Reallocation of operating room capacity using the due-time model.
ABSTRACT Demand for surgical treatment is rising while operating room (OR) resources are limited. Requests for more resources therefore can only be partly met by repartitioning the existing sparse resources.
Our goal is to define a method to allocate OR block times among surgical disciplines in such a way that patients can be treated within an acceptable time after the need for surgery is established. In this paper, we introduce and explore the potential of the concept of the individual patient deviation from the optimal due time (DT) as a potential driver for OR (re-) allocation.
Using retrospective data for abdominal and gynecologic surgery, we analyzed DT deviation and 3 additional modifiers. From this analysis, a reallocation of OR time to the different (sub-) specialties was calculated using a simple model.
The results show the capability of measuring and visualizing relative overcapacity versus undercapacity of OR resources with respect to this patient-centered metric of DT. The reallocation results from the model show a potentially significant shift between programs.
We propose the "due-time" concept as a valid measure to quantify OR resource use. The use of a DT-based model provides a transparent, acceptable system for regular reallocation of OR times between and within specialties.
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ABSTRACT: We considered the allocation of operating room (OR) time at facilities where the strategic decision had been made to increase the number of ORs. Allocation occurs in two stages: a long-term tactical stage followed by short-term operational stage. Tactical decisions, approximately 1 yr in advance, determine what specialized equipment and expertise will be needed. Tactical decisions are based on estimates of future OR workload for each subspecialty or surgeon. We show that groups of surgeons can be excluded from consideration at this tactical stage (e.g., surgeons who need intensive care beds or those with below average contribution margins per OR hour). Lower and upper limits are estimated for the future demand of OR time by the remaining surgeons. Thus, initial OR allocations can be accomplished with only partial information on future OR workload. Once the new ORs open, operational decision-making based on OR efficiency is used to fill the OR time and adjust staffing. Surgeons who were not allocated additional time at the tactical stage are provided increased OR time through operational adjustments based on their actual workload. In a case study from a tertiary hospital, future demand estimates were needed for only 15% of surgeons, illustrating the practicality of these methods for use in tactical OR allocation decisions.Anesthesia & Analgesia 06/2005; 100(5):1425-32, table of contents. DOI:10.1213/01.ANE.0000149898.45044.3D · 3.47 Impact Factor
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ABSTRACT: This paper sets out to investigate whether demand for gynaecological theatre time could be described in terms of the time required to undertake elective operations booked for surgery, and so help match the capacity to this. A questionnaire assessed the estimates for total operation time for seven common operations, sent to surgeons, anaesthetists and nursing staff in one tertiary referral and one district general hospital (total 49 staff; response rate 58 per cent), and estimates were obtained from theatre computer logs. Average timings for each operation were then applied to cases added from clinics to the waiting list at the district general, to yield the mean demand for elective surgery, and were also applied to emergencies to estimate emergency workload. Finally these demand estimates were compared with the theatre capacity available. The paper found no difference between the estimates of the three staff groups or between these and the theatre logs (p = 0.669), nor did it find that estimates differed between the two centers (p = 0.628). Including emergencies, the mean (95 per cent confidence intervals) demand at the district general was 2438 (1952-2924) min/week. Although the paper modelled the variation in demand using the relevant variation in operation times, any additional variation caused by differences in booking rates from clinics over time was not nodelled. The minimum period over which data should be collected was not established. The paper finds that the existing capacity of 1680 min/week did not match these needs and, unless it was increased, a rise in waiting lists was predictable. The paper concludes that time estimates for scheduled operations can be better used to assess the need for surgical operating capacity than current measures of demand or capacity.Journal of Health Organisation and Management 09/2009; 23(5):554-67. DOI:10.1108/14777260910984032 · 0.36 Impact Factor
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ABSTRACT: In an attempt to make rationing of elective surgery in the publicly funded health system more explicit, New Zealand has developed a booking system for surgery using clinical priority assessment criteria (CPAC). This paper is based on research undertaken to evaluate the use of CPAC. To explore whether the goals of explicit rationing were being met 69 interviews were undertaken with policy advisors, administrators and clinicians in six localities throughout New Zealand. The aims of reforming policy for access to elective surgery included improving equity, providing clarity for patients, and achieving a paradigm shift by relating likely benefit from surgery to the available resources. The research suggests that there have been changes in the way in which patients access elective surgery and that in many ways rationing has become more explicit. However, there is also some resistance to the use of CPAC, in part due to confusion over whether the tools are decision-aids or protocols, what role the tools play in achieving equity and differences between financial thresholds for access to surgery and clinical thresholds for benefit from surgery. For many surgical specialties implicit rationing will continue to play a major part in determining access to surgery unless validated and reliable CPAC tools can be developed.Health Policy 10/2005; 74(1):1-12. DOI:10.1016/j.healthpol.2004.12.011 · 1.91 Impact Factor