The accuracy of surgery time estimations

Larsson, Agneta

doi:10.1080/09537287.2012.666897

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…Travis et al ( 2014) find heterogeneity in the degree of bias in the prediction and show that the sign and magnitude of the bias depends on the surgeon and the procedure. Larsson (2013) reports that while historical averages are more accurate than the surgeon's prediction in general, surgeons are better at identifying long cases.…”

Section: Operating Room Management and Surgical Schedulingmentioning

confidence: 99%

“…Third, "there is at present no conclusive view on whether it is necessary to include the surgeons' subjective knowledge" to predict the surgery duration (Larsson 2013). Studies have shown that expert knowledge may be useful when the problem has structure, the performance can be evaluated via high-quality rapid feedback, and the expert has experienced many repetitions (Kahneman and Klein 2009).…”

Section: Introductionmentioning

confidence: 99%

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Is Expert Input Valuable? The Case of Predicting Surgery Duration

Ibrahim¹,

Kim²

2019

Seoul Journal of Business

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Most data-driven decision support tools do not include input from people. We study whether and how to incorporate physician input into such tools, in an empirical setting of predicting the surgery duration. Using data from a hospital, we evaluate and compare the performances of three families of models: models with physician forecasts, purely data-based models, and models that combine physician forecasts and data. We find that combined models perform the best, which suggests that physician forecasts have valuable information above and beyond what is captured by data. We also find that applying simple corrections to physician forecasts performs comparably well.

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Section: Operating Room Management and Surgical Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is Expert Input Valuable? The Case of Predicting Surgery Duration

Ibrahim¹,

Kim²

2019

Seoul Journal of Business

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“…Travis et al (2014) find heterogeneity in the degree of bias in the surgeon's prediction and show that the sign and magnitude of the bias depends on the type of the surgeon and the procedure. Larsson (2013) reports that while historical averages are more accurate than the surgeon's prediction in general, surgeons are better at identifying long cases. Wright et al (1996) and Roque et al (2015) describe that surgeons are generally more accurate than statistical models.…”

Section: Operating Room Management and Surgical Schedulingmentioning

confidence: 99%

Predicting Surgery Duration: Physician Input, Statistical Models, and Combined Models

Ibrahim

Kim

2018

SSRN Journal

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“…Traditional OR scheduling, based on the surgeon’s self-estimation of the time needed for the operation, has been shown to be insufficient for optimal OR utilization (Larsson, 2013; Laskin et al, 2013). More recently, electronic health record (EHR) systems and machine learning models have been used to improve OR scheduling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room

Elsaqa,

El Tayeb,

Yano

et al. 2024

Journal of Healthcare Management

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SUMMARY Goal: Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. Methods: Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5 months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30 min or ±20% of the scheduled duration if the scheduled time is ≥ or <150 min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. Principal Findings: The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p < .001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p < .001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p = .0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p = .0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p = .22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954 hr). Practical Applications: The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.

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The accuracy of surgery time estimations

Cited by 11 publications

References 20 publications

Is Expert Input Valuable? The Case of Predicting Surgery Duration

Is Expert Input Valuable? The Case of Predicting Surgery Duration

Predicting Surgery Duration: Physician Input, Statistical Models, and Combined Models

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room

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