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. 2013 Dec;48(6 Pt 1):2037-59.
doi: 10.1111/1475-6773.12078. Epub 2013 Jun 26.

Geographic variation in ambulatory electronic health record adoption: implications for underserved communities

Jennifer King  1 , Michael F Furukawa, Melinda B Buntin
Affiliations

Geographic variation in ambulatory electronic health record adoption: implications for underserved communities

Jennifer King et al. Health Serv Res. 2013 Dec.

Abstract

Objective: To describe small area variation in ambulatory electronic health record (EHR) adoption and assess evidence of a "digital divide" in whether adoption is lagging in traditionally underserved communities.

Data sources: Survey data on U.S. ambulatory health care sites (261,973 sites representing 716,160 providers) collected by SK&A Information Services in 2011.

Study design: We examined cross-sectional variation in two measures of local area EHR adoption: share of providers at sites using an EHR with e-prescribing functionality; and predicted probability of EHR adoption for the average site. Local areas were defined as Public Use Microdata Areas (n = 2,068). Using multivariate regression, we examined the association between adoption and three area characteristics: high concentration of minority population; high concentration of low-income population; and metropolitan status.

Principal findings: EHR adoption varied significantly across local areas, ranging from 8 to 88 percent with a median of 41 percent. Adoption was lower in large metropolitan areas; areas with high concentration of minority population in the Northeast and West; and areas with high concentration of low-income population in the Midwest.

Conclusions: Our 2011 estimates suggest there was substantial room for increased EHR adoption across the United States, including some underserved areas with relatively low EHR adoption rates. Further research should monitor policy initiatives in these areas and examine sources of heterogeneity in low- and high-adoption communities.

Keywords: Electronic health records; ambulatory care; geographic variation; underserved populations.

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Figures

Figure 1
Figure 1
Local Area Electronic Health Record (EHR) Adoption Rates Unadjusted for Site Characteristics Note. Local area EHR adoption rates are the share of ambulatory physicians, physician assistants, and nurse practitioners in the Public Use Microdata Area (PUMA) (n = 2,068) with an EHR with e-prescribing functionality. Rates were calculated as the average predicted probability of EHR adoption across all providers in the PUMA based on a multilevel linear probability model including PUMA random effects and no covariates.
Figure 2
Figure 2
Ranges in Local Area Electronic Health Record (EHR) Adoption Rates, U.S. Overall and by State Note. Bars represent ranges and dots represent medians of local area EHR adoption rates within each state. Local area EHR adoption rates are the share of ambulatory physicians, physician assistants, and nurse practitioners in the Public Use Microdata Area (PUMA) (n = 2,068) with an EHR with e-prescribing functionality. Rates were calculated as the average predicted probability of EHR adoption across all providers in the PUMA based on a multilevel linear probability model including PUMA random effects and no covariates.
Figure 3
Figure 3
Distribution of Local Area Electronic Health Record (EHR) Adoption Rates by Priority Population Characteristics Note. Boxes represent inter-quartile ranges (25th to 75th percentile), horizontal lines inside boxes represent medians, vertical lines extending from boxes represent the range of values within 1.5 times the inter-quartile range, and dots represent values outside 1.5 times the inter-quartile range. Local area EHR adoption rates are the share of ambulatory physicians, physician assistants, and nurse practitioners in the Public Use Microdata Area (PUMA) (n = 2,068) with an EHR with e-prescribing functionality. Rates were calculated as the average predicted probability of EHR adoption across all providers in the PUMA based on a multilevel linear probability model including PUMA random effects and no covariates.
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