使用电子健康记录数据开发和验证高强度心理健康服务使用的预测模型
Bharadwaj V Chada1,2, Robert Stewart1,2, James Lai3
1Institute of Psychiatry, Psychology & Neuroscience, King's College Londonhttps://ror.org/0220mzb33, London, UK.
BJPsych bulletin
|January 20, 2026
概括
电子健康记录数据可以预测患有高强度精神卫生保健使用风险的患者. 这使得早期干预和在精神卫生服务中有效分配资源成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 心理健康护理分析
- 在医疗保健中的预测建模.
背景情况:
- 在精神卫生保健中使用高强度服务会带来巨大的成本.
- 早期识别有风险的患者可以实现主动的护理规划.
- 电子健康记录 (EHR) 为预测分析提供了丰富的数据来源.
研究的目的:
- 使用EHR数据开发和验证一个预测模型.
- 在转诊后3个月内识别患有高强度精神卫生保健使用风险的患者.
- 支持个性化护理规划,减少昂贵的护理事件.
主要方法:
- 利用了从18869名患者获得的电子病历数据,这些患者在2007年至2011年期间被转诊.
- 开发了一个合并人口,临床和服务使用变量后勤回归模型.
- 在随后的患者队列 (2012-2017年和2018-2023年) 上验证了模型.
主要成果:
- 预测模型在开发过程中达到0.79的AUROC,在验证过程中达到0.81-0.83.
- 关键预测因素包括早期使用服务,特定诊断 (精神分裂症,饮食障碍) 和生活情况.
- 自然语言处理功能没有提高模型性能.
结论:
- 常规的EHR数据有效地预测了高成本的精神卫生保健利用的风险.
- 该模型促进了有针对性的干预,并优化了资源分配.
- 预测分析可以提高心理健康服务的效率和患者结果.
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