|Friday, February 22|
|CS06 Theme 3: Prediction and Analytics #2||
Fri, Feb 22, 10:45 AM - 12:15 PM
Supporting Healthcare Policy Initiatives through Modeling Efforts: Issues of Data Capacity and Statistical Quality (302437)
Keywords: modeling, projections, data quality
There is a strong demand for timely, high quality and precise estimates of health care parameters at the national and sub-national levels, and associated readily accessible data resources to inform health care policy and practice. Existing sentinel health care databases that provide nationally representative population based data on measures of health care access, cost, use, health insurance coverage, health status and health care quality, provide the necessary foundation to support descriptive and behavioral analyses of the U.S. health care system. To complement these assessments of the “current state” of health care, policymakers also depend on model-based estimates of the “future state” under alternative demographic,economic and technological assumptions, which are subject to greater levels of uncertainty traditionally associated with sampling and nonsampling error. Such modeling efforts directed to predicting the “future state” include economic models projecting health care expenditures and utilization, estimating the impact of changes in financing, coverage, and reimbursement policy, and determining who benefits and who bears the cost of a change in policy. Government and non-governmental entities rely upon these data to evaluate health reform policies, the effect of tax code changes on health expenditures and tax revenue, and proposed changes in government health programs. Comparable standards of data quality and statistical integrity for these types of modeling and microsimulation efforts are needed to ensure policymakers have a sound understanding of the level of uncertainty associated with these model-based estimates.This presentation will focus on several of these ongoing health care modeling and microsimulation efforts to characterize sources of uncertainty in the resultant estimates and methodologies that can be employed to better quantify their error bounds.