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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v9i7.15690</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the Application of Digital Intelligence-Based Total Quality Management System in Healthcare Quality Management</title><url>https://artdesignp.com/journal/PBES/9/7/10.26689/pbes.v9i7.15690</url><author>YangJinhong,LiuZhuo,DuWeilin,DaiNarengaowa,QuDongxue,LvLiying</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>9</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-07-28</published-time></date></history><abstract>Objective: To explore the application effect of a digital intelligence-based total quality management (TQM) system in the continuous improvement of quality in tertiary public hospitals. Methods: A public tertiary Grade A general hospital in a certain region was selected as the research site, with January to December 2024 as the control period and January to December 2025 as the observation period. A three-tier architecture of “data collection—quality control early warning—data analysis” was adopted to establish a digital intelligence-based TQM system, covering 42 clinical departments across the hospital. It involved the collection of core data such as performance monitoring indicators for tertiary hospitals, accreditation data for graded hospitals, implementation of core medical systems, and quality control indicators for key specialties. Changes in key quality indicators before and after the system’s implementation were compared, and healthcare personnel satisfaction was evaluated through questionnaires. Results: After one year of system operation, the information extraction rate for performance monitoring indicators in tertiary hospitals increased from 30.35% to 91.07% (P &amp;lt; 0.05), the information extraction rate for accreditation data in graded hospitals rose from 56.1% to 98.6% (P &amp;lt; 0.05), the implementation rate of core medical systems increased from 89.1% to 98.6% (P &amp;lt; 0.05), and the information collection rate for quality control indicators in key specialties improved from 10.3% to 61.2% (P &amp;lt; 0.05). The satisfaction score of healthcare personnel regarding quality management increased from 78.3 ± 8.5 to 86.9 ± 6.2 (P &amp;lt; 0.05). Conclusion: The digital intelligence-based TQM system can effectively promote the transformation of healthcare quality management from post-event traceability to pre-event prevention and in-process control, enhance management efficiency, achieve information extraction of indicators, and facilitate continuous improvement in healthcare quality. It holds significant practical importance for promoting high-quality development in hospitals.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Jin W, Huang X, Yu Y, et al., 2025, Construction and Application of Information Systems for the Comprehensive Quality Management System in Medical Groups. 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