Artificial Intelligence in Operating Room Management.
Artificial intelligence
Machine learning
Management
Operating room
Perioperative
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
14 Feb 2024
14 Feb 2024
Historique:
received:
29
11
2023
accepted:
05
02
2024
medline:
14
2
2024
pubmed:
14
2
2024
entrez:
14
2
2024
Statut:
epublish
Résumé
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
Identifiants
pubmed: 38353755
doi: 10.1007/s10916-024-02038-2
pii: 10.1007/s10916-024-02038-2
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
19Informations de copyright
© 2024. The Author(s).
Références
Lee DJ, Ding J, Guzzo TJ (2019) Improving Operating Room Efficiency. Curr Urol Rep 20(6):28. doi: https://doi.org/10.1007/s11934-019-0895-3 .
doi: 10.1007/s11934-019-0895-3
pubmed: 30989344
Birkhoff DC, van Dalen ASHM, Schijven MP (2021) A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 28(5):611–619. doi: https://doi.org/10.1177/1553350621996961 .
doi: 10.1177/1553350621996961
pubmed: 33625307
pmcid: 8450995
Snyder M, Zhou W (2019) Big data and health. Lancet Digit Health 1(6):e252-e254. doi: https://doi.org/10.1016/S2589-7500(19)30109-8 .
doi: 10.1016/S2589-7500(19)30109-8
pubmed: 33323249
Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E (2019) Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization. J Med Syst 44(1):20. doi: https://doi.org/10.1007/s10916-019-1512-1 .
doi: 10.1007/s10916-019-1512-1
pubmed: 31823034
Bellini V, Cascella M, Cutugno F, Russo M, Lanza R, Compagnone C, Bignami EG (2022) Understanding basic principles of Artificial Intelligence: a practical guide for intensivists. Acta Biomed 93(5):e2022297. doi: https://doi.org/10.23750/abm.v93i5.13626 .
doi: 10.23750/abm.v93i5.13626
pubmed: 36300214
pmcid: 9686179
Smith TG, Norasi H, Herbst KM, Kendrick ML, Curry TB, Grantcharov TP, Palter VN, Hallbeck MS, Cleary SP (2022) Creating a Practical Transformational Change Management Model for Novel Artificial Intelligence-Enabled Technology Implementation in the Operating Room. Mayo Clin Proc Innov Qual Outcomes 6(6):584–596. doi: https://doi.org/10.1016/j.mayocpiqo.2022.09.004 .
doi: 10.1016/j.mayocpiqo.2022.09.004
pubmed: 36324987
pmcid: 9618851
Mi D, Li Y, Zhang K, Huang C, Shan W, Zhang J (2023) Exploring intelligent hospital management mode based on artificial intelligence. Front Public Health 11:1182329. doi: https://doi.org/10.3389/fpubh.2023.1182329 .
doi: 10.3389/fpubh.2023.1182329
pubmed: 37645708
pmcid: 10461087
Bartek MA, Saxena RC, Solomon S, Fong CT, Behara LD, Venigandla R, Velagapudi K, Lang JD, Nair BG (2019) Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration. J Am Coll Surg 229(4):346–354.e3. doi: https://doi.org/10.1016/j.jamcollsurg.2019.05.029 .
doi: 10.1016/j.jamcollsurg.2019.05.029
pubmed: 31310851
pmcid: 7077507
Martinez O, Martinez C, Parra CA, Rugeles S, Suarez DR (2021) Machine learning for surgical time prediction. Comput Methods Programs Biomed 208:106220. doi: https://doi.org/10.1016/j.cmpb.2021.106220 .
doi: 10.1016/j.cmpb.2021.106220
pubmed: 34161848
Jiao Y, Xue B, Lu C, Avidan MS, Kannampallil T (2022) Continuous real-time prediction of surgical case duration using a modular artificial neural network. Br J Anaesth 128(5):829–837. doi: https://doi.org/10.1016/j.bja.2021.12.039 .
doi: 10.1016/j.bja.2021.12.039
pubmed: 35090725
pmcid: 9074795
Hassanzadeh H, Boyle J, Khanna S, Biki B, Syed F (2022) Daily surgery caseload prediction: towards improving operating theatre efficiency. BMC Med Inform Decis Mak 22(1):151. doi: https://doi.org/10.1186/s12911-022-01893-8 .
doi: 10.1186/s12911-022-01893-8
pubmed: 35672729
pmcid: 9172609
Abbou B, Tal O, Frenkel G, Rubin R, Rappoport N (2022) Optimizing operation room utilization—a prediction model. Big Data Cogn Comput 6(3):76. doi: https://doi.org/10.3390/bdcc6030076 .
doi: 10.3390/bdcc6030076
Lam SSW, Zaribafzadeh H, Ang BY, Webster W, Buckland D, Mantyh C, Tan HK (2022) Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study. Healthcare (Basel) 10(7):1191. doi: https://doi.org/10.3390/healthcare10071191 .
doi: 10.3390/healthcare10071191
pubmed: 35885718
Gabriel RA, Harjai B, Simpson S, Goldhaber N, Curran BP, Waterman RS (2022) Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center. Anesth Analg 135(1):159–169. doi: https://doi.org/10.1213/ANE.0000000000006015 .
doi: 10.1213/ANE.0000000000006015
pubmed: 35389380
pmcid: 9172889
Huang L, Chen X, Liu W, Shih PC, Bao J (2022) Automatic Surgery and Anesthesia Emergence Duration Prediction Using Artificial Neural Networks. J Healthc Eng 2022:2921775. doi: https://doi.org/10.1155/2022/2921775 .
doi: 10.1155/2022/2921775
pubmed: 35463687
pmcid: 9023179
Chu J, Hsieh C, Shih Y, Wu C, Singaravelan A, Hung Lun-Ping, Hsu Jia-Lien (2022) Operating room usage time estimation with machine learning models. Healthcare (Basel) 10(8):1518. doi: https://doi.org/10.3390/healthcare10081518 .
doi: 10.3390/healthcare10081518
pubmed: 36011177
Gabriel RA, Harjai B, Simpson S, Du AL, Tully JL, George O, Waterman R (2023) An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation. JMIR Perioper Med 6:e39650. doi: https://doi.org/10.2196/39650 .
doi: 10.2196/39650
pubmed: 36701181
pmcid: 9912154
Eshghali M, Kannan D, Salmanzadeh-Meydani N, Esmaieeli Sikaroudi AM (2023) Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre. Ann Oper Res 1–24. doi: https://doi.org/10.1007/s10479-023-05168-x .
Miller LE, Goedicke W, Crowson MG, Rathi VK, Naunheim MR, Agarwala AV (2023) Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology. Otolaryngol Head Neck Surg 168(2):241–247. doi: https://doi.org/10.1177/01945998221076480 .
doi: 10.1177/01945998221076480
pubmed: 35133897
Zhong W, Yao PY, Boppana SH, Pacheco FV, Alexander BS, Simpson S, Gabriel RA (2023) Improving case duration accuracy of orthopedic surgery using bidirectional encoder representations from Transformers (BERT) on Radiology Reports. J Clin Monit Comput. doi: https://doi.org/10.1007/s10877-023-01070-w
doi: 10.1007/s10877-023-01070-w
Adams T, O’Sullivan M, Walker C (2023) Surgical procedure prediction using medical ontological information. Comput Methods Programs Biomed 235:107541. doi: https://doi.org/10.1016/j.cmpb.2023.107541 .
doi: 10.1016/j.cmpb.2023.107541
pubmed: 37068449
Yeo I, Klemt C, Melnic CM, Pattavina MH, De Oliveira BMC, Kwon YM (2023) Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models. Arch Orthop Trauma Surg 143(6):3299–3307. doi: https://doi.org/10.1007/s00402-022-04588-x .
doi: 10.1007/s00402-022-04588-x
pubmed: 35994094
Strömblad CT, Baxter-King RG, Meisami A, Yee SJ, Levine MR, Ostrovsky A, Stein D, Iasonos A, Weiser MR, Garcia-Aguilar J, Abu-Rustum NR, Wilson RS (2021) Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial. JAMA Surg 156(4):315–321. doi: https://doi.org/10.1001/jamasurg.2020.6361
doi: 10.1001/jamasurg.2020.6361
pubmed: 33502448
pmcid: 7841577
Rozario N, Rozario D (2020) Can machine learning optimize the efficiency of the operating room in the era of COVID-19? Can J Surg 63(6):E527-E529. doi: https://doi.org/10.1503/cjs.016520 .
doi: 10.1503/cjs.016520
pubmed: 33180692
pmcid: 7747850
Schulz EB, Phillips F, Waterbright S (2020) Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists. Br J Anaesth 125(6):1079–1087. doi: https://doi.org/10.1016/j.bja.2020.06.068 .
doi: 10.1016/j.bja.2020.06.068
pubmed: 32863015
Cao B, Li L, Su X, Zeng J, Guo W (2021) Development and validation of a nomogram for determining patients requiring prolonged postanesthesia care unit length of stay after laparoscopic cholecystectomy. Ann Palliat Med 10(5):5128–5136. doi: https://doi.org/10.21037/apm-20-2182 .
doi: 10.21037/apm-20-2182
pubmed: 33977750
Tully JL, Zhong W, Simpson S, Curran BP, Macias AA, Waterman RS, Gabriel RA (2023) Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing. J Med Syst 47(1):71. doi: https://doi.org/10.1007/s10916-023-01966-9 .
doi: 10.1007/s10916-023-01966-9
pubmed: 37428267
pmcid: 10333394
Luo L, Zhang F, Yao Y, Gong R, Fu M, Xiao J (2020) Machine learning for identification of surgeries with high risks of cancellation. Health Informatics J 26(1):141–155. doi: https://doi.org/10.1177/1460458218813602 .
doi: 10.1177/1460458218813602
pubmed: 30518275
Zhang F, Cui X, Gong R, Zhang C, Liao Z (2021) Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations. J Healthc Eng 2021:6247652. doi: https://doi.org/10.1155/2021/6247652 .
doi: 10.1155/2021/6247652
pubmed: 33688420
pmcid: 7914093
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J (2021) Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open 4(3):e212240. doi: https://doi.org/10.1001/jamanetworkopen.2021.2240 .
doi: 10.1001/jamanetworkopen.2021.2240
pubmed: 33783520
pmcid: 8010590
Tuwatananurak JP, Zadeh S, Xu X, Vacanti JA, Fulton WR, Ehrenfeld JM, Urman RD (2019) Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study. J Med Syst 43(3):44. doi: https://doi.org/10.1007/s10916-019-1160-5 .
doi: 10.1007/s10916-019-1160-5
pubmed: 30656433
Sarker IH (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2(3):160. doi: https://doi.org/10.1007/s42979-021-00592-x .
doi: 10.1007/s42979-021-00592-x
pubmed: 33778771
pmcid: 7983091
Bing Xue, York Jiao, Thomas Kannampallil, Bradley Fritz, Christopher King, Joanna Abraham, Michael Avidan, and Chenyang Lu (2022) Perioperative Predictions with Interpretable Latent Representation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 4268–4278. https://doi.org/10.1145/3534678.3539190
Hovlid E, von Plessen C, Haug K, Aslaksen AB, Bukve O (2013) Patient experiences with interventions to reduce surgery cancellations: a qualitative study. BMC Surg 13:30. doi: https://doi.org/10.1186/1471-2482-13-30 .
doi: 10.1186/1471-2482-13-30
pubmed: 23924167
pmcid: 3750692