3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Addressing Bias in Electronic Health Record-Based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

      research-article
      , PhD, MPH, FAHA 1 , 2 , , MBBS, MPH 1 , , MS 1 , , PhD, MPH 1
      Current epidemiology reports
      electronic health record, bias, cardiovascular disease, risk factors, epidemiology

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          PURPOSE OF REVIEW:

          Use of the electronic health record (EHR) for CVD surveillance is increasingly common. However, these data can introduce systematic error that influences the internal and external validity of study findings. We reviewed recent literature on EHR-based studies of CVD risk to summarize the most common types of bias that arise. Subsequently, we recommend strategies informed by work from others as well as our own to reduce the impact of these biases in future research.

          RECENT FINDINGS:

          Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such as informed presence bias, perceptions about the healthcare system that influence entry, and access to health services. Further, the EHR may contain inaccurate information or be missing key data points of interest due to loss to follow-up or over-diagnosis bias. Several strategies, including implementation of unique patient identifiers, adoption of standardized rules for inclusion/exclusion criteria, statistical procedures for data harmonization and analysis, and incorporation of patient-reported data have been used to reduce the impact of these biases.

          SUMMARY:

          EHR data provide an opportunity to monitor and characterize CVD risk in populations. However, understanding the biases that arise from EHR datasets is instrumental in planning epidemiological studies and interpreting study findings. Strategies to reduce the impact of bias in the context of EHR data can increase the quality and utility of these data.

          Related collections

          Author and article information

          Journal
          101626185
          42560
          Curr Epidemiol Rep
          Curr Epidemiol Rep
          Current epidemiology reports
          2196-2995
          20 January 2019
          2 November 2017
          December 2017
          20 June 2019
          : 4
          : 4
          : 346-352
          Affiliations
          [1 ]Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH
          [2 ]Division of Cardiovascular Medicine, The Ohio State University College of Medicine, Columbus, OH
          Author notes
          Corresponding Author: Julie K. Bower, PhD, 1841 Neil Avenue, 344 Cunz Hall, Columbus, OH 43210, Phone: 614-688-2148, Fax: 614-688-3533, jbower@ 123456cph.osu.edu
          Article
          PMC6585457 PMC6585457 6585457 nihpa1007016
          10.1007/s40471-017-0130-z
          6585457
          31223556
          38e12eb3-bdbe-4d8c-8cd4-24511c8485ca
          History
          Categories
          Article

          cardiovascular disease,electronic health record,bias,risk factors,epidemiology

          Comments

          Comment on this article