The impact of management practices on relative patient mortality: Evidence from public hospitals.

A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.

The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory.

Queuing theory can and has been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper, we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and 'treatment' rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision-making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool, reducing variation should be considered.

Investigating consistent patterns of variation in short-notice cancellations of elective operations: The potential for learning and improvement through multi-site evaluations.

As part of efforts to increase productivity in healthcare, there is considerable interest in the extent and causes of variation in the performance of provider organisations. In this study, we use publically available data from the English NHS to examine the characteristics of variation in the rates of short-notice cancellations of elective operations due to hospital reasons (e.g. lack of the required resources such as operating theatres and beds). We find that the variation between hospital trusts is very non-random. There is a fourfold difference in the cancellation rates between the top and bottom deciles of performance. Little is known about the causes of this. There is a large and striking consistency in the relative performance of hospital trusts on cancellation rates over the last five years. Thus, the best and worst performers are consistently relatively very good or very poor, so a multi-site comparison of practices, and accounting for confounds like patient demographics, could be very valuable to inform both this knowledge gap and practice in healthcare. Of particular interest is that the cancellation rates could be a symptom of deeper issues with the efficiency of patient flows within hospitals.