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

Computing disease incidence, prevalence and comorbidity from electronic medical records.

Author information

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

PMID:
27498067
DOI:
10.1016/j.jbi.2016.08.005
[Indexed for MEDLINE]
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