A DeepSeek-powered locally deployed closed-loop system for enhancing quality control in electronic nursing documentation: development and clinical validation
Open accessAbstract
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.
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Yang, M., Long, J., He, Y., Meng, R., Ling, C., Long, Q., & Wen, J. (2026) · BMC Nursing · doi:10.1186/s12912-026-05132-7
- Authors
- Lv, J., Xu, Y., Jiang, M., Lv, Y., Sun, J., Lu, J., Wang, L., & Wang, H.
- Affiliations
- Department of Nursing, First People’s Hospital of Xinxiang and The Fifth Affiliated Hospital of Henan Medical University , Xinxiang 453003,
- Henan Medical University (CN)
- Network and Information Center, The First Affiliated Hospital of Henan Medical University , Xinxiang 453003,
- Corresponding author
- Wang, Lina
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-07-16
- Volume
- 32
- Issue
- 10
- Pages
- 1526-1532
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- OpenAlex
- W4412488953
- Cited by
- 12
- References
- 14
- Citations by year
- 2026: 11 · 2025: 1
- FWCI
- 27.42
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- Henan Province Postgraduate Education Reform and Quality Improvement Project (YJS2024JD25)
- Henan Provincial Science and Technology Tackling Project (212102310671)
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- electronic nursing documentation, quality control, DeepSeek, large language models, closed-loop systems, documentation accuracy, nursing satisfaction, data security, federated learning
- MeSH terms
- Documentation, Humans, Nursing Records, Quality Control, Retrospective Studies, Electronic Health Records
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