Use of Claims Data to Screen for Functional Limitations Among Medicare Beneficiaries
This paper describes the development of a survey-based index of functional limitations and a new claims-based model for predicting limitations.
Megan Mathews is a senior statistical analyst at RAND. Her research focuses on the development, evaluation, and implementation of the health care quality measurement program, patient experience assessments, disparities, and the Medicare population. She utilizes survey methodology to design and implement sampling procedures as well as analyze complex data using techniques such as sampling weights, case-mix adjustment, non-response, and response rate analyses. She uses statistical techniques such as propensity scoring and matching, multi-level, mixed, and survival modeling methods, to draw inferences between care processes, outcomes, and social determinants of health. She provides analytic leadership on large-scale sampling and national scoring tasks that inform public reporting and pay-for-performance initiatives, ensuring methodological rigor, reproducibility, and meaningful dissemination of results.
Before joining RAND, she worked as a data research analyst in the workers’ compensation insurance industry, where she applied predictive analytics to improve claims handling and evaluate operational performance. She also held a research position at the Office of Immigration Statistics in the Department of Homeland Security and a graduate fellow internship at the National Institutes of Health. She holds a Master of Applied Statistics and a Bachelor of Science in statistics from Pennsylvania State University.
M.S. in applied statistics, Pennsylvania State University; B.S. in statistics, Pennsylvania State University