Computing disease incidence, prevalence and comorbidity from electronic medical records

Steven C Bagley; Russ B Altman

doi:10.1016/j.jbi.2016.08.005

Computing disease incidence, prevalence and comorbidity from electronic medical records

J Biomed Inform. 2016 Oct;63:108-111. doi: 10.1016/j.jbi.2016.08.005. Epub 2016 Aug 4.

Authors

Steven C Bagley¹, Russ B Altman²

Affiliations

¹ Department of Genetics, Stanford University, School of Medicine, MSOB, X-211, 1265 Welch Road, MC 5479, Stanford, CA 94305-5479, USA. Electronic address: steven.bagley@stanford.edu.
² Departments of Bioengineering and Genetics, Stanford University, Shriram Room 209, MC 4245, 443 Via Ortega Drive, Stanford, CA 94305-4145, USA. Electronic address: russ.altman@stanford.edu.

Abstract

Electronic medical records (EMR) represent a convenient source of coded medical data, but disease patterns found in EMRs may be biased when compared to surveys based on sampling. In this communication we draw attention to complications that arise when using EMR data to calculate disease prevalence, incidence, age of onset, and disease comorbidity. We review known solutions to these problems and identify challenges for future work.

Keywords: Bias; Comorbidity; Electronic medical records; Incidence; Prevalence.

MeSH terms

Communication
Comorbidity
Electronic Health Records*
Epidemiology*
Humans
Incidence
Prevalence

Grant support

R01 LM005652/LM/NLM NIH HHS/United States