【作 者】Xiaobo ZhouAlexander TurchinJonathan P BickelWilliam G AdamsKeith MarsoloShawn N MurphyKenneth D MandlElmer V BernstamVijay A RaghavanKathe P FoxGriffin M Weber
【作者单位】Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Department of Pediatrics, Boston Medical Center, Boston, MA, USA Department of Internal Medicine, McGovern Medical School, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA Department of Analytics and Behavior Change, Aetna, Hartford, CT, USA Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA Scientific Information Management, Merck, Boston, MA, USA Division of Endocrinology, Brigham and Women’s Hospital, Boston, MA, USA Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA Department of Neurology, Massachusetts General Hospital, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA
【出 处】Journal of the American Medical Informatics Association
【摘 要】 Objective One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from... 更多 >> Objective One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data “completeness” affect the number of patients in the resulting cohort and introduce potential biases. Materials and Methods We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 2 16 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members. Results EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete. Discussion and Conclusion As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort. << 收起
