Economic impact of a Medicaid population health management program.

A population health management program was implemented to assess growth in health care expenditures for the disabled segment of Georgia's Medicaid population before and during the first year of a population health outcomes management program, and to compare those expenditures with projected costs based on various cost inflation trend assumptions. A retrospective, nonexperimental approach was used to analyze claims data from Georgia Medicaid claims files for all program-eligible persons for each relevant time period (intent-to-treat basis). These included all non-Medicare, noninstitutionalized Medicaid aged-blind-disabled adults older than 18 years of age. Comparisons of health care expenditures and utilization were made between base year (2003-2004) and performance year one (2006-2007), and of the difference between actual expenditures incurred in the performance year vs. projected expenditures based on various cost inflation assumptions. Demographic characteristics and clinical complexity of the population (as measured by the Chronic Illness and Disability Payment System risk score) actually increased from baseline to implementation. Actual expenditures were less than projected expenditures using any relevant medical inflation assumption. Actual expenditures were less than projected expenditures by $9.82 million when using a conservative US general medical inflation rate, by $43.6 million using national Medicaid cost trends, and by $106 million using Georgia Medicaid's own cost projections for the non-dually eligible disabled segment of Medicaid enrollees. Quadratic growth curve modeling also demonstrated a lower rate of increase in total expenditures. The rate of increase in expenditures was lower over the first year of program implementation compared with baseline. Weighted utilization rates were also lower in high-cost categories, such as inpatient days, despite increases in the risk profile of the population. Varying levels of cost avoidance could be inferred from differences between actual and projected expenditures using each of the health-related inflation assumptions.