Managing Medicaid Misclassification: Using Simulation Techniques to Identify Administrative Leverage Points in Policy Implementation
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Date
2017-03
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Abstract
A fundamental challenge for state Medicaid programs is the ongoing task of implementing beneficiary enrollment according to specified eligibility criteria. Errors in enrollment present themselves in the form of eligible individuals not taking up coverage (missed take-up), individuals continually moving on and off the program (churn), and ineligible individuals receiving coverage (fraud). In addition to posing problems for the coverage and continuity of care of vulnerable populations, enrollment errors create difficulties for state program planning, budgeting, and contract arrangements.
Despite passage of the Affordable Care Act in 2010, state Medicaid programs remain varied in the structure of their eligibility categories and their processes for determination and enrollment of beneficiaries. There are multiple levels of prescribed action and discretion in these systems, and both short (month-to-month) and long (multiple year) time horizons are of interest. Previous studies model enrollment outcomes as a deterministic function of personal characteristics (e.g., race/ethnicity) and general economic indicators (e.g., unemployment rate). The policies and procedures defining design and administration of the program are rarely included in models estimating enrollment patterns, thus ignoring potentially important sources of enrollment dynamics. The purpose of this research is to understand the mechanism by which Medicaid eligibility criteria are transformed into enrollment outcomes among state program populations.
Drawing on insights from systems science and implementation research, this study uses simulated experiments with program structure to describe and explain the dynamics of Medicaid eligibility determination and enrollment processes. Program-level state Medicaid enrollment patterns are modeled in terms of the dynamics among individual characteristics, program eligibility criteria, and administrative procedures within relevant social, economic, demographic, and political contexts. The effects of federal guidelines, along with exemplary cases of state eligibility rules and determination procedures, are analyzed through a set of system dynamics models of program-level enrollment patterns. This study tests the possible effects of a number of implementation strategies drawn from the Medicaid enrollment literature on eligibility determination and benefit enrollment errors.
Interrelationships among defined eligibility categories and (re)determination procedures create enrollment implementation error in Medicaid systems even when household economic conditions and decision making are stable. Delays in (re)determination processes lead to accumulations of households in enrollment states not consistent with their eligibility. A nonlinear relationship between the demand for benefits and the administrative capacity to process applications leads to further accumulation of misclassified households. Enrollment errors, including missed take-up, churn, and fraud, are shown to arise form the structure and dynamics of the program system itself, rather than solely from individual circumstance or exogenous economic shock.
These simulations allow policy makers and scholars to experiment with various implementation strategies with effectively zero social costs. Illuminating the core dynamics of the Medicaid enrollment mechanism aids in forecasting enrollment and spending levels, revealing administrative leverage points to improve system performance, and evaluating potential tradeoffs between costs and coverage over time. Additionally, the simulations can be tailored to specific economic, demographic, and program conditions, providing a picture of the possible range of outcomes associated with specific administrative actions.
Description
Poster Division: Arts, Humanities, and Social Sciences: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
Keywords
Medicaid, implementation, simulation, complex systems