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从电子病历中自动收集视网膜疾病患者前性实验数据 (VOYAGER)

Clare Bailey¹, Ian Pearce², Christiana Dinah^3,4

¹University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.

Clinical trials (London, England)

|August 31, 2025

概括

电子医疗记录到电子数据捕获 (EMR到EDC) 通过自动化数据传输,减少输入负担和提高准确性来简化视网膜疾病研究. 这种创新工具提高了临床研究的效率和数据质量.

科学领域:

眼科眼科
临床研究信息学

背景情况:

VOYAGER研究收集了视网膜疾病的真实数据,面临着手动输入电子病历 (EMR) 和电子病例报告表 (eCRF) 的挑战.
手动转录数据导致临床研究中的大量时间负担,资源紧张和转录错误的可能性.

研究的目的:

评估电子医疗记录到电子数据捕获 (EMR-to-EDC) 解决方案在减少数据输入负担方面的有效性.
评估EMR-to-EDC对VOYAGER视网膜疾病研究数据准确性的影响.
测量最终用户对EMR到EDC系统的满意度.

主要方法:

实施了EMR-to-EDC解决方案,自动将数据从mediSIGHT EMR传输到VOYAGER eCRF.
分析了来自英国两个站点的eCRF数据,将手动数据输入与6个月的EMR-to-EDC预定量进行比较.
通过调查评估了预填充率,手动覆盖,查询生成,并收集了员工反.

主要成果:

从EMR到EDC预填充了27.9%的基线和20.5%的后续eCRF字段,员工的覆盖率很低 (分别为8.1%和1.6%).
预先填充的访问显示出在基线时数据查询较少的趋势 (17. 1 vs 22. 0),并在随访时显著减少 (4. 1 vs 7. 1, p<0. 05).
现场工作人员报告数据输入负担减少 (在基线节省11-20分钟/ 患者,在随访时节省0-10分钟/ 患者) 和数据质量改善.

结论:

通过EMR-to-EDC有效地自动化数据从EMR传输到研究数据库,保持数据质量并减少视网膜疾病研究中的手动输入负担.
该系统有潜力通过提高效率和数据完整性来简化眼科和其他医学领域的临床研究.

关键词:

电子案例报告表数据输入负担电子数据捕获电子健康记录电子健康记录到电子数据的捕获电子医疗记录电子医疗记录到电子数据的捕获眼科眼科在现实世界中进行研究视网膜疾病

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