Modeling the Uncertainty of Surgical Procedure Times

Strum, David P.; May, Jerrold H.; Vargas, Luis G.

doi:10.1097/00000542-200004000-00035

Cited by 213 publications

(101 citation statements)

References 2 publications

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“…Data had been approved immediately after the surgical procedure by the surgery or anaesthesia nurse. The duration of surgical procedures, both emergency and elective, is assumed to be lognormal [3]. Table 1 shows the aggregate descriptive statistics of the central OR department of the Erasmus MC.…”

Section: Methodsmentioning

confidence: 99%

Closing Emergency Operating Rooms Improves Efficiency

et al. 2007

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Long waiting times for emergency operations increase a patient's risk of postoperative complications and morbidity. Reserving Operating Room (OR) capacity is a common technique to maximize the responsiveness of an OR in case of arrival of an emergency patient. This study determines the best way to reserve OR time for emergency surgery. In this study two approaches of reserving capacity were compared: (1) concentrating all reserved OR capacity in dedicated emergency ORs, and (2) evenly reserving capacity in all elective ORs. By using a discrete event simulation model the real situation was modelled. Main outcome measures were: (1) waiting time, (2) staff overtime, and (3) OR utilisation were evaluated for the two approaches. Results indicated that the policy of reserving capacity for emergency surgery in all elective ORs led to an improvement in waiting times for emergency surgery from 74 (±4.4) minutes to 8 (±0.5) min. Working in overtime was reduced by 20%, and overall OR utilisation can increase by around 3%. Emergency patients are operated upon more efficiently on elective Operating Rooms instead of a dedicated Emergency OR. The results of this study led to closing of the Emergency OR in the Erasmus MC (Rotterdam, The Netherlands).

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Section: Methodsmentioning

confidence: 99%

Closing Emergency Operating Rooms Improves Efficiency

et al. 2007

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“…For example, in [30] it was shown that lognormal distributions model the uncertainty of surgical procedure times better than normal distributions. A consequent line of research explores different methods for fitting lognormal distributions, e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving predictions of pediatric surgical durations with supervised learning

Master

Zhou

Miller

et al. 2017

Int J Data Sci Anal

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Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatric hospitals due to the extreme variation in pediatric patient populations. We pursue two supervised learning approaches: (1) We directly predict the surgical case durations using features derived from electronic medical records and from hospital operational information. For this regression problem, we propose a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. We evaluate several prediction models; some are automated (they do not require input from surgeons) while others are semi-automated (they do require input from surgeons). We see that many of our automated (2) We consider a classification problem in which each prediction provided by a surgeon is predicted to be correct, an overestimate, or an underestimate. This classification mechanism builds on the metric mentioned above and could potentially be useful for detecting human errors. Both supervised learning approaches give insights into the feature engineering process while creating the basis for decision support tools.

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“…Pre-surgery set-up time and post-surgery operating room cleaning time, if significant, is also added to the model (see Spangler et al [2004], Strum et al [2000], and May et al [2000]). …”

Section: Simulation Modelingmentioning

confidence: 99%

A mixed integer programming approach for allocating operating room capacity

Zhang

Murali

Dessouky

et al. 2009

Journal of the Operational Research Society

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We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (e.g., emergency over non-emergency patient) and clinical constraints (e.g., maximum number of hours allocated to each specialty, surgeon and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (e.g., surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (e.g., the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced.

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Modeling the Uncertainty of Surgical Procedure Times

Cited by 213 publications

References 2 publications

Closing Emergency Operating Rooms Improves Efficiency

Closing Emergency Operating Rooms Improves Efficiency

Improving predictions of pediatric surgical durations with supervised learning

A mixed integer programming approach for allocating operating room capacity

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