ArticleJournal of the American Medical Informatics Association : JAMIA2025
A DeepSeek-powered locally deployed closed-loop system for enhancing quality control in electronic nursing documentation: development and clinical validation.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education.BMC nursing · 2026Article
- The impact of mobile nursing information systems on patient satisfaction with nursing care: a systematic review and meta-analysis.BMC nursing · 2026Article
- Applications of DeepSeek in Medicine: Bibliometric Analysis and Scoping Review.Journal of medical Internet research · 2026Article
- An Evaluation Framework for Large Language Models in Clinical Nursing: A Scoping Review and Expert Consultation.Journal of nursing management · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo 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
methodsWe 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.
resultsAfter 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.
conclusionDeepSeek 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.