Department of Biomedical and Health Information Sciences, University of Illinois Chicago, Chicago, Illinois, USA.
Duke University School of Nursing, Durham, North Carolina, USA.
J Am Med Inform Assoc. 2023 Aug 18;30(9):1561-1566. doi: 10.1093/jamia/ocad115.
Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges-incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology-that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.
嵌入式实用临床试验(ePCT)在解决当前的人口健康问题方面发挥着至关重要的作用,它们对电子健康记录(EHR)系统的使用有望提高效率,从而加快相关和可推广研究的速度和数量。然而,随着使用 EHR 衍生数据的 ePCT 的数量不断增加,由于不同人群的数据采集和获得医疗服务的差异,研究变得更容易受到偏差的影响的风险也在增加,从而导致健康和医疗体系中的不平等现象加剧。我们确定了 3 个挑战——对健康的社会决定因素的数据的不完整和可变捕获、无法代表未接受或未接受治疗的弱势群体,以及由于技术的可变使用而导致的数据丢失——当使用 EHR 数据时,这些挑战加剧了偏差,并提供了一些建议和减轻偏差的方法示例。