Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; The Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles, CA, USA.
Centre for Health Economics, University of York, York, England, UK.
Value Health. 2022 Jul;25(7):1063-1080. doi: 10.1016/j.jval.2022.03.022.
Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.
机器学习（ML）和人工智能的进步为患者带来了巨大的潜在益处。使用 ML 的预测分析已经在医疗保健运营和护理提供中得到广泛应用，但 ML 如何用于健康经济学和结果研究（HEOR）？为了回答这个问题，ISPOR 成立了一个新兴的良好实践工作组，用于 ML 在 HEOR 中的应用。该工作组确定了 5 个 ML 可以增强 HEOR 的方法领域：（1）队列选择，确定与纳入标准更具特异性的样本；（2）确定健康结果的独立预测因素和协变量；（3）健康结果的预测分析，包括那些高成本或危及生命的结果；（4）通过靶向最大似然估计或双重偏差估计等方法进行因果推断，有助于更快地产生可靠的证据；（5）将 ML 应用于经济模型的开发，以减少成本效益分析中的结构、参数和抽样不确定性。总体而言，ML 通过对大数据进行有意义和高效的分析来促进 HEOR。然而，由于缺乏关于 ML 方法如何为特征选择和预测分析提供解决方案的透明度，特别是在无监督情况下，增加了提供者和其他决策者在使用 ML 结果时的风险。为了检查 ML 是否为医疗保健分析提供了有用和透明的解决方案，工作组制定了 PALISADE 清单。它是一个指南，用于平衡 ML 的许多潜在应用与方法开发和发现的透明度需求。
