Authors: Hubbard RA, Chubak J, Rutter CM
Title: Estimating screening test utilization using electronic health records data
Journal: 2(1):Article 14-
Abstract: Background: Electronic health records (EHRs) are increasingly used by medical providers and offer a wide-reaching source of information on utilization of preventive services. Numerous measures used for quality assessment and public reporting are estimated based on EHR data. However, sources of error and misclassification can lead to over- or underestimation of true utilization rates. EHR-derived measures of screening test use are subject to error due to misclassification of screening and diagnostic tests. The implications of this misclassification for EHR-based screening utilization estimates have not been well explored. Objectives: We calculated the bias in estimates of screening test utilization associated with several published EHR-based algorithms for identifying screening colonoscopies and propose two simple methods to correct this bias. We apply these corrections to obtain adjusted estimates of screening colonoscopy utilization using EHR data from Group Health, an integrated health care system in Washington state. Findings: The bias in screening colonoscopy utilization estimates ranged from an underestimation of 3 percentage points to an overestimation of 12 percentage points across classification methods. If the operating characteristics of the classification method are known or if a statistical model that returns predicted probabilities of screening indication is applied in the population of interest, this information can be used to obtain unbiased estimates through simple corrections to the utilization rates with little loss of precision. When applied to data on colonoscopies received at Group Health, we found that an unadjusted estimate was 4 percentage points higher than our adjusted estimate. Discussion: Error in classification of tests as screening when using EHR data to study screening utilization should be accounted for in order to eliminate bias and prevent spurious findings.
Last Updated: 02 Mar 2015