Surgical Procedure Times Are Well Modeled by the Lognormal Distribution

Strum, DP; May, Jerrold H.; Vargas, LG

doi:10.1097/00000539-199802001-00047

Cited by 24 publications

(4 citation statements)

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“…The first direction is fitting the data to a known distribution, such as normal 22,23 or log-normal. [24][25][26][27] While studies show that such models can produce accurate predictions and increase operating room efficiency, 27 they did not offer insight into the factors that may impact the predictions. Therefore, we chose to focus on the second direction, which uses statistical and machine learning models to identify important features and produce predictions.…”

Section: Discussionmentioning

confidence: 99%

Determining pre-procedure fasting alert time using procedural and scheduling data

Zheng,

Beck,

Mafeld

et al. 2024

Health Informatics J

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Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6 h and not drink clear fluids for 2 h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08 h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food/non-clear fluids to 7.7 h (IQR 2) and clear liquids fasting to 3.7 h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system.

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Section: Discussionmentioning

confidence: 99%

Determining pre-procedure fasting alert time using procedural and scheduling data

Zheng,

Beck,

Mafeld

et al. 2024

Health Informatics J

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“…It is common to assume that surgical durations are lognormally distributed [6][7][8] . Here, it is shown that this assumption is questionable in approximately half of the specialties at a large Australian public hospital.…”

Section: Discussionmentioning

confidence: 99%

Analysis of uncertainty in the surgical department: durations, requests and cancellations

Spratt¹,

Kozan²,

Sinnott³

2019

Aust. Health Review

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Analytical techniques are being implemented with increasing frequency to improve the management of surgical departments and to ensure that decisions are well informed. Often these analytical techniques rely on the validity of underlying statistical assumptions, including those around choice of distribution when modelling uncertainty. The aim of the present study was to determine a set of suitable statistical distributions and provide recommendations to assist hospital planning staff, based on three full years of historical data. Statistical analysis was performed to determine the most appropriate distributions and models in a variety of surgical contexts. Data from 2013 to 2015 were collected from the surgical department at a large Australian public hospital. A log-normal distribution approximation of the total duration of surgeries in an operating room is appropriate when considering probability of overtime. Surgical requests can be modelled as a Poisson process with rate dependent on urgency and day of the week. Individual cancellations could be modelled as Bernoulli trials, with the probability of patient-, staff- and resource-based cancellations provided herein. The analysis presented herein can be used to ensure that assumptions surrounding planning and scheduling in the surgical department are valid. Understanding the stochasticity in the surgical department may result in the implementation of more realistic decision models. Many surgical departments rely on crude estimates and general intuition to predict surgical duration, surgical requests (both elective and non-elective) and cancellations. This paper describes how statistical analysis can be performed to validate common assumptions surrounding surgical uncertainty. The paper also provides a set of recommended distributions and associated parameters that can be used to model uncertainty in a large public hospital's surgical department. The insights on surgical uncertainty provided here will prove valuable for administrative staff who want to incorporate uncertainty in their surgical planning and scheduling decisions.

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“…Therefore, we used surgery time distributions estimated through data collected by Torres (2007) for the HUCFF. Based on the recommendation by Strum, May, and Vargas (1998), we used log-normal statistical distributions. These distributions were compared to the mentioned data and passed Kolmogorov-Smirnov tests.…”

Section: Simulation Modellingmentioning

confidence: 99%

Integrating soft and hard operational research to improve surgical centre management at a university hospital

Pessôa¹,

Lins

Silva

et al. 2015

European Journal of Operational Research

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a b s t r a c tThe aim of this work is to present the practical applications of an integrated use of soft and hard methodologies applied in a case study of the Surgical Centre of the University Hospital Clementino Fraga Filho, where the low volume of surgeries is of major concern. The proposed approach is particularly appropriate in situations where there is limited time, financial resources, and institutional cooperation. Cognitive maps were used to elicit the perspectives of health professionals, which supported simulation experiments and guided the model's execution. Human-resource, patient-related, room-schedule, material, and structural constraints were found to affect the number of surgeries performed. The major contribution of this paper is the proposal of a multi-methodological approach with a committed focus on problem solving that incorporates specialists' views in simulation experiments; these specialists' collaborative work highlights actions that can lead to the resolution (or improvement) of real-world problems.

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Surgical Procedure Times Are Well Modeled by the Lognormal Distribution

Cited by 24 publications

References 1 publication

Determining pre-procedure fasting alert time using procedural and scheduling data

Determining pre-procedure fasting alert time using procedural and scheduling data

Analysis of uncertainty in the surgical department: durations, requests and cancellations

Integrating soft and hard operational research to improve surgical centre management at a university hospital

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