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Related Experiment Video

Updated: Apr 19, 2026

Surgical Robot-Assisted Transanal Specimen Extraction Radical Sigmoidectomy Without an Auxiliary Abdominal Incision

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Published on: June 13, 2025

A robust estimation model for surgery durations with temporal, operational, and surgery team effects.

Enis Kayış¹, Taghi T Khaniyev, Jaap Suermondt

¹Department of Industrial Engineering, Bogazici University, Istanbul, Turkey, enis.kayis@ozyegin.edu.tr.

Health Care Management Science

|December 16, 2014

Summary

Accurate surgery duration estimation is vital for operating room (OR) planning. This study introduces a statistical model using temporal, operational, and staff factors to improve OR time predictions and reduce errors.

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Area of Science:

Healthcare Operations Research
Medical Informatics
Surgical Planning

Background:

Effective operating room (OR) planning hinges on accurate surgery duration estimation.
Overestimation of surgery time leads to resource underutilization, while underestimation causes delays and increased patient waiting times.

Purpose of the Study:

To develop a statistical model that refines existing surgery duration estimates by incorporating additional factors.
To enhance the accuracy of OR time predictions, thereby optimizing resource allocation and patient flow.

Main Methods:

A statistical model was developed to adjust current surgery duration estimates.
The model incorporates temporal, operational, and staff-related factors, including team experience and frequency of collaboration.
The model's performance was evaluated on 8093 surgical cases.

Main Results:

The proposed model decreased the mean absolute deviation (MAD) of scheduled OR durations by 1.98 ± 0.28 minutes.
Significant reductions in large negative errors were observed (20.35 ± 0.74 minutes MAD decrease).
Combining the model with existing methods, such as Dexter et al., further improved accuracy, reducing MAD by an additional 1.02 ± 0.21 minutes.

Conclusions:

Temporal, operational, and medical staff experience factors significantly improve surgery duration estimates.
The developed statistical model offers a practical approach to enhance OR planning accuracy.
Integration with other prediction models presents a pathway for further optimization of surgical scheduling.