AI Article Synopsis

  • The study investigates how county-level factors influence the "meaningful use" of electronic health records (EHRs) among over 8,400 primary care providers in Florida's Medicaid EHR Incentive Program from 2011 to 2018.
  • Key county-level factors analyzed include rurality, poverty, educational attainment, managed care penetration, population changes, and the number of PCPs per capita, with significant findings linking rurality and poverty to lower rates of MU.
  • The research emphasizes the importance of understanding these county-level influences for policymakers and healthcare managers to effectively promote EHR adoption in underserved communities.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10810449PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295435PLOS

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