Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department.
Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department.
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DOI：
10.3389/fdgth.2022.958663
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发表时间：
2022
影响因子：
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通讯作者：
Patterson, Brian W
中科院分区：
文献类型：
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作者：
Engstrom, Collin J;Adelaine, Sabrina;Liao, Frank;Jacobsohn, Gwen Costa;Patterson, Brian W
预测模型越来越多地被开发和实施，以改善各种临床情况下的患者护理。虽然存在大量关于使用现有数据开发模型的文献，但很少关注这些模型在实时生产环境中部署的实际操作。本案例研究描述了在旨在预测老年人急诊科 (ED) 就诊后门诊跌倒风险的模型的操作过程中发现和克服的挑战和障碍。根据我们的经验，我们提供了将基于 EHR 的预测模型从研究和报告环境转化为实时操作的一般原则。
Predictive models are increasingly being developed and implemented to improve patient care across a variety of clinical scenarios. While a body of literature exists on the development of models using existing data, less focus has been placed on practical operationalization of these models for deployment in real-time production environments. This case-study describes challenges and barriers identified and overcome in such an operationalization for a model aimed at predicting risk of outpatient falls after Emergency Department (ED) visits among older adults. Based on our experience, we provide general principles for translating an EHR-based predictive model from research and reporting environments into real-time operation.