Waiting times for elective surgery are a key issue for the NHS. The principal policy response in the English NHS has been to introduce maximum waiting time targets against which performance is measured and rewarded. The aim of this paper is to identify the effect of government targets on the distribution of waiting times in the NHS. Specifically, we investigate the following questions: How does the probability of admission for any given patient vary during the time that they wait? How is the probability of admission for any given waiting time affected by the targets? Can variations in waiting times be explained by clinical, patient, or provider-level characteristics? What implications may be drawn from our results with respect to providers' managerial responses to the targets? This paper investigates these questions by applying duration analysis techniques to waiting time data from 2001/2002 and 2002/2003 for three specialties: general surgery, trauma & orthopaedics and ophthalmology. Estimation of survival functions reveals considerable variations in waiting times between specialties, operative procedures and hospitals. Hazard rates vary over time and peaks in them-high probabilities of admission-coincide with targets and change when targets change. Amongst patient characteristics, whether they are NHS or private and whether they are day or inpatient cases both influence waiting times, but other characteristics such as age, sex and ethnicity do not.

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Excessive waiting times for elective surgery have been a long-standing concern in many national healthcare systems in the OECD. How do the hospital admission patterns that generate waiting lists affect different patients? What are the hospitals characteristics that determine waiting times? By developing a model of healthcare provision and analysing empirically the entire waiting time distribution we attempt to shed some light on those issues. We first build a theoretical model that describes the optimal waiting time distribution for capacity constraint hospitals. Secondly, employing duration analysis, we obtain empirical representations of that distribution across hospitals in the UK from 1997–2005. We observe important differences on the ‘scale’ and on the ‘shape’ of admission rates. Scale refers to how quickly patients are treated and shape represents trade-offs across duration-treatment profiles. By fitting the theoretical to the empirical distributions we estimate the main structural parameters of the model and are able to closely identify the main drivers of these empirical differences. We find that the level of resources allocated to elective surgery (budget and physical capacity), which determines how constrained the hospital is, explains differences in scale. Changes in benefits and costs structures of healthcare provision, which relate, respectively, to the desire to prioritise patients by duration and the reduction in costs due to delayed treatment, determine the shape, affecting short and long duration patients differently. JEL Classification I11; I18; H51

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