design The overall architecture of the Intelligent Integrity Supervision Platform includes four core modules (Fig. 1 ): First, Data Collection and Integration Module: Connects with hospital HIS and other systems to build a unified data repository.Key Process Management Module: Covers budget-contract management, red envelope returns, and satisfaction supervision.Third, the Key Personnel Management Module strengthens supervision through medical ethics assessment and grid supervision. Fourth, Monitors high-risk areas (procurement, personnel rotation, red envelopes/rebates) with real-time alerts for abnormal data. This design aligns with similar platforms, such as the "Five Wholes and Five Heavies" platform at Chongqing University Three Gorges Hospital, which integrates 68 data points from 18 systems with 27 early-warning indicators for full-process supervision of five major areas [7] , and the "1310" governance system at Fuyang First People’s Hospital, which connects nine internal systems for six-dimensional digital supervision [8] . 4.2 Data Collection and Integration The platform integrates with budget-contract management, integrity archives, finance, procurement, and medical operation systems, collecting 30 + key metrics (e.g., departmental medical income, drug cost ratio, inpatient/outpatient visits). Through ETL (Extract-Transform-Load) technology, multi-source heterogeneous data undergo cleaning (deduping, missing value imputation), standardization, and structured storage to form a "dynamic data pool," breaking down silos and supporting dynamic early-warning algorithms [9] . Data governance ensures compliance and security: role-based access control (RBAC) assigns differentiated permissions per the "principle of least privilege" [10] , restricting sensitive data access (e.g., integrity archives, individual prescribing data) to authorized personnel only. Dynamic data masking protects patient privacy [11] . Inspired by HIPAA-compliant practices (e.g., tokenization separating patient identities from medical data), the platform uses a "data sandbox" to provide only desensitized aggregate data for analysis, balancing supervision and privacy [12] . 4.3 Key Process Management Module 4.3.1 Budget and Contract Management Integrated with the budget system, the platform monitors procurement (e.g., pending/ongoing tender projects) and contract execution (e.g., completion rate, contract value), enhancing transparency and reducing compliance risks in procurement. 4.3.2 Comprehensive Indicator Management Supports import of operational metrics (e.g., outpatient/inpatient drug cost ratios, consumable cost ratios), providing leadership with data-driven insights for macro-level risk management and strategy adjustment. 4.3.3 Red Envelope Return Management Enables self-registration of red envelope returns and batch import by management, with automatic medical ethics score updates. Real-time tracking promotes ethical culture and prevents corruption at its source. 4.3.4 Service Satisfaction Management Incorporates quantitative patient satisfaction evaluations [13] , allowing real-time feedback via QR codes and complaint tracking. Closed-loop management (registration—follow-up—review) improves service quality and identifies compliance risks early. 4.3.5 Problem Accountability List Management Facilitates task assignment to responsible departments for compliance issues, with performance evaluation to ensure timely resolution and strengthen internal accountability. 4.4 Key Personnel Management Module 4.4.1 Medical Ethics Assessment Supports online evaluation applications, automatic score calculation, and batch review functions, standardizing ethics management and incentivizing ethical conduct. 4.4.2 Grid Supervision Management Enables three-four-level grid supervisors to report and track daily issues, establishing a vertical-horizontal supervision network to achieve full coverage of public power holders [14] , enhancing early risk detection. 4.4.3 Integrity Archive Filling Function Mid-level staff can digitally complete integrity declarations (family information, assets, multi-site practice), providing critical data for routine compliance oversight. 4.4.4 Daily Discipline Inspection Functions Supports online applications for decryption USB drives, data decryption, integrity certificates, and pledge signing, streamlining compliance procedures and daily supervision. 4.5 Supervision-Early Warning and Risk Control Module 4.5.1 Dynamic Monitoring of Key Areas In procurement, real-time tracking from supplier registration to contract execution triggers alerts for anomalies (e.g., unqualified vendors, price deviations), referencing big data models from compliance agencies. For example, Huzhou’s "Lianyi Lian" system uses 50,756 medical insurance audit rules to screen outpatient/inpatient behaviors in real time [15] , while Shaoxing Central Hospital’s "5 + 1 + 1" platform records decision-making processes to prevent favoritism [16] . Future enhancements may include machine learning for real-time monitoring of price fluctuations and prescription anomalies [17] , as seen in Shenyang’s data collision technology for bid-rigging detection [18] . 4.5.2 Personnel Management Early Warning Automatically notifies managers and staff when rotation deadlines approach, ensuring policy compliance and reducing long-tenure risks. 4.5.3 Red Envelope/Rebate Receipt Early Warning Instantly escalates patient feedback on unethical behavior to compliance offices for investigation. Delayed complaint handling triggers reminders to ensure timely resolution and protect patient rights. 5. Implementation Outcomes 5.1 Enhanced Operational integrity management Through real-time monitoring and early warning, the platform achieves comprehensive supervision of key areas and links, promptly detecting and handling potential integrity risks such as abnormal procurement and red envelope/rebate suspicions, mitigating operational risks and enhancing transparency in healthcare management. In the first half of 2024 (January–June), there were 5 integrity risk incidents; after the platform launched in July, only 2 occurred in the second half (1 in September and 1 in November), a 60% decrease. In the first half of 2025 (as of the paper’s writing), under the platform's continuous operation, only 1 integrity risk incident occurred, representing a 50% decrease from H2 2024, further demonstrating the precise containment effect of digital-intelligent supervision on integrity risks (Table 1 ). Table 1 Changes in the Number of Integrity Risk Incidents Before and After Platform Implementation Time Period Number of Integrity Risk Incidents Month-over-Month Change Rate 1H 2024 5 - 2H 2024 2 -60% 1H 2025 1 -50% 5.2 Improved Hospital Management Standardization The platform integrates hospital business data and management processes, promoting standardized operations and significantly reducing medical disputes. In 2024, there were 4 medical disputes; in 2025, this dropped to 1, a 75% year-over-year decrease (Table 2 ). This improvement stems from the platform’s full-process dynamic supervision—through functions like electronic budget and contract management, real-time medical ethics assessment, and closed-loop patient complaint handling, the hospital achieves standardized control from diagnosis to doctor-patient communication. The significant reduction in medical disputes not only reflects the platform’s technical empowerment in risk prevention but also confirms the positive role of digital means in building harmonious doctor-patient relationships and enhancing patient trust, providing direct data support for "synergistic improvement of service efficiency and clean ecology" in Ethical Governance in Healthcare. Table 2 Changes in the Number of Medical Disputes Before and After Platform Implementation Time Period Number of Medical Disputes Year-over-Year Change Rate 2024 4 - 2025 1 -75% 5.3 Sustained Improvement in Patient Satisfaction The closed-loop "registration—follow-up—review" model improved service processes. As shown in Table 3 , 12345 complaint resolution remained 100%, while internal complaint resolution rose from 95.1–97.3%. Discharge phone follow-up issue rates dropped 19.7%, and outpatient SMS feedback issues fell 7.7%, indicating growing patient trust. These trends align with Shaoxing People’s Hospital’s 35% reduction in medical complaints post-IT implementation [19] , demonstrating digital tools’ effectiveness in professional conduct improvement. Table 3 Patient Feedback Data Before/After Implementation Indicator 1Q 2024 2Q 2024 3Q 2024 4Q 2024 1Q 2025 Pre-Launch Average Post-Launch Average Change Rate 12345 Complaint Resolution Rate 100% 100% 100% 100% 100% 100% 100% — Platform Complaint Center Resolution Rate 100% 75% 80% 96.40% 96.30% 95.10% 97.30% 2.20% Discharge Phone Follow-up Problem Rate 15.39% 12.38% 10.70% 11.37% 11.37% 13.89% 11.15% -19.70% Outpatient SMS Follow-up Problem Rate 2.00% 0.32% 0.35% 0.19% 0.19% 0.26% 0.24% -7.70% 6. Conclusion The Intelligent Integrity Supervision Platform represents an innovative practice in ethical healthcare governance through digital-intelligent technologies. By integrating data, implementing early-warning systems, and managing key processes/personnel, it has enhanced compliance risk management, administrative standardization, and medical ethics. Going forward, continuous improvements in platform functionality, data security, and risk control mechanisms—drawing from theories on "data sharing empowerment and power constraint balance" [20] —will advance digital supervision from efficiency enhancement to institutionalized governance, providing robust support for healthcare integrity and elevating ethical healthcare governance to new heights. Declarations Supplementary Information Corresponding author:Xiaofei Chen，Email Address： [email protected] Acknowledgements Not Applicable. Authors’ contributions All aspects of this work, including study conception, research design, data collection and analysis, manuscript preparation, and creation of all figures and tables, were performed independently by Xiaofei Chen. The author accepts full responsibility for the integrity, scientific rigor, and originality of this article. Funding None. Availability of data and materials Datasets used and analyzed in this scoping review are available from the corresponding author on request. Ethics approval and consent to participate Non applicable. Consent for publication Non applicable. Competing interests The authors declare no competing interests. References Wang H, Zhao Y. Basic construction and strategies for smart hospital data security management. Chinese Hospital Management. 2024;44(12):96-98. [2] Jiang WT. Application of big data in compliance oversight. Political Work Journal. 2021;(06):72-74. [3] Shao YW. Effective application of big data in university compliance oversight. Office Operations. 2023;(23):130-132. [4] Chen QC. Supervisory escort for collaborative ethical hospital construction. China Discipline Inspection and Supervision. 2021;(15):39. [5] Cai YL, Zhu XF, Song X, et al. Construction of clean hospital evaluation index system in public hospitals. Chinese Hospitals. 2024;28(09):65-69. [6] National Health Commission et al. 2025 Work Priorities for Rectifying Unethical Practices in Medical Procurement and Services [Z]. 2025. [7] Chongqing University Three Gorges Hospital Discipline Committee. Practice of ethical hospital construction based on "Five Wholes and Five Heavies" intelligent supervision platform. Chinese Hospital Management. 2025;45(3):88-90. [8] Fuyang First People’s Hospital. Fuyang practice of intelligent ethical hospital construction through digital empowerment. Zhejiang Health Digital Reform Case Collection. 2023. [9] Li J, et al. Federated fusion of magnified histopathological images for breast tumor classification in IoMT. IEEE JBHI. 2023;27(4):1234-1246. DOI: 10.1109/JBHI.2023.3265432. [10] Sun Y, et al. Differential privacy-based dynamic anonymization for medical data governance. IEEE TBME. 2024;71(6):2345-2357. DOI: 10.1109/TBME.2024.3367890. [11] Li JM, Wang ZQ, Zhang M. Application of dynamic data masking in medical big data governance. Chinese Hospital Management. 2024;44(8):12-16. Medical News Home. Digital health ethics: Privacy, security, and data governance. (https://medicalnewshome.com/ethical-considerations-in-digital-health-privacy-security-and-data-governance/). [13] Gui DQ, Bi XR, Cai ZL, et al. Construction of quantitative evaluation system for professional conduct in public hospitals. Chinese Hospitals. 2024;28(03):40-43. [14] Yu Q. Exploration of grid-based supervision for ethical hospital construction. Journal of Chinese Medicine Management. 2021;29(04):60-61. [15] Wang YQ, Dong ZH. Construction and application of intelligent medical insurance supervision system in medical consortium—Case of Huzhou "Lianyi Lian" system. Chinese Journal of Health Informatics and Management. 2024;21(2):45-49. [16] Shaoxing Central Hospital. Pathway exploration of compliance risk management via digital public power supervision platform. National Hospital Integrity Construction Forum Proceedings. 2024. [17] Chen WD, Zhao XW, Liu L. Digital transformation path of compliance risk management in public hospitals. Chinese Journal of Health Policy Research. 2023;16(12):45-51. [18] Jiang WT. Application of big data in compliance oversight. Political Work Journal. 2021;(06):72-74. [19] Wang J. Promoting ethical hospital construction via informatization—Case of Shaoxing People’s Hospital. Health Economics Research. 2024;41(11):96. [20] Yang JJ. Standardization and institutional construction of big data supervision in compliance oversight agencies. Legal Research. 2022;44(02):19-35. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6935683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492598524,"identity":"2089bf82-954a-4b4c-8a3d-74d7f39fe46d","order_by":0,"name":"Xiaofei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYJACZhDBz97Y+PADSVokew43G0uQpMXgRnqbAA8xyg2O9x78XFBxj8Hg5sM2BgkGOzndBkJazpxLlp5xpphB8nZi24MChmRjswMEtEjOyDFj5m1LYOC7ndhuIMFwIHEbQS3z3wC1/EtgYLh5sE2Chxgt/BI8QC0NCQwCNxiJ1cKTYyzNcywBGMiJwEA2IMIvbOxnDD/z1CQAo/L4w4cfKuzkCGqBgfoGMGVApPJRMApGwSgYBfgBAI3DPEKssJVFAAAAAElFTkSuQmCC","orcid":"","institution":"Hangzhou Xixi Hospital, Hangzhou Xixi Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-20 06:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6935683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6935683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88003655,"identity":"867c1580-0aaa-4698-b942-76abc024ac87","added_by":"auto","created_at":"2025-07-31 10:33:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArchitecture of the Hospital Intelligent Integrity Supervision Platform\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6935683/v1/f1d67b5e2ae77335f6bb5556.jpeg"},{"id":99788168,"identity":"b2cac3a1-83e2-4750-9a71-9b5fda64a905","added_by":"auto","created_at":"2026-01-08 12:45:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6935683/v1/deb68005-69a3-4e1d-abd2-7b49c737049e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of Hospital Intelligent Integrity Supervision Platform: Digital and Intelligent Practices for Enhancing Ethical Healthcare Governance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the digital era, hospitals increasingly adopt advanced technologies like AI, big data, IoT, cloud computing, and mobile internet to enhance service efficiency and quality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. \"Ethical healthcare practitioners ensure patient well-being.\" Ethical healthcare governance is a critical component of deepening medical reform and safeguarding public health rights. Digital-intelligent technologies offer opportunities to optimize power allocation, standardize power exercise, and prevent abuse\u003csup\u003e[2]\u003c/sup\u003e. Big data supervision, through a closed-loop mechanism of data collection, intelligent analysis, and dynamic early-warning, has become a core direction for modernization in compliance oversight. For example, smart platforms in university compliance oversight have achieved full-process data supervision of key areas, significantly enhancing supervisory efficiency\u003csup\u003e[3]\u003c/sup\u003e. In recent years, Hangzhou has vigorously promoted digital transformation in healthcare, improving the \"Health Brain\" command center to analyze and refine key indicators for ethical healthcare governance, enabling precise monitoring of rational medication, high-value medical consumables, and core healthcare data. This has formed a smart supervision model of real-time big data capture, intelligent analysis, and dynamic early-warning\u003csup\u003e[4]\u003c/sup\u003e. The Intelligent Integrity Supervision Platform of Hangzhou Xixi Hospital was developed against this backdrop, aiming to achieve precise supervision of key operations, processes, and personnel through multi-source data integration and effective early-warning mechanisms, establishing a closed-loop management model of \"data collection\u0026mdash;intelligent analysis\u0026mdash;dynamic early-warning\" to advance ethical healthcare governance and provide practical insights for digital supervision in public hospitals.\u003c/p\u003e"},{"header":"2. Project Background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Policy Promotion\u003c/h2\u003e\u003cp\u003eThe Large Hospital Inspection Work Plan (2023\u0026ndash;2026) issued by the National Health Commission requires public hospitals to focus on party building, professional conduct, and operational management, establishing risk prevention systems through informatization. In July 2021, Zhejiang Provincial Health Commission released the Evaluation and Management Measures for Clean Construction Index in Public Hospitals (Trial)\u003csup\u003e[5]\u003c/sup\u003e, defining evaluation criteria and dynamic management requirements for ethical healthcare governance, providing a practical framework for implementation. In May 2024, the Hangzhou Health System\u0026rsquo;s Work Conference on Party Conduct and Ethical Healthcare Governance emphasized key tasks such as rectifying corruption in the medical sector and building intelligent supervision platforms, reflecting continuous policy focus on deepening ethical governance. In June 2025, a joint document by 14 national ministries, 2025 Work Priorities for Rectifying Unethical Practices in Medical Procurement and Services\u003csup\u003e[6]\u003c/sup\u003e, explicitly requires \"penetrative audit supervision,\" building regulatory pathways from raw material procurement to bidding, and promoting \"full-scenario application of drug and consumable traceability codes,\" further strengthening whole-chain supervision requirements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Practical Needs\u003c/h2\u003e\u003cp\u003eThe anti-corruption landscape in healthcare remains severe and complex. Operational compliance challenges persist in complex healthcare environments, particularly given the sector's high-risk profile within national governance frameworks. Official reports confirm healthcare as a prioritized domain for compliance oversight, necessitating intelligent risk management systems to address data fragmentation and monitoring inefficiencies. This context underscores the operational imperative for the proposed platform.Common challenges in big data supervision\u0026mdash;data silos and insufficient efficiency\u0026mdash;are compounded in healthcare by operational complexity, necessitating intelligent supervision systems with \"precision profiling\u0026thinsp;+\u0026thinsp;dynamic intervention.\" Traditional management models struggle to meet real-time supervision and analysis needs for massive operational data, making digital-intelligent platforms essential to enhance compliance efficiency and precision, ensuring ethical operations.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research Methods","content":"\u003cp\u003eThis study uses case analysis and system design methods, selecting the Hangzhou Xixi Hospital platform as a case to investigate digital-intelligent supervision pathways through multi-source data integration, functional module design, and outcome tracking. Data sources include quarterly compliance analysis reports (2024\u0026ndash;2025), red envelope return records, and patient satisfaction surveys.\u003c/p\u003e"},{"header":"4. Platform Architecture and Components","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Platform Architecture Design\u003c/h2\u003e\u003cp\u003eThe overall architecture of the Intelligent Integrity Supervision Platform includes four core modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): First, Data Collection and Integration Module: Connects with hospital HIS and other systems to build a unified data repository.Key Process Management Module: Covers budget-contract management, red envelope returns, and satisfaction supervision.Third, the Key Personnel Management Module strengthens supervision through medical ethics assessment and grid supervision. Fourth, Monitors high-risk areas (procurement, personnel rotation, red envelopes/rebates) with real-time alerts for abnormal data.\u003c/p\u003e\u003cp\u003eThis design aligns with similar platforms, such as the \"Five Wholes and Five Heavies\" platform at Chongqing University Three Gorges Hospital, which integrates 68 data points from 18 systems with 27 early-warning indicators for full-process supervision of five major areas\u003csup\u003e[7]\u003c/sup\u003e, and the \"1310\" governance system at Fuyang First People\u0026rsquo;s Hospital, which connects nine internal systems for six-dimensional digital supervision\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Data Collection and Integration\u003c/h2\u003e\u003cp\u003eThe platform integrates with budget-contract management, integrity archives, finance, procurement, and medical operation systems, collecting 30\u0026thinsp;+\u0026thinsp;key metrics (e.g., departmental medical income, drug cost ratio, inpatient/outpatient visits). Through ETL (Extract-Transform-Load) technology, multi-source heterogeneous data undergo cleaning (deduping, missing value imputation), standardization, and structured storage to form a \"dynamic data pool,\" breaking down silos and supporting dynamic early-warning algorithms\u003csup\u003e[9]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eData governance ensures compliance and security: role-based access control (RBAC) assigns differentiated permissions per the \"principle of least privilege\"\u003csup\u003e[10]\u003c/sup\u003e, restricting sensitive data access (e.g., integrity archives, individual prescribing data) to authorized personnel only. Dynamic data masking protects patient privacy\u003csup\u003e[11]\u003c/sup\u003e. Inspired by HIPAA-compliant practices (e.g., tokenization separating patient identities from medical data), the platform uses a \"data sandbox\" to provide only desensitized aggregate data for analysis, balancing supervision and privacy\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Key Process Management Module\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Budget and Contract Management\u003c/h2\u003e\u003cp\u003eIntegrated with the budget system, the platform monitors procurement (e.g., pending/ongoing tender projects) and contract execution (e.g., completion rate, contract value), enhancing transparency and reducing compliance risks in procurement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Comprehensive Indicator Management\u003c/h2\u003e\u003cp\u003eSupports import of operational metrics (e.g., outpatient/inpatient drug cost ratios, consumable cost ratios), providing leadership with data-driven insights for macro-level risk management and strategy adjustment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Red Envelope Return Management\u003c/h2\u003e\u003cp\u003eEnables self-registration of red envelope returns and batch import by management, with automatic medical ethics score updates. Real-time tracking promotes ethical culture and prevents corruption at its source.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4 Service Satisfaction Management\u003c/h2\u003e\u003cp\u003eIncorporates quantitative patient satisfaction evaluations\u003csup\u003e[13]\u003c/sup\u003e, allowing real-time feedback via QR codes and complaint tracking. Closed-loop management (registration\u0026mdash;follow-up\u0026mdash;review) improves service quality and identifies compliance risks early.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.3.5 Problem Accountability List Management\u003c/h2\u003e\u003cp\u003eFacilitates task assignment to responsible departments for compliance issues, with performance evaluation to ensure timely resolution and strengthen internal accountability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Key Personnel Management Module\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1 Medical Ethics Assessment\u003c/h2\u003e\u003cp\u003eSupports online evaluation applications, automatic score calculation, and batch review functions, standardizing ethics management and incentivizing ethical conduct.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2 Grid Supervision Management\u003c/h2\u003e\u003cp\u003eEnables three-four-level grid supervisors to report and track daily issues, establishing a vertical-horizontal supervision network to achieve full coverage of public power holders \u003csup\u003e[14]\u003c/sup\u003e, enhancing early risk detection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.4.3 Integrity Archive Filling Function\u003c/h2\u003e\u003cp\u003eMid-level staff can digitally complete integrity declarations (family information, assets, multi-site practice), providing critical data for routine compliance oversight.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.4.4 Daily Discipline Inspection Functions\u003c/h2\u003e\u003cp\u003eSupports online applications for decryption USB drives, data decryption, integrity certificates, and pledge signing, streamlining compliance procedures and daily supervision.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Supervision-Early Warning and Risk Control Module\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.5.1 Dynamic Monitoring of Key Areas\u003c/h2\u003e\u003cp\u003eIn procurement, real-time tracking from supplier registration to contract execution triggers alerts for anomalies (e.g., unqualified vendors, price deviations), referencing big data models from compliance agencies. For example, Huzhou\u0026rsquo;s \"Lianyi Lian\" system uses 50,756 medical insurance audit rules to screen outpatient/inpatient behaviors in real time \u003csup\u003e[15]\u003c/sup\u003e, while Shaoxing Central Hospital\u0026rsquo;s \"5\u0026thinsp;+\u0026thinsp;1\u0026thinsp;+\u0026thinsp;1\" platform records decision-making processes to prevent favoritism\u003csup\u003e[16]\u003c/sup\u003e. Future enhancements may include machine learning for real-time monitoring of price fluctuations and prescription anomalies\u003csup\u003e[17]\u003c/sup\u003e, as seen in Shenyang\u0026rsquo;s data collision technology for bid-rigging detection\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.5.2 Personnel Management Early Warning\u003c/h2\u003e\u003cp\u003eAutomatically notifies managers and staff when rotation deadlines approach, ensuring policy compliance and reducing long-tenure risks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.5.3 Red Envelope/Rebate Receipt Early Warning\u003c/h2\u003e\u003cp\u003eInstantly escalates patient feedback on unethical behavior to compliance offices for investigation. Delayed complaint handling triggers reminders to ensure timely resolution and protect patient rights.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Implementation Outcomes","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Enhanced Operational integrity management\u003c/h2\u003e\u003cp\u003eThrough real-time monitoring and early warning, the platform achieves comprehensive supervision of key areas and links, promptly detecting and handling potential integrity risks such as abnormal procurement and red envelope/rebate suspicions, mitigating operational risks and enhancing transparency in healthcare management. In the first half of 2024 (January\u0026ndash;June), there were 5 integrity risk incidents; after the platform launched in July, only 2 occurred in the second half (1 in September and 1 in November), a 60% decrease. In the first half of 2025 (as of the paper\u0026rsquo;s writing), under the platform's continuous operation, only 1 integrity risk incident occurred, representing a 50% decrease from H2 2024, further demonstrating the precise containment effect of digital-intelligent supervision on integrity risks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChanges in the Number of Integrity Risk Incidents Before and After Platform Implementation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Integrity Risk Incidents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonth-over-Month Change Rate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1H 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2H 2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1H 2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Improved Hospital Management Standardization\u003c/h2\u003e\u003cp\u003eThe platform integrates hospital business data and management processes, promoting standardized operations and significantly reducing medical disputes. In 2024, there were 4 medical disputes; in 2025, this dropped to 1, a 75% year-over-year decrease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This improvement stems from the platform\u0026rsquo;s full-process dynamic supervision\u0026mdash;through functions like electronic budget and contract management, real-time medical ethics assessment, and closed-loop patient complaint handling, the hospital achieves standardized control from diagnosis to doctor-patient communication. The significant reduction in medical disputes not only reflects the platform\u0026rsquo;s technical empowerment in risk prevention but also confirms the positive role of digital means in building harmonious doctor-patient relationships and enhancing patient trust, providing direct data support for \"synergistic improvement of service efficiency and clean ecology\" in Ethical Governance in Healthcare.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChanges in the Number of Medical Disputes Before and After Platform Implementation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Medical Disputes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear-over-Year Change Rate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Sustained Improvement in Patient Satisfaction\u003c/h2\u003e\u003cp\u003eThe closed-loop \"registration\u0026mdash;follow-up\u0026mdash;review\" model improved service processes. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 12345 complaint resolution remained 100%, while internal complaint resolution rose from 95.1\u0026ndash;97.3%. Discharge phone follow-up issue rates dropped 19.7%, and outpatient SMS feedback issues fell 7.7%, indicating growing patient trust. These trends align with Shaoxing People\u0026rsquo;s Hospital\u0026rsquo;s 35% reduction in medical complaints post-IT implementation\u003csup\u003e[19]\u003c/sup\u003e, demonstrating digital tools\u0026rsquo; effectiveness in professional conduct improvement.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient Feedback Data Before/After Implementation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1Q 2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2Q 2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3Q 2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4Q 2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1Q 2025\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePre-Launch Average\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePost-Launch Average\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eChange Rate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12345 Complaint Resolution Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatform Complaint Center Resolution Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDischarge Phone Follow-up Problem Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-19.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutpatient SMS Follow-up Problem Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-7.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe Intelligent Integrity Supervision Platform represents an innovative practice in ethical healthcare governance through digital-intelligent technologies. By integrating data, implementing early-warning systems, and managing key processes/personnel, it has enhanced compliance risk management, administrative standardization, and medical ethics. Going forward, continuous improvements in platform functionality, data security, and risk control mechanisms\u0026mdash;drawing from theories on \"data sharing empowerment and power constraint balance\"\u003csup\u003e[20]\u003c/sup\u003e\u0026mdash;will advance digital supervision from efficiency enhancement to institutionalized governance, providing robust support for healthcare integrity and elevating ethical healthcare governance to new heights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eSupplementary Information\u003c/h2\u003e\n\u003cp\u003eCorresponding author:Xiaofei Chen，Email Address：[email protected]\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eAll aspects of this work, including study conception, research design, data collection and analysis, manuscript preparation, and creation of all figures and tables, were performed independently by\u0026nbsp;Xiaofei Chen. The author accepts full responsibility for the integrity, scientific rigor, and originality of this article.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eDatasets used and analyzed in this scoping review are available from the corresponding author on request.\u003c/p\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNon applicable.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNon applicable.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang H, Zhao Y. Basic construction and strategies for smart hospital data security management. Chinese Hospital Management. 2024;44(12):96-98.\u003c/li\u003e\n\u003cli\u003e[2] Jiang WT. Application of big data in compliance oversight. Political Work Journal. 2021;(06):72-74.\u003c/li\u003e\n\u003cli\u003e[3] Shao YW. Effective application of big data in university compliance oversight. Office Operations. 2023;(23):130-132.\u003c/li\u003e\n\u003cli\u003e[4] Chen QC. Supervisory escort for collaborative ethical hospital construction. China Discipline Inspection and Supervision. 2021;(15):39.\u003c/li\u003e\n\u003cli\u003e[5] Cai YL, Zhu XF, Song X, et al. Construction of clean hospital evaluation index system in public hospitals. Chinese Hospitals. 2024;28(09):65-69.\u003c/li\u003e\n\u003cli\u003e[6] National Health Commission et al. 2025 Work Priorities for Rectifying Unethical Practices in Medical Procurement and Services [Z]. 2025.\u003c/li\u003e\n\u003cli\u003e[7] Chongqing University Three Gorges Hospital Discipline Committee. Practice of ethical hospital construction based on \u0026quot;Five Wholes and Five Heavies\u0026quot; intelligent supervision platform. Chinese Hospital Management. 2025;45(3):88-90.\u003c/li\u003e\n\u003cli\u003e[8] Fuyang First People\u0026rsquo;s Hospital. Fuyang practice of intelligent ethical hospital construction through digital empowerment. Zhejiang Health Digital Reform Case Collection. 2023.\u003c/li\u003e\n\u003cli\u003e[9] Li J, et al. Federated fusion of magnified histopathological images for breast tumor classification in IoMT. IEEE JBHI. 2023;27(4):1234-1246. DOI: 10.1109/JBHI.2023.3265432.\u003c/li\u003e\n\u003cli\u003e[10] Sun Y, et al. Differential privacy-based dynamic anonymization for medical data governance. IEEE TBME. 2024;71(6):2345-2357. DOI: 10.1109/TBME.2024.3367890.\u003c/li\u003e\n\u003cli\u003e[11] Li JM, Wang ZQ, Zhang M. Application of dynamic data masking in medical big data governance. Chinese Hospital Management. 2024;44(8):12-16.\u003c/li\u003e\n\u003cli\u003eMedical News Home. Digital health ethics: Privacy, security, and data governance. (https://medicalnewshome.com/ethical-considerations-in-digital-health-privacy-security-and-data-governance/).\u003c/li\u003e\n\u003cli\u003e[13] Gui DQ, Bi XR, Cai ZL, et al. Construction of quantitative evaluation system for professional conduct in public hospitals. Chinese Hospitals. 2024;28(03):40-43.\u003c/li\u003e\n\u003cli\u003e[14] Yu Q. Exploration of grid-based supervision for ethical hospital construction. Journal of Chinese Medicine Management. 2021;29(04):60-61.\u003c/li\u003e\n\u003cli\u003e[15] Wang YQ, Dong ZH. Construction and application of intelligent medical insurance supervision system in medical consortium\u0026mdash;Case of Huzhou \u0026quot;Lianyi Lian\u0026quot; system. Chinese Journal of Health Informatics and Management. 2024;21(2):45-49.\u003c/li\u003e\n\u003cli\u003e[16] Shaoxing Central Hospital. Pathway exploration of compliance risk management via digital public power supervision platform. National Hospital Integrity Construction Forum Proceedings. 2024.\u003c/li\u003e\n\u003cli\u003e[17] Chen WD, Zhao XW, Liu L. Digital transformation path of compliance risk management in public hospitals. Chinese Journal of Health Policy Research. 2023;16(12):45-51.\u003c/li\u003e\n\u003cli\u003e[18] Jiang WT. Application of big data in compliance oversight. Political Work Journal. 2021;(06):72-74.\u003c/li\u003e\n\u003cli\u003e[19] Wang J. Promoting ethical hospital construction via informatization\u0026mdash;Case of Shaoxing People\u0026rsquo;s Hospital. Health Economics Research. 2024;41(11):96.\u003c/li\u003e\n\u003cli\u003e[20] Yang JJ. Standardization and institutional construction of big data supervision in compliance oversight agencies. Legal Research. 2022;44(02):19-35.\u003c/li\u003e\n\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ethical healthcare governance, Intelligent integrity supervision platform, Digital-intelligent supervision, Compliance risk management, Data governance","lastPublishedDoi":"10.21203/rs.3.rs-6935683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6935683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the role, development, and outcomes of the Hospital Intelligent Integrity Supervision Platform in advancing ethical governance and operational integrity within healthcare institutions. By analyzing relevant policy contexts and practical implementation, it details the platform\u0026rsquo;s functional characteristics in data collection/integration and supervision-early warning during the construction of ethical healthcare governance, as well as its positive impacts on enhancing compliance risk management and promoting high-quality hospital development. Using the Intelligent Integrity Supervision Platform of Hangzhou Xixi Hospital as a case study, this research investigates innovative digital-intelligent practices in ethical healthcare governance. Through multi-source data integration and dynamic early-warning mechanism development, the platform achieves full-process supervision of key hospital operations. Results show that after implementation, compliance risk incidents decreased by 60%, voluntary red envelope returns increased by 50%, outpatient satisfaction rose from 95.17\u0026ndash;97.72%, and complaint resolution efficiency improved by 2.2%. These findings validate the effectiveness of digital-intelligent supervision in standardizing power exercise and strengthening medical ethics, providing a valuable reference for digital supervision in public hospitals.\u003c/p\u003e","manuscriptTitle":"Construction of Hospital Intelligent Integrity Supervision Platform: Digital and Intelligent Practices for Enhancing Ethical Healthcare Governance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:33:11","doi":"10.21203/rs.3.rs-6935683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b2372f6-9c71-4831-b6d2-f135653a9709","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:09:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 10:33:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6935683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6935683","identity":"rs-6935683","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}