Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA, 02115, USA.
Int J Epidemiol. 2021 Jan 23;49(6):1763-1770. doi: 10.1093/ije/dyaa035.
The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.
卫生服务研究领域广泛，旨在回答有关医疗保健系统的问题。它本质上是跨学科的，流行病学家做出了至关重要的贡献。参数回归技术仍然是卫生服务研究中的标准实践，相比之下，机器学习技术的应用渗透率较低。然而，在包括医疗保健支出、结果和质量在内的几个重要领域的研究中，已经开始为这些应用部署机器学习工具。尽管如此，在卫生服务研究中，流行病学方法的重大进展仍然没有得到充分利用。本文总结了机器学习在卫生服务研究关键领域的现状，并讨论了机器学习和流行病学方法在卫生服务研究中的交叉点的重要未来方向。
