Blewett, L. A., G. Davidson, M. Brown, and R. Maude-Griffin. 2003. “Hospital Provision of Uncompensated Care and Public Program Enrollment.” Medical Care Research and Review 60 (4): 509-527.

Hospital provision of uncompensated care is partly a function of insurance coverage of state populations. As states expand insurance coverage options and reduce the number of uninsured, hospital provision of uncompensated care should also decrease. Controlling for hospital characteristics and market factors, the authors estimate that increases in MinnesotaCare (a state-subsidized health insurance program for the working poor) enrollment resulted in a 5-year cumulative savings of $58.6 million in hospital uncompensated care costs. Efforts to evaluate access expansions should take into account the costs of the program and the savings associated with reductions in hospital uncompensated care.

Beebe, T.J., P. A. Harrison, J. A. McRae, and S. E. Asche. 2003. “Evaluating Behavioral Health Services in Minnesota's Medicaid Population Using the Experience of Care and Health Outcomes (ECHO tm) Survey.” Journal of Health Care for the Poor and Underserved 14 (4): 608-621.

Welch, W. P., B. Rudolph, L. A. Blewett, S. Parente, C. Brach, D. Love, and R. Harmon. 2006. “Management Tools in Medicaid and State Children’s Health Insurance Program SCHIP.” Journal of Ambulatory Care Management 29(4): 272-282.

Medicaid and the State Children's Health Insurance Program need analytic tools to manage their programs. Drawing upon extensive discussions with experts in states, this article describes the state of the art in tool use, making several observations: (1) Several states have linked Medicaid/State Children's Health Insurance Program administrative data to other data (eg, birth and death records) to measure access to care. (2) Several states use managed care encounter data to set payment rates. (3) The analysis of pharmacy claims data appears widespread. The article also describes "lessons learned" regarding building capacity and improving data to support the implementation of management tools.

Kincheloe, J., E. R. Brown, J. Frates, K. T. Call, W. Yen, and J. Watkins. 2006. “Can We Trust Population Surveys to Count Medicaid Enrollees and the Uninsured?” Health Affairs 25(4): 1163-1167.

Health foundations, such as the Robert Wood Johnson Foundation (RWJF), make multimillion-dollar investments in programs to expand insurance coverage. These efforts are driven largely by estimates of the number of uninsured people derived from population surveys, which might overestimate the number of uninsured people if they under-count people enrolled in Medicaid. This paper reports the results of the RWJF-funded California Medicaid Undercount Experiment (CMUE) to estimate the extent of underreporting of Medicaid in the California Health Interview Survey (CHIS) and its effect on estimates of uninsurance. Although some over- and underreporting occurs, overall CHIS Medicaid estimates match administrative counts for adults.

McAlpine, D., Beebe, T. J., Davern, M. E., & Call, K. T. 2007. “Agreement between Self-Reported and Administrative Race and Ethnicity Data among Medicaid Enrollees in Minnesota.” Health Services Research 42(6, part II): 2373-2388.

OBJECTIVE: This paper measures agreement between survey and administrative measures of race/ethnicity for Medicaid enrollees. Level of agreement and the demographic and health-related characteristics associated with misclassification on the administrative measure are examined. DATA SOURCES: Minnesota Medicaid enrollee files matched to self-report information from a telephone/mail survey of 4,902 enrollees conducted in 2003. STUDY DESIGN: Measures of agreement between the two measures of race/ethnicity are computed. Using logistic regression, we also assess whether misclassification of race/ethnicity on administrative files is associated with demographic factors, health status, health care utilization, or ratings of quality of health care. DATA EXTRACTION: Race/ethnicity fields from administrative Medicaid files were extracted and merged with self-report data. PRINCIPAL FINDINGS: The administrative data correctly classified 94 percent of cases on race/ethnicity. Persons who self-identified as Hispanic and those whose home language was English had the greater odds (compared with persons who self-identified as white and those whose home language was not English) of being misclassified in administrative data. Persons classified as unknown/other on administrative data were more likely to self-identify as white. CONCLUSIONS: In this case study in Minnesota, researchers can be reasonably confident that the racial designations on Medicaid administrative data comport with how enrollees self-identify. Moreover, misclassification is not associated with common measures of health status, utilization, and ratings of quality of care. Further replication is recommended given variation in how race information is collected and coded by Medicaid agencies in different states.