Technological Trend Analysis for Surgical Operation Duration Estimation

Ziya Karakaya; Bahadır Tatar

doi:10.55549/epstem.1368277

Conference Paper

Technological Trend Analysis for Surgical Operation Duration Estimation

Year 2023, Volume: 23, 349 - 360, 30.09.2023

Ziya Karakaya Bahadır Tatar

https://doi.org/10.55549/epstem.1368277

Abstract

Surgical procedures are complex in nature and operative time is subject to variability influenced by many factors. Accurate estimation of the surgical operation duration not only helps to maximize Operation rooms’ efficiency, but also helps to optimize hospital resources which are a crucial factor in planning surgical procedures. In this regard, Al techniques such as machine learning and deep learning promise to significantly improve the duration estimation by identifying hidden factors and make more accurate prediction. They achieve this success by identifying latent factors which are generally hard to be explored by human intelligence. Eventually, accuracy in time estimation added to a good scheduling optimization leads to make more efficient utilization of hospital resources by better aligning Operation Room, relevant equipment, and human resources. This study addresses the recent trends in research on surgical operations duration estimation, considering the relevant factors.

Keywords

Surgical procedure duration , Time estimation , Operating room scheduling , Operation room optimization , Scheduling

References

Abbas, A., Mosseri, J., Lex, J. R., Toor, J., Ravi, B., Khalil, E. B., & Whyne, C. (2022). Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty. International Journal of Medical Informatics, 158. 104670.
Bartek, M. A., Saxena, R. C., Solomon, S., Fong, C. T., Behara, L. D., Venigandla, R., Velagapudi, K., Lang, J. D., &Nair, B. G. (2019). Improving operating room efficiency: Machine learning approach to predict case-time duration. Journal of the American College of Surgeons, 229(4), 346–354.
Bodenstedt, S., Wagner, M., Mündermann, L., Kenngott, H., Müller-Stich, B., Breucha, M., Mees, S. T., Weitz, J., & Speidel, S. (2019). Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data. International Journal of Computer Assisted Radiology and Surgery, 14, 1089-1095.

Year 2023, Volume: 23, 349 - 360, 30.09.2023

Ziya Karakaya Bahadır Tatar

https://doi.org/10.55549/epstem.1368277

Abstract

References

Abbas, A., Mosseri, J., Lex, J. R., Toor, J., Ravi, B., Khalil, E. B., & Whyne, C. (2022). Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty. International Journal of Medical Informatics, 158. 104670.
Bartek, M. A., Saxena, R. C., Solomon, S., Fong, C. T., Behara, L. D., Venigandla, R., Velagapudi, K., Lang, J. D., &Nair, B. G. (2019). Improving operating room efficiency: Machine learning approach to predict case-time duration. Journal of the American College of Surgeons, 229(4), 346–354.
Bodenstedt, S., Wagner, M., Mündermann, L., Kenngott, H., Müller-Stich, B., Breucha, M., Mees, S. T., Weitz, J., & Speidel, S. (2019). Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data. International Journal of Computer Assisted Radiology and Surgery, 14, 1089-1095.

There are 3 citations in total.

Details

Primary Language	English
Subjects	Environmental and Sustainable Processes
Journal Section	Articles
Authors	Ziya Karakaya Bahadır Tatar
Early Pub Date	September 29, 2023
Publication Date	September 30, 2023
Published in Issue	Year 2023 Volume: 23

Cite

APA	Karakaya, Z., & Tatar, B. (2023). Technological Trend Analysis for Surgical Operation Duration Estimation. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 349-360. https://doi.org/10.55549/epstem.1368277