在数字风险分层模型中使用全科医生临床判断改善事件预测:试点研究
Emma Parry1,2, Kamran Ahmed3, Elizabeth Guest3
1School of Medicine, Keele University, University Road, Keele, Staffordshire, ST5 5BG, UK. e.parry@keele.ac.uk.
BMC medical informatics and decision making
|December 19, 2024
概括
将数字风险分层与全科医生 (GP) 临床判断相结合,改善了紧急护理事件的患者风险预测. 这种混合方法增强了负预测值,确保更少的患者不必要地升级到评估.
科学领域:
- 医疗信息学 医疗信息学
- 临床风险管理临床风险管理
- 初级医疗保健医学 一级医疗保健医学
背景情况:
- 预测紧急护理和死亡风险的现有电子健康记录 (EHR) 工具往往缺乏外部验证,并可能导致无效的患者升级.
- 临床判断对于准确的风险预测至关重要,尤其是在排除严重疾病时.
研究的目的:
- 评估数字驱动的风险分层模型与全科医生 (GP) 全球临床判断 (GCJ) 结合的性能.
- 使用混合方法识别患有升级紧急护理和死亡事件风险的患者.
主要方法:
- 一项临床风险分层的队列研究在英国一座城市的6家GP实践中进行.
- 一个初步的数字分层识别了基于7个风险因素的"升级"组.
- "升级"组被全科医生进一步分为"关注"和"不关注"类别,使用GCJ.
主要成果:
- 在31,392名患者中,3968人属于"升级"组,518人 (1.7%) 被全科医生归类为"关注".
- 30天事件率 (非计划护理或死亡) 在"关注"组 (168.0/1000) 与整个人口 (19.0/1000) 相比显著更高.
- 医生评估显著改善了负预测值,从"关注"降低到"不关注"的几率比率为0.25 (p < 0.001).
结论:
- 数字风险分层模型本身表现良好.
- 整合全科医生的临床判断显著提高了风险预测的准确性.
- 添加GP GCJ显著改善了风险分层模型的负预测值.
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