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Title
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DETERMINANTS OF PRODUCTIVITY IN HOSPITAL-BASED RURAL HEALTH CLINICS: A GROWTH CURVE MODELING APPROACH.
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Creator
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Agiro, Abiy, Wan, Thomas, University of Central Florida
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Abstract / Description
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The Patient Protection and Affordable Care Act of 2010 expanded rural Medicaid and Medicare coverage. However, different vehicles of delivering care (e.g., hospitals, health clinics, etc.) have differing organizational capacity that may or may not enable them to overcome the challenges of expanded provision. Consequently, this research employed structural contingency and organizational performance models to investigate the impact of organizational factors on productivity growth, while...
Show moreThe Patient Protection and Affordable Care Act of 2010 expanded rural Medicaid and Medicare coverage. However, different vehicles of delivering care (e.g., hospitals, health clinics, etc.) have differing organizational capacity that may or may not enable them to overcome the challenges of expanded provision. Consequently, this research employed structural contingency and organizational performance models to investigate the impact of organizational factors on productivity growth, while recognizing that contextual factors also affect the delivery of care. Latent growth curve modeling was used to study a national panel of 708 U.S. hospital-based Rural Health Clinics for the years 2005 to 2008. Productivity was measured through dynamic slacks-based data envelopment analyses. Unconditional and conditional linear growth curve models were fitted to data. Findings revealed that 1) hospital-based clinics with higher baseline levels of productivity in 2005 had a slower rate of growth in productivity for the years 2006 to 2008, 2) hospital-based clinics with physicians had significantly higher productivity, 3) hospital-based clinics in urban focused areas had significantly higher productivity, 4) newer hospital-based clinics had significantly higher productivity, and 5) prospective payment system was negatively related to the rate of change in productivity growth. Organizational and contextual factors included in this study significantly explained initial differences in productivity but were unable to explain productivity growth.Future research could improve the study by 1) including additional explanatory variables, such as the use of technology and disease management programs, 2) adjusting productivity measures by case mix measures, and 3) conducting truncated panel data regression with Monte Carlo simulation.
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Date Issued
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2011
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Identifier
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CFE0003912, ucf:48753
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0003912
