Abstract
Abstract Background: ‘Failure to rescue’ - death after a treatable complication - is used as a nursing sensitive quality indicator in the USA. It is associated with the size of the nursing workforce relative to patient load, for example patient to nurse ratio, although assessments of nurse sensitivity have not previously considered other staff groups. This study aims to assess the potential to derive failure to rescue and a proxy measure, based on long length of stay, from English hospital administrative data. By exploring change in coding practice over time and measuring associations between failure to rescue and factors including staffing, we assess whether these are useful nurse sensitive indicators. Design: Cross sectional observational study of routinely collected administrative data. Participants: Discharge data from 66,100,672 surgical admissions to 146 general acute hospital trusts in England (1997 to 2009). Results: Median percentage of surgical admissions with at least one secondary diagnosis recorded increased from 26% in 1997/8 to 40% in 2008/9. Regression analyses showed that mortality based failure to rescue was significantly associated (p<0.05) with several hospital characteristics previously associated with quality, including staffing levels. Lower rates of failure to rescue were associated with a greater number of nurses per bed and doctors per bed in a bivariate analysis. Higher total clinically qualified staffing (doctors + nurses) per bed and a higher number of doctors relative to the number of nurses were both associated with lower mortality based failure to rescue in the fully adjusted analysis (p<0.05); however, the extended stay based measure showed the opposite relationship. Conclusion: Failure to rescue can be derived from English administrative data and may be a valid quality indicator. This is the first study to assess the association between failure to rescue and medical staffing. The suggestion that it is particularly sensitive to nursing is not clearly supported, nor is the suggestion that the number of patients with an extended hospital stay is a good proxy.