{"title":"一个由deepseek驱动的本地部署闭环系统，用于加强电子护理文档的质量控制：开发和临床验证。","authors":"Jinhong Lv, Yangyang Xu, Mengzhu Jiang, Yuanhao Lv, Jialu Sun, Jinming Lu, Lina Wang, Hongru Wang","doi":"10.1093/jamia/ocaf109","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework.</p><p><strong>Materials and methods: </strong>We implemented a three-dimensional (3D) QC framework (real-time, final, and vertical QC). A retrospective analysis of 556 electronic nursing records was conducted to evaluate pre- and postimplementation outcomes, with documentation accuracy and audit efficiency assessed via blinded nurse evaluations.</p><p><strong>Results: </strong>After implementation, omission rates decreased from 7.19% to 1.79%, the prevalence of logical inconsistencies decreased from 9.35% to 0.72%, and the prevalence of timeliness errors decreased from 8.63% to 0%. The QC time per record decreased by 3.2-fold. Nurse satisfaction was evaluated using the Clinical Nursing Information System Effectiveness Evaluation Scale (Zhao Y, Gu Y, Zhang X, et al. Developed the clinical nursing information system effectiveness evaluation scale based on the new D&M model and conducted reliability and validity evaluation. Chin J Prae Nurs. 2020;36:544-550. https://doi.org/10.3760/cma.j.issn.1672-7088.2020.07.013), yielding a total score of 102.73 ± 3.25 out of a maximum 115 points.</p><p><strong>Discussion: </strong>This study demonstrates that the Artificial Intelligence (AI)-powered closed-loop QC system significantly enhances documentation accuracy and workflow efficiency while ensuring data security. The 3D framework (real-time, final, and vertical QC) represents a paradigm shift from reactive to proactive quality governance in nursing practice. High nurse satisfaction (102.73/115) confirms clinical viability, offering a scalable model for intelligent health-care quality ecosystems. Future work should explore federated learning for multicenter deployment and regulatory frameworks for clinical AI.</p><p><strong>Conclusion: </strong>DeepSeek demonstrated robust efficacy in enhancing QC accuracy and workflow efficiency, with localized deployment ensuring data security. This system redefines nursing documentation management, heralding an era of \"intelligent negative feedback\" in health-care quality ecosystems.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A DeepSeek-powered locally deployed closed-loop system for enhancing quality control in electronic nursing documentation: development and clinical validation.\",\"authors\":\"Jinhong Lv, Yangyang Xu, Mengzhu Jiang, Yuanhao Lv, Jialu Sun, Jinming Lu, Lina Wang, Hongru Wang\",\"doi\":\"10.1093/jamia/ocaf109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework.</p><p><strong>Materials and methods: </strong>We implemented a three-dimensional (3D) QC framework (real-time, final, and vertical QC). A retrospective analysis of 556 electronic nursing records was conducted to evaluate pre- and postimplementation outcomes, with documentation accuracy and audit efficiency assessed via blinded nurse evaluations.</p><p><strong>Results: </strong>After implementation, omission rates decreased from 7.19% to 1.79%, the prevalence of logical inconsistencies decreased from 9.35% to 0.72%, and the prevalence of timeliness errors decreased from 8.63% to 0%. The QC time per record decreased by 3.2-fold. Nurse satisfaction was evaluated using the Clinical Nursing Information System Effectiveness Evaluation Scale (Zhao Y, Gu Y, Zhang X, et al. Developed the clinical nursing information system effectiveness evaluation scale based on the new D&M model and conducted reliability and validity evaluation. Chin J Prae Nurs. 2020;36:544-550. https://doi.org/10.3760/cma.j.issn.1672-7088.2020.07.013), yielding a total score of 102.73 ± 3.25 out of a maximum 115 points.</p><p><strong>Discussion: </strong>This study demonstrates that the Artificial Intelligence (AI)-powered closed-loop QC system significantly enhances documentation accuracy and workflow efficiency while ensuring data security. The 3D framework (real-time, final, and vertical QC) represents a paradigm shift from reactive to proactive quality governance in nursing practice. High nurse satisfaction (102.73/115) confirms clinical viability, offering a scalable model for intelligent health-care quality ecosystems. Future work should explore federated learning for multicenter deployment and regulatory frameworks for clinical AI.</p><p><strong>Conclusion: </strong>DeepSeek demonstrated robust efficacy in enhancing QC accuracy and workflow efficiency, with localized deployment ensuring data security. This system redefines nursing documentation management, heralding an era of \\\"intelligent negative feedback\\\" in health-care quality ecosystems.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Medical Informatics Association\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1093/jamia/ocaf109\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocaf109","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A DeepSeek-powered locally deployed closed-loop system for enhancing quality control in electronic nursing documentation: development and clinical validation.
Objectives: To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework.
Materials and methods: We implemented a three-dimensional (3D) QC framework (real-time, final, and vertical QC). A retrospective analysis of 556 electronic nursing records was conducted to evaluate pre- and postimplementation outcomes, with documentation accuracy and audit efficiency assessed via blinded nurse evaluations.
Results: After implementation, omission rates decreased from 7.19% to 1.79%, the prevalence of logical inconsistencies decreased from 9.35% to 0.72%, and the prevalence of timeliness errors decreased from 8.63% to 0%. The QC time per record decreased by 3.2-fold. Nurse satisfaction was evaluated using the Clinical Nursing Information System Effectiveness Evaluation Scale (Zhao Y, Gu Y, Zhang X, et al. Developed the clinical nursing information system effectiveness evaluation scale based on the new D&M model and conducted reliability and validity evaluation. Chin J Prae Nurs. 2020;36:544-550. https://doi.org/10.3760/cma.j.issn.1672-7088.2020.07.013), yielding a total score of 102.73 ± 3.25 out of a maximum 115 points.
Discussion: This study demonstrates that the Artificial Intelligence (AI)-powered closed-loop QC system significantly enhances documentation accuracy and workflow efficiency while ensuring data security. The 3D framework (real-time, final, and vertical QC) represents a paradigm shift from reactive to proactive quality governance in nursing practice. High nurse satisfaction (102.73/115) confirms clinical viability, offering a scalable model for intelligent health-care quality ecosystems. Future work should explore federated learning for multicenter deployment and regulatory frameworks for clinical AI.
Conclusion: DeepSeek demonstrated robust efficacy in enhancing QC accuracy and workflow efficiency, with localized deployment ensuring data security. This system redefines nursing documentation management, heralding an era of "intelligent negative feedback" in health-care quality ecosystems.
期刊介绍：
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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