在电子患者报告结果 (ePRO) 远程症状监测系统 (AFT-39) 中优化警报通知
Gina L Mazza1,2, Amylou C Dueck3,4, Brenda Ginos3,4
1Alliance Foundation Trials Statistics and Data Center, Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ, 85259, USA. Mazza.Gina@mayo.edu.
概括
一个算法可以从电子患者报告结果 (ePRO) 系统中过非紧急警报,减少护士负担并改善远程症状监测的可行性.
科学领域:
- 在瘤学瘤学.
- 数字健康数字健康
- 临床信息学 临床信息学
背景情况:
- 电子患者报告结果 (ePRO) 系统促进了远程症状监测,提供了临床益处.
- 来自ePRO系统的实时警报可以压倒护理人员,影响工作流程,并可能推迟关键病例的护理.
研究的目的:
- 开发和评估用于识别和抑制ePRO系统生成的非紧急警报的算法.
- 通过过不需要立即关注的警报来减轻护士的负担.
主要方法:
- 该研究评估了来自PRO-TECT试验的警报,利用瘤病患者每周的ePRO症状调查.
- 开发了一种算法来预测护士对警报紧急情况的评估,将患者报告的紧急情况和症状变化纳入其中.
- 根据算法的正确识别非紧急警报的能力,评估了警报抑制.
主要成果:
- 该算法将38%的警报识别为可能非紧急的,表明可接受的歧视 (80%的灵敏度,52%的特异性).
- 患者报告的症状恶化显著增加了护士认为警报紧急的可能性 (33%对26%).
- 该研究包括593名患者,34%的调查产生了警报,59%的警报促使护士立即采取行动.
结论:
- 一个算法可以有效地识别可能的非紧急的远程症状监测警报.
- 这种算法方法可以提高护士的接受度和ePRO系统在临床环境中的实际实施.
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