Health Maintenance Organizations and Meaningful Use of Electronic Health Records among Primary Care Providers

• In Event: Health IT and Outcomes

Abstract

Research Objective: Technology diffusion is widely considered a key driver of long-term growth in healthcare spending.  It is also agreed that managed care plays a key role in slowing the growth in healthcare costs in the US.  The present study will examine the relationship between Health Maintenance Organizations (HMOs) and the meaningful use (MU) of electronic health records (EHRs) among primary care providers (PCPs) in the United States.Study Design:  We conducted a retrospective cohort study using the 2011–2021 records of PCPs who participated in the Medicaid EHR Incentives Program, later renamed Medicaid Promoting Interoperability (MPI) program.   The MPI program was created by the Centers for Medicare and Medicaid Services (CMS) using funds from the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act, which was designed to stimulate the diffusion of health information technology.  In addition to procuring a certified EHR, the MPI program requires participating PCPs to “meaningfully use” the functionalities of their EHRs to meet specific federal benchmarks related to improving safety and quality of care, patient and family engagement in care, safety and care coordination.  Population studied: We received data on more than 180,000 PCPs from 30 states and the District of Columbia.  The dataset includes at least two states for each of the nine U.S. Census Divisions, indicating the representativeness of the data for the entire U.S.  Statistical Methods: To more formally estimate the relationship between HMOs and MU, we use a reduced form econometric model with error terms that are assumed normally distributed and independent across counties but potentially correlated within counties. We use a generalized hierarchical linear model (GHLM) model given the outcome data are clustered at the county level and measured at the practice level.  We then estimate two models: Model 1 will be a null random intercept model while Model 2 will be a full random intercept model.  To estimate Model 2, fixed effect for MU will be added to Model 1 while controlling for other county-level explanatory measures, EHR technology type, provider specialty and practice typeMain Results: Higher HMO penetration rates were significantly associated with lower MU rates among PCPs that participated in the MPI program.  More specifically, providers located in counties with ‘high’ HMO penetration rates (>25.6 %) were 40 percent less likely to attest to MU compared to providers located in counties with ‘low’ HMO penetration rates (<15.5%) (P=.02).  Conclusions and Implications for Health Policy or Practice: As health care costs increase, cost-control mechanisms become more widespread.  It is crucial to understand the implications of these mechanisms for the health care market. Policies regarding increasing EHR adoption have typically focused on providing financial support to physician practices.  While this is an important factor for many providers, particularly the Medicaid providers, the findings for this study suggest that policy makers should not ignore the contribution of healthcare market conditions such as HMO penetration rates in EHR adoption.  Healthcare managers should also assess the effects of their specific markets when making decisions on EHR adoption.

Author

• Pierre K. Alexandre, Florida Atlantic University
  Presenting Author