预测提供者工作量使用预测的患者风险评分和健康的社会决定因素在初级保健设置
Yiqun Jiang1, Yu-Li Huang1, Alexandra Watral1
1Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States.
Applied clinical informatics
|July 3, 2024
概括
使用健康的社会决定因素 (SDOH) 预测患者的健康 (ACG分数) 可以帮助估计初级保健提供者的工作量. 这种方法改善了预约和非访问护理的工作负载预测,有助于预防倦怠.
科学领域:
- 初级医疗保健医学 一级医疗保健医学
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的机器学习
背景情况:
- 提供者倦怠是初级保健中的一个重大问题,工作量加剧了这种情况.
- 工作负载包括预定访问和非访问护理,受患者健康敏度 (ACG分数) 的影响.
- 新患者缺乏足够的健康数据来准确确定ACG得分.
研究的目的:
- 使用健康的社会决定因素 (SDOH),年龄和性别来预测新患者调整后临床组 (ACG) 的得分.
- 根据预测的ACG分数,估计预约和非访问护理互动的数量.
主要方法:
- 使用了两年的预约数据,用于初始请求的患者.
- 采用机器学习算法来预测ACG分数和线性回归来估计参观和非参观护理互动.
- 将人口统计数据,SDOH和预测的ACG分数集成到预测模型中.
主要成果:
- 机器学习模型显著提高了ACG得分预测准确度 (比基线优于9%以上).
- 将SDOH和预测的ACG分数纳入预测预约 (95.2%的R2增加) 和非访问护理 (93.8%的R2增加) 的预测大大提高.
- 确定了影响预约 (年龄,吸烟,家族史,性别) 和非访问护理 (烟草,运动,教育,团体活动) 的关键因素.
结论:
- 健康的社会决定因素 (SDOH) 和预测的ACG分数对于准确预测初级保健提供者的工作量至关重要.
- 这种预测方法可以为管理工作量和减轻供应商倦怠的策略提供信息.
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