title: Computing disease incidence, prevalence and comorbidity from electronic medical records.
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.
authors: 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.
authors: Steven C
authors: SC
authors: Bagley
authors: 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.
authors: Russ B
authors: RB
authors: Altman
mesh_terms: D003142:Communication; D015897:Comorbidity; D057286:Electronic Health Records; D004813:Epidemiology; D006801:Humans; D015994:Incidence; D015995:Prevalence
keywords: Bias; Comorbidity; Electronic medical records; Incidence; Prevalence