Validation of Statistical Methods to Compare Cancellation Rates on the Day of Surgery

Dexter, Franklin; Marcon, Eric; Epstein, Richard H.; Ledolter, Johannes

doi:10.1213/01.ane.0000154536.34258.a8

Cited by 116 publications

(49 citation statements)

References 25 publications

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“…Because of the expensive equipment and highly trained staff, operating rooms are very expensive with the average cost of operating room time in the US being roughly $4000 per hour [18,26]. In addition, mismanaged operating rooms lead to cancelled surgeries with each cancellation decreasing revenue by roughly $1500 per hour [11,12,19]. The management process is complicated and must account for heterogeneity of patient needs, uncertainty in patient recovery times, and uncertainty in surgical durations.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…The master surgical schedule influences which surgeons work on which days and thereby results in the presence of prolonged tracheal extubation times to be clustered by time of day and day of the week. 35 Consequently, the statistics were calculated using the method of batch means, [34][35][36][37] specifically, by pooling the proportions among four-week periods. For each of the two proportions (i.e., one per hypothesis), Visual Basic for Applications in Excel 2010 was used to perform the Freeman-Tukey transformation for every four-week period (Microsoft, Redmond, WA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…The mean and the lower confidence limit were calculated using Student's onesample t test, and then the inverse of the Freeman-Tukey transformation was performed analytically. 37,38 The upper (Hypothesis #1) and lower (Hypothesis #2) 95% confidence limits for the corresponding percentages were calculated using the n = 39 four-week periods and are described by the phrases, ''at least/most''. The P values testing the hypotheses were Bonferroni adjusted for the two comparisons.…”

Section: Methodsmentioning

confidence: 99%

Cohort study of cases with prolonged tracheal extubation times to examine the relationship with duration of workday

Epstein

Dexter

Brull

2013

Can J Anesth/J Can Anesth

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Purpose The economics of the use of an anesthetic drug or device that produces benefit through reduction in operating room (OR) time depends on the day of the week and the total hours of surgical cases in the OR in which they are performed. Principally, this has to do with different durations of the regularly scheduled workday in the ORs within and among hospitals. We tested hypotheses relevant to the economic benefit of avoiding prolonged tracheal extubation times. Methods Observational data were obtained from a multiple-specialty academic tertiary hospital that uses an anesthesia information management system. Prolonged tracheal extubation times were considered those with tracheal extubations occurring 15 min or more after the end of surgery. The assessment of prolonged tracheal extubation times was limited to cases for which the patient's trachea was intubated and extubated while physically in the OR. Percentages were calculated for each of n = 39 four-week periods. Results are reported as mean (standard error of the mean) of these percentages, and the phrases ''at most/least'' are used to refer to the corresponding 95% confidence limits.Results At most, 6.1% [mean 5.5 (0.3)%] of the prolonged tracheal extubation times were attributable to cases that did not end during regular workdays from 7:00 AM-10:59 PM. At least 55.6% of prolonged tracheal extubation times occurred during cases on regular workdays and in an OR with more than eight hours of cases and turnovers [mean 57.0 (0.9)%; P \ 0.0001]. This percentage was 23.8 (0.8)% larger than for all other cases. Conclusions In the absence of an accurate facilityspecific cost analysis, prolonged tracheal extubation times should not be treated as fixed costs but as resulting in proportionally increased OR variable costs. RésuméObjectif En anesthésie, les économies résultant de l'utilisation d'un médicament ou d'un dispositif qui entraîne des avantages en réduisant le temps en salle d'opération (SO) dépendent du jour de la semaine et du nombre total d'heures de cas chirurgicaux réalisés dans cette salle. Ces économies ont surtout à voir avec les différences de durée d'une journée de travail normale planifiée dans les SO à l'intérieur de chaque hô pital et entre les hô pitaux. Nous avons testé des hypothèses pertinentes aux avantages économiques liés à la prévention d'un délai d'extubation trachéale prolongés. Méthode Nous avons obtenu des données observationnelles d'un hô pital tertiaire, universitaire et pluri-spécialisé qui se sert d'un système de gestion de l'information en anesthésie. Nous avons défini qu'il y avait

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“…There is also the potential for revenue losses incurred by the hospital. 80,81 An acute symptomatic infection, one that presents as an intraoral abscess for example, An acute symptomatic infection, one that presents as an intraoral abscess for example, should be the primary focus of any preoperative dental evaluation.…”

Section: Optimizing Dental Health Before Surgery: Risks and Benefitsmentioning

confidence: 99%