Relationship Between Skilled Nursing Facility Nurse Staffing Levels and Resident Rehospitalizations

• Main Author: Bowens, Crystal Spring
• Format: Others
• Language: en
• Published: ScholarWorks 2019
• Subjects: Nursing Homes; Readmissions; Rehospitalizations; Skilled Nursing Facilities; SNF; Staffing; Medicine and Health Sciences
• Online Access: https://scholarworks.waldenu.edu/dissertations/6308; https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=7587&context=dissertations

Readmission of skilled nursing facility (SNF) residents has become a financial and quality-of-care concern for facility leaders. SNF administrators do not know whether nurse staffing levels are impacting readmission rates. The Affordable Care Act included measures to monitor and improve quality and to penalize SNFs that have high readmission rates. The purpose of this quantitative correlational study was to examine the relationship between SNF nurse staffing levels and readmission rates using the Skilled Nursing Facility Readmission Measure (SNF RM). The theoretical framework for the study was Donabedian's structure, process, outcome model. The research questions addressed the relationship between nurse staffing levels and rehospitalization percentages for SNFs, and the relationship between RN staffing levels and rehospitalization percentages. A quantitative methodology was used to analyze publicly reported secondary data from Centers for Medicare and Medicaid Services staffing files and SNF Value-Based Purchasing (SNF VBP) program data. Pearson's correlation was used to examine the relationship and strength between nurse staffing levels and the SNF RM. The sample included 374 SNFs across Georgia that participated in the SNF VBP program. Findings from the multiple regression analysis and analysis of variance indicated no statistically significant relationship between nurse staffing levels and SNF RM rates. Facility characteristics across Georgia showed some variations in staffing levels and SNF RM rates. Findings promote positive social change by providing SNF leaders with needed information to make decisions about staffing needs when considering staffing above the state averages. Health care leaders and policymakers might use the findings when considering recommendations for staffing regulations.