Research project: Consequences, costs and cost-effectiveness of different workforce configurations in English acute hospitals: a longitudinal retrospective study using routinely collected data

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. We will build on findings from our previous study, where we looked at staffing on wards in a single hospital. 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. In order to determine if results are likely to be sensitive to staff groups not on ward rosters we will use national routine data to explore the associations with staffing levels of other groups including medical and therapy staff.

Methods

Study 1 will be a panel study using routine national workforce data and outcomes (standardised mortality indicators, patient experience) to consider all staff groups including medical and therapy staff at the hospital level. This study will generate hypotheses about staffing for other groups, confirm the independence (or otherwise) of nurse staffing effects and fill a significant gap in international literature about the association between hospital safety and non-nursing staff levels.

Study 2 will be a retrospective longitudinal observational study with 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. We 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. We 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. We will estimate cost per Quality Adjusted Life Year gained or lost (QALY), associated with each staffing configuration using the DANQALE approach.