ArticlePloS one2024
The impact of county-level factors on meaningful use of electronic health records (EHRs) among primary care providers.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 2 citations in OpenAlex.
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
This study examines the impact of county-level factors on "meaningful use" (MU) of electronic health records (EHRs) for 8415 primary care providers (PCPs) that enrolled in the Florida Medicaid EHR Incentive Program through adopting, improving, or upgrading (AIU) a certified EHR technology. PCPs received incentive payments at enrollment and if they used their EHRs in meaningful ways; ways that benefit patients and providers alike they received additional payments. We conducted a retrospective cohort study of these providers over the 2011-2018 period while linking their records to other state data. We used the core constructs of the resource dependence theory (RDT), a well-established organization theory in business management, to operationalize the county-level variables. These variables were rurality, poverty, educational attainment, managed care penetration, changes in population, and number of PCPs per capita. The unit of analysis was provider-years. For practical and computational purposes, all the county variables were dichotomized. We used analysis of variance (ANOVA) to test for differences in MU attestation rates across each county variable. Odds ratios and corresponding 95% confidence intervals were derived from pooled logistic regressions using generalized estimated equations (GEE) with the binomial family and logit link functions. Clustered standard errors were used. Approximately 42% of these providers attested to MU after receiving first-year incentives. Rurality and poverty were significantly associated with MU. To some degree, managed care penetration, change in population size, and number of PCPs per capita were also associated with MU. Policy makers and healthcare managers should not ignore the contribution of county-level factors in the diffusion of EHRs among physician practices. These county-level findings provide important insights about EHR diffusion in places where traditionally underserved populations live. This county-perspective is particularly important because of the potential for health IT to enable public health monitoring and population health management that might benefit individuals beyond the patients treated by the Medicaid providers.
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