Date of Award
Open Access Dissertation
Doctor of Philosophy (PhD)
Laura L. Hayman
Background: Previous research and quality improvement initiatives have underscored the prevalence of healthcare acquired conditions (HACs) and their associated costs in American hospitals. In response to these findings, in 2008, The Centers for Medicare and Medicaid Services identified 10 condition categories that they would no longer pay for if acquired during hospitalization. The conditions were selected based on high cost, high volume, or both, assigned to a higher paying medical severity diagnostic related group (MS-DRG), and were deemed preventable through application of evidence-based guidelines. The Health Quality Outcomes Model and a Path Model guided the study.
Objective: To quantify the association between patient and hospital characteristics, and nursing care intensity of HACs.
Data Sources: Medicare Provider Analysis and Review file, Provider of Service file, 2010 Medicare Occupational Mix Adjustment Survey for Acute Care Hospitals, Medicare Hospital and Hospital Health Care Complex Cost Report, and Magnet Hospital List.
Methods: Pooled cross-sectional secondary analysis of a random set of Medicare beneficiaries admitted to an inpatient prospective payment system hospital (2009 - 2011). Descriptive statistics, correlation analysis, and multivariate regression analyses were computed.
Results: The significant predictors of a reported HAC were length of stay (LOS) and severity of illness (SOI). Patients with a high SOI were 9-times more likely than patients with a lower SOI to incur an HAC. Controlling for LOS, the likelihood of a patient incurring an HAC declined almost 1/3 (OR= 8.9 vs. 12.8). High (>20.1) RN hours per patient day were significantly (p=
Conclusions: The hospital acquired condition program is a significant step in aligning pay-for-performance incentives for reducing hospital-acquired conditions and infections. This policy has important implications for health care quality and costs and research should be conducted to evaluate the long term consequences of this policy.
Kahlert Eng, Terry, "The Impact of Nursing Hours and Hospital and Patient Characteristics on Medicare Hospital Acquired Conditions: A National Pooled Cross-Sectional Secondary Data Model and Analysis" (2015). Graduate Doctoral Dissertations. 207.