Workforce Hospital Outcomes (in English Acute Hospitals)

Background: The NHS is facing significant challenges in recruiting and retaining staff, particularly registered nurses (RNs). Recruiting unregistered staff is often adopted as a solution to the RN shortage; however recent research found a negative effect of low RN staffing levels on mortality with no evidence that high levels of assistant staff could mitigate the increased risk. Economic modelling suggested that increases in skill mix were potentially cost-effective, but these findings derive from a single NHS hospital Trust with limited cost and outcome data. Aims and objectives: This project aims to estimate the consequences, costs and cost effectiveness of variation in the size and composition of the staff on hospital wards in England. In order to provide estimates that are more likely to apply across the NHS, this study will include at least four hospitals and consider a wider range of outcomes and sources of costs, including death within 30 days of admission, adverse events such as infections, length of hospital stay, readmissions and rates of staff sickness. Methods: This retrospective longitudinal observational study will use routinely collected data on ward and shift level nurse staffing, and patient outcomes. Data will be derived from the E-Roster systems, used by hospitals to record all planned and worked shifts. The investigators will consider all rostered direct care staff. These data will be linked to patient data derived from the hospital patient administration system (PAS); and other clinical systems and databases of adverse events (e.g. datix). Relationships between RN and assistant staffing levels and outcomes will be explored using survival models incorporating mixed effects. The investigators will use the results of these analyses to model the costs and consequences of different staffing configurations and to estimate the incremental cost-effectiveness associated with change. Our study will provide evidence to inform staffing levels and skill mi