How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use

Published: June 9, 2014
Category: Bibliography > Papers
Authors: Ancker JS, Edwards A, Hauser D, HITEC Investigators, Kaushal R, Kern LM, Nosal S, Stein DM
Countries: United States
Language: null
Types: Care Management
Settings: Academic, Hospital

J Am Med Inform Assoc

Weill Cornell Medical College, New York, NY, USA

BACKGROUND: Studies of the effects of electronic health records (EHRs) have had mixed findings, which may be attributable to unmeasured confounders such as individual variability in use of EHR features.

OBJECTIVE: To capture physician-level variations in use of EHR features, associations with other predictors, and usage intensity over time.

METHODS: Retrospective cohort study of primary care providers eligible for meaningful use at a network of federally qualified health centers, using commercial EHR data from January 2010 through June 2013, a period during which the organization was preparing for and in the early stages of meaningful use.

RESULTS: Data were analyzed for 112 physicians and nurse practitioners, consisting of 430 803 encounters with 99 649 patients. EHR usage metrics were developed to capture how providers accessed and added to patient data (eg, problem list updates), used clinical decision support (eg, responses to alerts), communicated (eg, printing after-visit summaries), and used panel management options (eg, viewed panel reports). Provider-level variability was high: for example, the annual average proportion of encounters with problem lists updated ranged from 5% to 60% per provider. Some metrics were associated with provider, patient, or encounter characteristics. For example, problem list updates were more likely for new patients than established ones, and alert acceptance was negatively correlated with alert frequency.

CONCLUSIONS: Providers using the same EHR developed personalized patterns of use of EHR features. We conclude that physician-level usage of EHR features may be a valuable additional predictor in research on the effects of EHRs on healthcare quality and costs.

PMID: 24914013

United States,Population Markers,Practice Pattern Comparison,Total Disease Burden,Cost Burden Evaluation,Adult,Decision Making,Computer-Assisted,Electronic Health Records/statistics & numerical data,Gender,Retrospective Studies

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