Yim Wen-Wai, Wheeler Amanda J, Curtin Catherine, Wagner Todd H, Hernandez-Boussard Tina
VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304.
Department of Medicine, Biomedical Informatics Research, School of Medicine, Stanford University, 1265 Welch Road, Stanford, CA 94305.
Converg Sci Phys Oncol. 2018 Mar;4(1). doi: 10.1088/2057-1739/aaa905. Epub 2018 Feb 12.
With increasingly ubiquitous electronic medical record (EMR) implementation accelerated by the adoption of the HITECH Act, there is much interest in the secondary use of collected data to improve outcomes and promote personalized medicine. A plethora of research has emerged using EMRs to investigate clinical research questions and assess variations in both treatments and outcomes. However, whether because of genuine complexities of modeling disease physiology or because of practical problems regarding data capture, data accuracy, and data completeness, the state of current EMR research is challenging and gives rise to concerns regarding study accuracy and reproducibility. This work explores challenges in how different experimental design decisions can influence results using a specific example of breast cancer patients undergoing excision and reconstruction surgeries from EMRs in an academic hospital and the Veterans Health Administration (VHA) We discuss emerging strategies that will mitigate these limitations, including data sharing, application of natural language processing, and improved EMR user design.
随着《健康信息技术经济与临床健康法案》(HITECH Act)的推动,电子病历(EMR)的应用日益普及,人们对利用收集到的数据进行二次使用以改善治疗效果和推动个性化医疗产生了浓厚兴趣。大量研究利用电子病历调查临床研究问题,并评估治疗方法和治疗效果的差异。然而,无论是由于疾病生理学建模的真正复杂性,还是由于数据采集、数据准确性和数据完整性方面的实际问题,当前电子病历研究的状况颇具挑战性,并引发了对研究准确性和可重复性的担忧。这项工作以一家学术医院和退伍军人健康管理局(VHA)的电子病历中接受切除和重建手术的乳腺癌患者的具体例子,探讨了不同实验设计决策如何影响结果的挑战。我们讨论了将减轻这些限制的新兴策略,包括数据共享、自然语言处理的应用以及改进电子病历用户设计。