Determining Optimum Operating Room Utilization

Tyler, Denise; Pasquariello, Caroline A.; Chen, Chun‐Hung

doi:10.1213/01.ane.0000050561.41552.a6

Cited by 127 publications

(95 citation statements)

References 4 publications

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“…Underutilization is defined as time during the scheduled day not used for operations and overutilization is time used after the end of the scheduled day to finish cases. [10,11] Cardoen et al note the distinction between overutilization/underutilization and overtime/undertime pay, since it is possible for a hospital to pay employees overtime on a day in which the hospital ORs were underutilized. For example, imagine two ORs with daily capacities of 4 hours.…”

Section: Discussionmentioning

confidence: 99%

Improving operating room start times in a community teaching hospital

Darwish

Mehta

Mahmoud

et al. 2016

JHA

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Introduction: The operating room (OR) is an expensive entity to manage. Efficiency in hospital resource utilization is critical for hospital financial solvency. One measure of efficiency in the OR is percentage of on-time starts for cases at the beginning of each day. This study looks at a community teaching hospital where measures were taken to identify and address causes of tardiness in the OR. Methods: An interdisciplinary team of doctors, nurses, and other hospital staff came together to implement a three-phase agenda. In Phase I, staff identified causes of tardiness. In Phase II, potential solutions to address each specific task were drawn up. Phase III involved maintenance of efficiency measures created in Phase II and documentation of progress for future analysis. Results and Discussion: Over twelve months, the percentage of cases that started on time steadily increased from 14% to 68%. Additionally, of the cases that were late, the average number of minutes late decreased significantly. Of the identified causes of tardiness, surgeon arriving late was found to be the most prevalent. We analyzed the relationship between average minutes late each month and the cost and revenue per unit of service (UOS). Average minutes late per month and hospital revenue per UOS showed a strong inverse correlation of -0.83, while average minutes late per month and cost er UOS showed a moderate positive correlation of 0.62. We analyzed the relationship between average minutes late each month and the cost and revenue per UOS. Average minutes late per month and hospital revenue per UOS showed a strong inverse correlation of -0.83, while average minutes late per month and cost per UOS showed a moderate positive correlation of 0.62. Conclusions: Identifying causes of tardiness based on input from a multidisciplinary healthcare team and addressing each cause with a specific measure to combat it was effective in improving the percentage of on-time starts in the OR. We demonstrated that reducing delays in OR start times can both decrease cost and increase revenue. Documenting progress of efficiency measures is critical in distinguishing measures that work from those that do not. Furthermore, continued analysis of efficiency is required to maintain efficiency standards.

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Section: Discussionmentioning

confidence: 99%

Improving operating room start times in a community teaching hospital

Darwish

Mehta

Mahmoud

et al. 2016

JHA

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show abstract

“…Note that simulation is often preferred as the solution technique to study complex stochastic set- [3,5,6,9,10,17,20,25,26,27,28,41,43,44,49,54,60,62,63,64,70,75,76,78,79,82,83,84,86,88,92,94,97,104,109,110,111,112,113,114,115,120,122] other [20,35,37] tings, as it features an extensive modeling flexibility. Next to arrival and duration uncertainty, other types of uncertainty may be addressed.…”

Section: Uncertaintymentioning

confidence: 99%

Operating room planning and scheduling: A literature review

Cardoen

Demeulemeester

Beliën

2010

European Journal of Operational Research

883

539

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“…The coefficient of variation is an indicator of the variability of a system and equals the standard deviation divided by the mean (Tyler et al 2003). A higher coefficient of variation indicates high variability in surgery duration, which leads to more uncertainty in the realization of instances.…”

Section: Classification Of Surgery Scheduling Instancesmentioning

confidence: 99%

“…This effect may necessitate applying a more robust approach, in which (for example) scheduled slack time is used to alleviate the effects of uncertain surgery durations (Hans et al 2008). A low coefficient of variation results in easier scheduling problems with almost no risk of incurring overtime and a high probability of fully utilizing OR-blocks, for example in an outpatient OR department (Tyler et al 2003).…”

Section: Classification Of Surgery Scheduling Instancesmentioning

confidence: 99%

“…3. This case mix gives the opportunity to derive high performances on different performance indicators, for example in terms of utilization, compared to case mixes with higher coefficients of variations or longer surgery durations (Tyler et al 2003). Lamiri et al (2009) developed methods for operating room planning with shared capacity.…”

Section: Example Application Of Case MIX Classificationmentioning

confidence: 99%

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Case mix classification and a benchmark set for surgery scheduling

Leeftink

Hans

2017

J Sched

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Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

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Determining Optimum Operating Room Utilization

Abstract: Using a simulated operating room (OR), the authors demonstrate that OR utilization higher than 85% to 90% leads to patient delays and staff overtime. Increased efficiency of an OR comes at a cost of patient convenience.

Cited by 127 publications

References 4 publications

Improving operating room start times in a community teaching hospital

Improving operating room start times in a community teaching hospital

Operating room planning and scheduling: A literature review

Case mix classification and a benchmark set for surgery scheduling

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