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Dr Cook compliments many aspects of our new formula for efficiency [1] but argues that we have not modelled the delays in between cases (commonly referred to as ‘turnover time’– the time when no anaesthesia or surgery takes place). Cook is mistaken: our measure of utilisation actually incorporates this time, as shown by the following example. If a hospital knows its mean times for operations and books lists accordingly, lists should be properly utilised and on average not over-run. If, however, turnover times at this hypothetical hospital are extremely long, this would clearly add to total list time and the lists would over-run: this would be properly reflected in our calculation of efficiency.

At worst our formula might be criticised for not making a specific comment on turnover time, and it is pertinent for us to outline why we chose not to do this. We agree that slow turnovers are an irritation and frustration – delays are subjectively perceived to be a potent cause of ‘inefficiency’ on a surgical list [2]. However, that perception is both incorrect and misleading. Whenever the issue has been formally studied, reductions in turnover time cannot be shown significantly to increase overall performance [3–6]. This is because actual turnover times are usually of the order of approximately 10–15 min between cases: even complete elimination of these delays rarely facilitates the addition of even one extra case onto a list (that is, the time saved simply does not ‘add up’ to any useful total) [3–6]. Second, the true impact of reducing turnover times is shown to depend exquisitely on the duration of the case(s) on that list. A total reduction in delays of about 30 min on a list consisting of cases each lasting approximately 15 min will enable two more cases to be added. The same reduction in delays on a list where cases are about 45 min long will achieve nothing [3–6]. Eliminating turnover time might cause the list to finish early (which is certainly popular, enhances staff morale and gives the perception of ‘efficiency’) but an early finish does not of itself actually enhance performance. Finally, delays between cases are naturally highly dependent upon the number of cases on the list. Thus a list with just one or two cases will, by definition, always have a smaller turnover or delay time than a list with 10 or 12 cases [3–6]. For these reasons, some authors have gone as far as describing the analysis of turnover times as ‘meaningless’ (that is, the effort or expense involved generally outweighs any conceivable gain) [6]. Indeed, Cook's own data from Bath confirm this point. He explicitly states that his preferred measure of theatre utilisation (which eliminates delay time) is 10–15% lower than the chosen measure in our formula. This percentage translates at best to just 21–36 min of a half-day list. So, even if delays were completely abolished (itself unlikely) it is difficult to imagine which extra case(s) might be accommodated in the saved time, without risk of over-running. No operation in the 20 analysed by Silber et al. [7] and Pandit [8], and perhaps just one examined by Pandit and Carey (check cystoscopy at 33 min) [9] would reliably fall into this time window. We do not know the full details in Bath, but from the figures provided by Cook, we suggest that they would need to consider carefully if investment in reducing turnover times will be justified by any potential gains.

We do not wish to hold an extreme view on turnover times. It is conceivable that there are hospitals where turnover times are ridiculously long and are the only blot on an otherwise near-perfect system. Equally, in hospitals where lists are persistently over-booked (as in our dataset), anything that reduces the total list time (and thereby brings it closer to the scheduled finish time) is beneficial. But in such hospitals it is important to emphasise that the root problem is not turnover, it is over-booking!

In summary we thank Cook for his suggestion, but we confirm that our formula will detect over-runs due to long turnover times, so no modification is needed in this respect. We also confirm – as others have done before – that reducing turnover times alone rarely, if ever, increases actual efficiency of a surgical list. It is appropriate for hospitals to look at Cook's preferred measure of utilisation in addition to (but not instead of) the measure that we suggest. However, we believe that the main cause of inefficiency on surgical lists is not turnover, it is over-booking [1].

References

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  2. References
  • 1
    Pandit JJ, Westbury S, Pandit M. The concept of surgical operating list ‘efficiency’: a formula to describe the term. Anaesthesia 2007; 62: 895 903.
  • 2
    Durani, P. Seagrave M, Neumann L. The use of theatre time in elective orthopaedic surgery. Annals of the Royal College of Surgeons 2005; 87 (Suppl.): 1702.
  • 3
    Abouleish AE, Hensley SL, Zornow MH, Prough DS. Inclusion of turnover time does not influence identification of surgical services that over- and underutilise allocated block time. Anesthesia and Analgesia 2003; 96: 8138.
  • 4
    Dexter F, Abouleish AE, Epstein RH, Whitten CW, Lubarsky DA. Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesthesia and Analgesia 2003; 97: 111926.
  • 5
    Mathias JM. Operating room efficiency: benchmarking operating room turnover times. Operating Room Manager 2000; 16: 14.
  • 6
    Brenn BR, Reilly JS, Deutsch ES, Hettrick MH, Cook SC. Analysis of efficiency of common otolaryngology operations: comparison of operating room vs. short procedure room in a paediatric tertiary hospital. Archives of Otolaryngology, Head and Neck Surgery 2003; 129: 4357.
  • 7
    Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Influence of patient and hospital characteristics on anaesthesia time in Medicare patients undergoing general and orthopedic surgery. Anesthesiology 2007; 106: 35664.
  • 8
    Pandit JJ. Similarity of operation times for common general surgical procedures in the United Kingdom and the United States. Anesthesiology 2007; 107: 5123.
  • 9
    Pandit JJ, Carey A. Estimating the duration of common elective operations: implications for operating list management. Anaesthesia 2006; 61: 76876.