基于DeepSeek的本地部署的闭环系统,用于加强电子护理文档的质量控制:开发和临床验证
Jinhong Lv1, Yangyang Xu2, Mengzhu Jiang1
1Department of Nursing, First People's Hospital of Xinxiang and The Fifth Affiliated Hospital of Henan Medical University, Xinxiang 453003, China.
概括
使用DeepSeek的新人工智能系统改善了电子护理文档质量控制,大大减少了错误并节省了时间. 这种闭环系统提高了准确性和效率,确保了数据安全性和高护士满意度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 提高护理质量 提高护理质量
背景情况:
- 电子护理文档对于患者护理质量和安全至关重要.
- 目前用于护理文档的质量控制 (QC) 方法通常是反应性和低效的.
- 人工智能 (AI) 的整合为主动和自动化QC提供了潜力.
研究的目的:
- 开发和评估一个本地部署的DeepSeek驱动的闭环系统,用于电子护理文档质量控制 (QC).
- 通过使用多维验证框架,评估这种人工智能驱动系统的临床疗效.
- 调查对文档准确性,审计效率和护士满意度的影响.
主要方法:
- 实施一个三维 (3D) QC框架:实时,最终和垂直的QC.
- 556个电子护理记录的回顾性分析系统实施前后.
- 盲目的护士评估,以评估文档准确性和审计效率.
主要成果:
- 缺席率从7.19%下降到1.79%.
- 逻辑不一致性从9.35%减少到0.72%.
- 及时性错误从8.63%降至0%,每条记录的QC时间减少了3.2倍.
- 报告的高护士满意度 (102.73/115在评估尺度上).
结论:
- 由人工智能驱动的闭环质量控制系统显著提高了护理文档的准确性和工作流程的效率.
- 三维质量控制框架将质量治理从反应性转变为主动性.
- 该系统证明了临床可行性,并为智能医疗保健质量生态系统提供了一个可扩展的模型.
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