Hearld, Kristine Ria, PhD

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Assistant Professor, M.S. In Health Administration Program

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The University of Alabama at Birmingham
SHP 563
1720 2nd Avenue South
Birmingham Alabama 35294-1212

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Phone: (205) 934-1670

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Dr. Ria Hearld is currently an Assistant Professor in the Department of Health Services Administration the University of Alabama at Birmingham. Her educational background includes a B.A. in Anthropology, an M.A. in Demography, and a Ph.D. in Demography from the University of Pennsylvania. Dr. Hearld also holds a Masters of Philosophy degree from University of Cambridge. Prior to joining UAB, Dr. Hearld worked for Medical Advantage Group in East Lansing, Michigan, where she served as a consultant and manager for a 200- member independent physician association and a 140-member physician-hospital organization.

Her research interests have focused on the relationships between quality and access to care and long term patient-level health outcomes. Her research has benefited not only from sociological and economic perspectives gained through her training in an interdisciplinary program, but also from ‘on the ground’ experience working in the private sector as a consultant to physicians associated with hospital systems. Dr. Hearld says these experiences have given her a greater appreciation of the opportunities, challenges, and rewards associated with translating research into practice.

Mirroring her interdisciplinary training, Dr. Hearld’s research incorporates multiple methods. To date, most of this research has been quantitative in design, focusing on patterns of performance across large datasets of hospitals and longitudinal analyses of health outcomes. These studies have helped her develop proficiency in several statistical techniques, including longitudinal and categorical data analysis, hierarchical linear modeling, analysis of complex sample survey data, and the evaluation of psychometric properties of surveys. In addition, Dr. Hearld says that these projects have provided her with invaluable data management experience, including multiple imputation techniques for handling missing data and the challenges associated with merging datasets across time and different sources.