The number needed to benefit: estimating the value of predictive analytics in healthcare
The number needed to benefit: estimating the value of predictive analytics in healthcare
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DOI：
10.1093/jamia/ocz088
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发表时间：
2019-12-01
影响因子：
6.4
通讯作者：
Shah, Nigam
中科院分区：
文献类型：
--
作者：
Liu, Vincent X.;Bates, David W.;Shah, Nigam
最近，医疗保健中的预测分析产生了越来越大的热情，这反映在文献中报道的预测模型和使用电子健康记录数据的实时嵌入式模型中。然而，估计将任何单一模型应用于特定临床问题的益处仍然具有挑战性。因此，开发一个用于估计模型价值的共享框架对于促进未来有效、安全和可持续地使用预测工具至关重要。我们强调预测-行动二元体中的关键概念，这些概念预计将共同影响模型效益。这些因素包括与模型预测相关的因素（包括需要筛选的数量）以及与后续行动相关的因素（需要治疗的数量）。简而言之，受益所需的数字将筛选和治疗所需的数字联系起来，提供了一个评估临床预测模型价值的机会。
Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that together are expected to impact model benefit. These include factors relevant to model prediction (including the number needed to screen) as well as those relevant to the subsequent action (number needed to treat). In the simplest terms, a number needed to benefit contextualizes the numbers needed to screen and treat, offering an opportunity to estimate the value of a clinical predictive model in action.