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

This study seeks to understand how variation in the size and make-up of care teams on hospital wards in England influences patient outcomes and the costs of care.

Research shows that low registered nurse (RN) staffing levels on hospital wards are linked to undesirable outcomes. These include increased poor experiences for patients, an increased risk of dying and, potentially, other outcomes that are bad for patients and increase the cost of care. These include falls, longer stays and unplanned readmissions. For a long time, studies used hospital level averages rather than looking at what happened to individual patients. This uncertainty makes it hard to understand the likely costs and benefits from investing in staff differently. Developments in information technology now make it possible to link nurse staffing levels experienced by individual patients on every day of their stay, to the outcomes experienced by those patients.

Our research group was the first to use these new sources of information to explore how the mix of staff in the nursing team affected outcomes and cost of care. We found that each additional hour of RN time per patient reduced the risk of death and shortened their hospital stay. We found that a small reduction in assistant staff, and a small increase in RNs would improve outcomes with no overall increase in costs. Such findings have implications for how hospitals respond to nurse shortages, but the results come from one hospital and use limited costs and outcomes. It is important to see if the conclusions apply more widely. As RNs are in short supply it is also important to better understand how other staff contribute.

Our study is in two parts. We will analyse national data at a whole hospital level to see how the size of other staff groups (e.g. therapy staff and doctors) might influence outcomes. In the second part we will use information about ward staff and patients' outcomes drawn from hospital electronic systems. Using statistical models, we will estimate the impact of RN and assistant staff levels on outcomes. For example, whether the risk of death is lower when more RNs are working on a ward. We will estimate staff costs and also the costs of events such as unplanned readmission or longer hospital stay. We will estimate the cost per 'quality adjusted life year' associated with changes in nurse staffing. Such measures help policy makers to compare the results of investments in health care and put more value on each year where people are expected to be healthy and independent.