The Implications of AI in Optimizing Operating Theatre Efficiency

Wiam El Jellouli *

Department of Surgery and Scientific Research, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.

Yousra Ouhammou

Department of Surgery and Scientific Research, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.

Mohammed El Gaabouiri

Department of Surgery and Scientific Research, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.

Mohamed Alioui

Department of Anesthesiology and Intensive Care, Faculty of Medicine and Pharmacy, Mohammed V Military Hospital, Mohammed V University, Rabat, Morocco.

Houda Nadir

Department of Surgery and Scientific Research, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco.

Mustapha Bensghir

Department of Anesthesiology and Intensive Care, Faculty of Medicine and Pharmacy, Mohammed V Military Hospital, Mohammed V University, Rabat, Morocco.

Khalil Abou Elalaa

Department of Anesthesiology and Intensive Care, Faculty of Medicine and Pharmacy, Mohammed V Military Hospital, Mohammed V University, Rabat, Morocco.

*Author to whom correspondence should be addressed.


Abstract

Operating theatre efficiency remains a crucial concern within health care systems, directly influencing the timeliness and effectiveness of surgical care. However, persistent challenges like surgical delays, suboptimal scheduling, and inefficient resource allocation persist. Artificial Intelligence (AI) has emerged as a promising avenue to address these challenges and optimize operating the atre efficiency. This article provides an in-depth exploration of the implications of AI in improving surgical punctuality, scheduling precision, and resource allocation.

Key components of AI-driven strategies encompass machine learning models, intelligent management systems, and optimization algorithms. Notably, recent research demonstrates that machine learning models exhib it remarkable accuracy in predicting surgical case durations, leading to improved surgical punctuality. Simultaneously, intelligent management systems play a pivotal role in facilitating streamlined surgical scheduling, patient flow management, and optimizing resource distribution. Furthermore, the application of optimization algorithms, including genetic algorithms, is instrumental in resolving intricate scheduling dilemmas and curtailing waiting times.

The integration of AI into efforts to enhance operating theatre efficiency heralds numerous benefits, including elevated patient care standards, reduced costs, and heightened operational efficiency. However, challenges pertaining to data quality, interpretability, and organizational adaptability need rigorous addressing. Ethical and legal considerations, encompassing patient privacy, data security, and algorithm icbias, also prominently emerge with AI's use in healthcare. To harness AI's full potential, future advancements should focus on real-time data analytics, predictive modeling, and autonomous decision-making. This article's finding sunder score AI's transformative impact on optimizing operating theatre efficiency, while emphasizing the need for well-defined ethical guidelines and comprehensive regulations to ensure responsible implementation.

Keywords: Operating theatre, efficiency, artificial intelligence (AI), predictive modeling, intelligent management systems


How to Cite

Jellouli , W. E., Ouhammou , Y., Gaabouiri , M. E., Alioui , M., Nadir , H., Bensghir , M., & Elalaa , K. A. (2023). The Implications of AI in Optimizing Operating Theatre Efficiency. Asian Journal of Research in Surgery, 6(2), 193–200. Retrieved from https://journalajrs.com/index.php/AJRS/article/view/154


References

Fairley M, Scheinker D, Brandeau ML. Improving the efficienc yof the operating room environment with an optimization and machinelearningmodel. Health Care Manag Sci. déc 2019;22(4):756‑67.

Schoenfelder J, Kohl S, Glaser M, McRae S, Brunner JO, Koperna T. Simulation-basedevaluationofoperating roommanagementpolicies. BMC Health Serv Res. 24 mars 2021;21(1):271.

Lin YK, Yen CH. Genetic AlgorithmforSolvingtheNo-WaitThree-Stage Surgery Scheduling Problem. Healthc Basel Switz. 2 mars 2023;11(5): 739.

Can machine learning optimize the efficiency of theo perating room in theeraof COVID-19? - PubMed [Internet]; [cité 17 juill 2023]. Available:https://pubmed.ncbi.nlm.nih.gov/33180692/

Li G, Huang S. Role of Intelligent Management Systems in SurgicalPunctuality and Quality of Care. Chen D, éditeur. Comput Intell Neurosci. 11 oct2022;2022:1‑6.

Eshghali M, Kannan D, Salmanzadeh-Meydani N, Esmaieeli Sikaroudi AM. Machine learning based integrated scheduling and reschedulingforelective and emergencypatients in theoperatingtheatre. Ann Oper Res. 19 janv 2023;1‑24.

Tuwatananurak JP, Zadeh S, Xu X, Vacanti JA, Fulton WR, Ehrenfeld JM, et al. Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study. J Med Syst. 17 jan 2019; 43(3):44.

Dexter F, Epstein RH. Absence of a Comprehensive Literature Search Protocol in a Systematic Review of Published Studies Describing Operating Room Optimization. J Med Syst. 13 mars 2023; 47(1):35.

Defining difficultlaryngos copy findings by using multiple parameters: A machine learning approach – Science Direct [Internet]; [cité 17 juill 2023]. Available:https://www.sciencedirect.com/science/article/pii/S1110184917300740

Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E. ArtificialIntelligence: A New Tool in Operating Room Management. RoleofMachine Learning Models in Operating Room Optimization. J Med Syst. 10 déc 2019;44(1):20.

L L, F Z, Y Y, R G, M F, J X. Machine learning for identification of surgerieswith high risks of cancellation. HealthInformatics J [Internet]. mars 2020 [cité 17 juill 2023];26(1).

Available:https://pubmed.ncbi.nlm.nih.gov/30518275/

Sobrie O, Lazouni MEA, Mahmoudi S, Mousseau V, Pirlot M. A new decision support model for preanesthetic evaluation. Comput Methods Programs Biomed. sept 2016;133:183‑93.

Mehmet N, Rahimi A, Aghaie L. Economic impact of surgery cancellation in a general hospital, Iran. Ethiop J Health Dev. 1 janv2016;30.

Klemt C, Cohen-Levy WB, Robinson MG, Burns JC, Alpaugh K, Yeo I, et al. Can machine learning models predict failure of revision total hip arthroplasty? Arch Orthop Trauma Surg. juin 2023;143(6):2805‑12.

Operating Room Performance Optimization Metrics: A Systematic Review - PubMed [Internet]; [cité 17 juill 2023]. Available:https://pubmed.ncbi.nlm.nih.gov/36738376/

Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 4 avr2019;380(14):1347‑58.

B Z, Rs W, Rd U, Ra G. A Machine Learning Approach to Predicting Case Duration for Robot-AssistedSurgery. J Med Syst [Internet]. 1 mai 2019 [cité 17 juill 2023];43(2). Available:https://pubmed.ncbi.nlm.nih.gov/30612192/

Basson MD. Better Quality Metrics Could Illuminate Quality-Efficiency Tradeoffs in Operating Room Management. J Investig Surg Off J Acad Surg Res. mars 2020;33(3):271‑2.

Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine:apractical introduction. BMC Med Res Methodol. 19 mars 2019;19(1):64.

Wu HL, Chang WK, Hu KH, Langford RM, Tsou MY, Chang KY. A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction. PloS One. 2015;10(8): e0134838.

Stepaniak PS, Heij C, Mannaerts GHH, de Quelerij M, de Vries G. Modeling procedure and surgical times for current proceduralterminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: Amulticenterstudy. Anesth Analg. oct 2009;109(4):1232‑45.

Yeo I, Klemt C, Melnic CM, Pattavina MH, De Oliveira BMC, Kwon YM. Predicting surgical operative time in primary total kneearthropla styutilizing machine learningmodels. Arch Orthop Trauma Surg. juin 2023;143(6):3299‑307.

Feng Z, Bhat RR, Yuan X, Freeman D, Baslanti T, Bihorac A, et al. Intelligent Perioperative System: Towards Real-time Big Data Analytics in Surgery Risk Assessment. DASC-PICom-Data Com-Cyber Sci Tech 2017 2017 IEEE 15th Int Conf Dependable Auton Secure Comput 2017 IEEE 15th Int Conf Pervasive Intell Comput 2017 IEEE 3rd Int. nov 2017;2017:1254‑9.

Suzuki S, Yamashita T, Sakama T, Arita T, Yagi N, Otsuka T, et al. Comparison of riskmodels for mortality and cardiovasculareventsbetween machine learning and conventional logistic regressiona nalysis. PLoS ONE. 9 sept 2019;14(9):e0221911.

Lia H, Hammond Mobilio M, Rudzicz F, Moulton CA. Contextualizing the tone of the operating room in practice: Drawing on the literature to connect the dots. Front Psychol. 2023;14: 1167098.

Lee CH, Yoon HJ. Medical big data: promise and challenges. Kidney Res Clin Pract. 31 mars 2017;36(1):3‑11.

Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. juin 2019;110:12‑22.

Bignami E, Bellini V. Do We Need Specific Certification to Use Anesthesia Information Management Systems? Anesth Analg. févr 2019;128(2):e30‑1.

Bellini V, Maestroni U, Bignami E. Surgical Block Scheduling Controlled by a Machine: Reality or Science Fiction? J Med Syst. 28 Janv 2019;43(3):54.