Skip to main content
Erschienen in: Journal of Medical Systems 1/2020

01.01.2020 | Systems-Level Quality Improvement

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization

verfasst von: Valentina Bellini, Marco Guzzon, Barbara Bigliardi, Monica Mordonini, Serena Filippelli, Elena Bignami

Erschienen in: Journal of Medical Systems | Ausgabe 1/2020

Einloggen, um Zugang zu erhalten

Abstract

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.
Literatur
12.
Zurück zum Zitat Shahabikargar, Z., Khanna, S., Sattar, A., and Lind, J., Improved prediction of procedure duration for elective surgery. Stud Health Technol Inform. 239:133–138, 2017.PubMed Shahabikargar, Z., Khanna, S., Sattar, A., and Lind, J., Improved prediction of procedure duration for elective surgery. Stud Health Technol Inform. 239:133–138, 2017.PubMed
16.
Zurück zum Zitat Moccia, S., Mattos, L. S., Patrini, I., Ruperti, M., Poté, N., Dondero, F., Cauchy, F., Sepulveda, A., Soubrane, O., De Momi, E., Diaspro, A., and Cesaretti, M., Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int J Comput Assist Radiol Surg. 13(9):1357–1367, 2018 Sep. https://doi.org/10.1007/s11548-018-1787-6 Epub 2018 May 23.CrossRef Moccia, S., Mattos, L. S., Patrini, I., Ruperti, M., Poté, N., Dondero, F., Cauchy, F., Sepulveda, A., Soubrane, O., De Momi, E., Diaspro, A., and Cesaretti, M., Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int J Comput Assist Radiol Surg. 13(9):1357–1367, 2018 Sep. https://​doi.​org/​10.​1007/​s11548-018-1787-6 Epub 2018 May 23.CrossRef
19.
20.
Zurück zum Zitat Albala, D., Manak, M. S., Varsanik, J. S., Rashid, H. H., Mouraviev, V., Zappala, S. M., Ette, E., Kella, N., Rieger-Christ, K. M., Sant, G. R., and Chander, A. C., Clinical proof-of-concept of a novel platform utilizing biopsy-derived live single cells, phenotypic biomarkers, and machine learning toward a precision risk stratification test for prostate Cancer grade groups 1 and 2 (Gleason 3 + 3 and 3 + 4). Urology. 124:198–206, 2019 Feb. https://doi.org/10.1016/j.urology.2018.09.032.CrossRefPubMed Albala, D., Manak, M. S., Varsanik, J. S., Rashid, H. H., Mouraviev, V., Zappala, S. M., Ette, E., Kella, N., Rieger-Christ, K. M., Sant, G. R., and Chander, A. C., Clinical proof-of-concept of a novel platform utilizing biopsy-derived live single cells, phenotypic biomarkers, and machine learning toward a precision risk stratification test for prostate Cancer grade groups 1 and 2 (Gleason 3 + 3 and 3 + 4). Urology. 124:198–206, 2019 Feb. https://​doi.​org/​10.​1016/​j.​urology.​2018.​09.​032.CrossRefPubMed
23.
Zurück zum Zitat Moustafa MA, El-Metainy S, Mahar K, Mahmoud Abdel-magied E. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach, Egyptian Journal of Anaesthesia, 33:2, 153–158,CrossRef Moustafa MA, El-Metainy S, Mahar K, Mahmoud Abdel-magied E. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach, Egyptian Journal of Anaesthesia, 33:2, 153–158,CrossRef
24.
Zurück zum Zitat Hadjerci, O., Hafiane, A., Morette, N., Novales, C., Vieyres, P., and Delbos, A., Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications: An International Journal 61(C):64–77, November 2016.CrossRef Hadjerci, O., Hafiane, A., Morette, N., Novales, C., Vieyres, P., and Delbos, A., Assistive system based on nerve detection and needle navigation in ultrasound images for regional anesthesia. Expert Systems with Applications: An International Journal 61(C):64–77, November 2016.CrossRef
26.
Zurück zum Zitat Sahu, M., Moerman, D., Mewes, P., Mountney, P., Rose, G., Instrument state recognition and tracking for effective control of robotized laparoscopic systems. International Journal of Mechanical Engineering and Robotics Research Vol. 5, No. 1, January 2016 Sahu, M., Moerman, D., Mewes, P., Mountney, P., Rose, G., Instrument state recognition and tracking for effective control of robotized laparoscopic systems. International Journal of Mechanical Engineering and Robotics Research Vol. 5, No. 1, January 2016
30.
Zurück zum Zitat Maimaiti, N., Rahimi, A., and Aghaie, L. A., Economic impact of surgery cancellation in a general hospital, Iran. Ethiop J Health Dev 30:92–95, 2017. Maimaiti, N., Rahimi, A., and Aghaie, L. A., Economic impact of surgery cancellation in a general hospital, Iran. Ethiop J Health Dev 30:92–95, 2017.
33.
Zurück zum Zitat Stepaniak, P. S., Heij, C., Mannaerts, G. H., de Quelerij, M., and de Vries, G., Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: A multicenter study. Anesth Analg. 109(4):1232–1245, 2009 Oct. https://doi.org/10.1213/ANE.0b013e3181b5de07.CrossRefPubMed Stepaniak, P. S., Heij, C., Mannaerts, G. H., de Quelerij, M., and de Vries, G., Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: A multicenter study. Anesth Analg. 109(4):1232–1245, 2009 Oct. https://​doi.​org/​10.​1213/​ANE.​0b013e3181b5de07​.CrossRefPubMed
Metadaten
Titel
Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization
verfasst von
Valentina Bellini
Marco Guzzon
Barbara Bigliardi
Monica Mordonini
Serena Filippelli
Elena Bignami
Publikationsdatum
01.01.2020
Verlag
Springer US
Erschienen in
Journal of Medical Systems / Ausgabe 1/2020
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-019-1512-1

Weitere Artikel der Ausgabe 1/2020

Journal of Medical Systems 1/2020 Zur Ausgabe