Intended for healthcare professionals

  1. Research
  2. Biases in electronic...
  3. Biases in electronic health record data due to processes within the healthcare system: retrospective observational study
CCBYNC Open access
Research

Biases in electronic health record data due to processes within the healthcare system: retrospective observational study

BMJ 2018; 361 doi: https://doi.org/10.1136/bmj.k1479 (Published 30 April 2018) Cite this as: BMJ 2018;361:k1479

This article has a correction. Please see:

  1. Denis Agniel, research fellow1,
  2. Isaac S Kohane, department head12,
  3. Griffin M Weber, associate professor13
  1. 1Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St,
  2. Boston, MA 02115, USA
  3. 2Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
  4. 3Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
  1. Correspondence to: G M Weber weber{at}hms.harvard.edu
  • Accepted 13 March 2018

Abstract

Objective To evaluate on a large scale, across 272 common types of laboratory tests, the impact of healthcare processes on the predictive value of electronic health record (EHR) data.

Design Retrospective observational study.

Setting Two large hospitals in Boston, Massachusetts, with inpatient, emergency, and ambulatory care.

Participants All 669 452 patients treated at the two hospitals over one year between 2005 and 2006.

Main outcome measures The relative predictive accuracy of each laboratory test for three year survival, using the time of the day, day of the week, and ordering frequency of the test, compared to the value of the test result.

Results The presence of a laboratory test order, regardless of any other information about the test result, has a significant association (P<0.001) with the odds of survival in 233 of 272 (86%) tests. Data about the timing of when laboratory tests were ordered were more accurate than the test results in predicting survival in 118 of 174 tests (68%).

Conclusions Healthcare processes must be addressed and accounted for in analysis of observational health data. Without careful consideration to context, EHR data are unsuitable for many research questions. However, if explicitly modeled, the same processes that make EHR data complex can be leveraged to gain insight into patients’ state of health.

Footnotes

  • Contributors: ISK and GMW designed the study. DA and GMW conducted the analysis. All authors contributed to the interpretation of the results and writing the manuscript. GMW is the guarantor.

  • Funding: This study was supported by Informatics for Integrating Biology and the Bedside, a National Institutes of Health (NIH) funded National Center for Biomedical Computing (U54LM008748). Additional funding was provided by NIH Big Data to Knowledge (BD2K) awards U01CA198934 through the National Cancer Institute (NCI) and U54HG007963 through the National Human Genome Research Institute (NHGRI). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: all authors had financial support from the National Institutes of Health for this study.

  • Ethical approval: The study was approved by the institutional review board (IRB) of the two participating hospitals (Brigham and Women’s Hospital and Massachusetts General Hospital), and a waiver of consent was obtained.

  • Data sharing: The data used in these experiments may be requested by registration and submission of a data use agreement at http://HealthcareSystemDynamics.org.

  • Transparency: The manuscript’s guarantor (GMW) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

View Full Text
Back to top