一个使预测模型在实践中有用的框架。
A framework for making predictive models useful in practice.
机构信息
Stanford Center for Biomedical Informatics, School of Medicine, Stanford University, Stanford, California, USA.
Department of Computer Science, School of Engineering, Stanford University, Stanford, California, USA.
出版信息
J Am Med Inform Assoc. 2021 Jun 12;28(6):1149-1158. doi: 10.1093/jamia/ocaa318.
OBJECTIVE
To analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality.
MATERIALS AND METHODS
We built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models' predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases. We also quantified the relative benefits of increasing capacity for inpatient ACP versus outpatient ACP.
RESULTS
Work capacity constraints and discharge timing can significantly reduce the net benefit of triggering the ACP workflow based on a model's predictions. However, the reduction can be mitigated by creating an outpatient ACP workflow. Given limited resources to either add capacity for inpatient ACP versus developing outpatient ACP capability, the latter is likely to provide more benefit to patient care.
DISCUSSION
The benefit of using a predictive model for identifying patients for interventions is highly dependent on the capacity to execute the workflow triggered by the model. We provide a framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit.
CONCLUSION
An analysis of the sensitivity of the net benefit realized by a predictive model triggered clinical workflow to various healthcare delivery factors is necessary for making predictive models useful in practice.
目的
分析医疗保健提供因素对基于 12 个月死亡率预测触发高级医疗照护计划(ACP)工作流程的净收益的影响。
材料与方法
我们使用电子健康记录数据构建了 12 个月死亡率预测模型,并根据模型预测评估了医疗保健提供因素对触发 ACP 工作流程的净收益的影响。这些因素包括使 ACP 不适当的非临床原因:ACP 能力有限、因患者出院而无法跟进以及缺乏门诊工作流程来跟进错过的病例。我们还量化了增加住院 ACP 与门诊 ACP 能力的相对收益。
结果
工作能力限制和出院时间可以根据模型预测显著降低触发 ACP 工作流程的净收益。但是,通过创建门诊 ACP 工作流程可以减轻这种减少。鉴于资源有限,无论是增加住院 ACP 的能力还是开发门诊 ACP 能力,后者更有可能为患者护理带来更多收益。
讨论
使用预测模型识别干预患者的获益高度依赖于执行模型触发的工作流程的能力。我们提供了一种量化医疗保健提供因素和工作能力限制对实现收益的影响的框架。
结论
对预测模型触发的临床工作流程的净收益实现对各种医疗保健提供因素的敏感性进行分析对于使预测模型在实践中有用是必要的。