Regular and Off-Shift Nursing: Impacts on Patient Outcomes and Cost of Care

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Anticipated Impact on Veterans Health Care: The knowledge gained from this study will help to inform hospital staffing practices, nursing delivery and patient outcomes across the VA healthcare system. To ensure dissemination and translation of our findings into effective VA policies, we have recruited a high-level advisory group representing important stakeholders and thought leaders including the Chief Nursing Officer from the VA, and representatives from both the Joint Commission, and the Institute of Healthcare Improvement. Project Background: Although there is increasing evidence that establishes the positive relationship between the level of RN staffing and quality patient outcomes in general acute care settings, there are still significant scientific knowledge gaps. For example, hospitals provide 24-hour, 7-day a week service and there is evidence that patients admitted on off-shifts (nights, weekends, and holidays) have worse outcomes. However, there is almost no research that has been conducted examining relationships between off-shifts nurse staffing and patient outcomes. Due to limitations in previous datasets, researchers have not been able to account for variation in patient needs at the unit-level, nor have they been able to consider other important workforce characteristics such as education and tenure. Furthermore, most nurse staffing studies have been cross- sectional, which limits the ability to infer causality. Finally, very few studies have explored the economic considerations related to nurse staffing. We are just completing a project that has exploited the detailed VA data systems to address some of these limitations. Specifically, we used a 4-year panel of monthly, unit-level data for all VA acute care units to examine the effects of nurse staffing on adverse patient events and length of stay (LOS). By integrating VA payroll data, we were able to move beyond just staffing levels to examine the effects of contract nurses, the characteristics of the nursing staff (education, VA tenure, etc.), and the stability of the nursing teams. Project Objectives: The purpose of this project is to expand on our current research to examine how the differences in nurse staffing between regular and off-shifts affect patient outcomes, and to conduct a detailed examination of the trade-offs between the costs and benefits of increased nurse staffing. The existing dataset we have created integrates patient, accounting, and payroll data from all VA acute care hospitals across the nation for FY 03-06. We propose to expand this through FY11, to pull DSS cost of care data, and to develop indicators related to off-shift nurse staffing to create the most comprehensive picture of nurse human capital and patient outcomes available in the United States. We will also extend the model to control for treating physician. Using this dataset, in the next two years our aims are 1) examine how the differences between regular shifts and off-shifts in nursing inputs (staffing levels, skill mix, contract nurses, and general, facility- specific, unit/team-specific human capital) effect nursing-sensitive patient outcomes and 2) analyze efficiency in nursing services by studying the trade-offs between nursing personnel costs and cost savings from a reduction in nursing-sensitive adverse events and LOS. Project Methods: We will use fixed effects models to obtain the most current and comprehensive examination of nurse staffing strategies and the impact on important nursing sensitive patient outcomes (e.g., decubitus ulcers, failure-to-rescue) and LOS. Staffing will be controlled separately for unit type (ICUs vs. other acute care units). In addition to controlling for nurse staffing and the characteristics of the nursing team, we will include variables to capture how the nurse staffing on the off-shifts differs from regular shifts. To compute the cost of various staffing strategies, we will use the estimates of the effects of the human capital variables from the previous aim 1 and link these data to associated wage data. We will re-run models using the cost of patient encounters as the dependent variable to estimate the potential cost savings.