This session will provide an overview of the Medicaid program and the Medicaid population, to provide a framework for understanding the kinds of questions that may or may not be answered by using Medicaid claims data. Presentation will especially emphasize the potential value as well as limitations of Medicaid claims data for answering epidemiologic, health policy, and health services research questions. The Medicaid claims data will be presented as a complex data set connected to a fragmented delivery system for a dynamic population through a fiscal intermediary. For example, the Medicaid population is by definition a low-income population of individuals or families who have had a reason to apply for coverage and who have maintained current coverage. This leads to issues of discontinuity in the population, as well as selection bias from the broader population of low-income individuals. Issues of state-to-state and year-to-year comparability of data will also be explored. Participants may expect to learn the age and socioeconomic distribution of the Medicaid population, the types of services covered, the billing mechanisms that generate claims, and the potential biases inherent in capturing actual utilization through administrative claims data. At the same time the presentation will emphasize the richness of the data set and the opportunities it affords to answer questions of disparities in health outcomes.
Learning Objectives: At the end of this session the learner should be able to: 1. Describe age / gender characteristics of the Medicaid population. 2. Define specific sources of selection bias in th Medicaid population. 3. Name the services covered by Medicaid, and the process by which paid claims enter into the administrative database. 4. Articulate the critical points at which there may be state-to-state variability or year-to-year variability in Medicaid population or program
Keywords: Medicaid, Health Care Utilization
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: We will mention the availability of technical assistance through the HCFA-funded consortium known as Research Data Assistance Center (ResDAC), which is housed at University of Minnesota, but which also includes faculty from Boston U., Dartmouth, and Moreh
I have a significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.
Relationship: Morehouse School of Medicine is one of four academic partners in ResDAC. Support for travel and workshops is provided with HCFA funds by sub-contract through Univ. of Minnesota