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

Bellini, Valentina; Guzzon, Marco; Bigliardi, Barbara; Mordonini, Monica; Filippelli, Serena; Bignami, Elena

doi:10.1007/s10916-019-1512-1

Cited by 60 publications

(55 citation statements)

References 36 publications

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“…RF is a classifier composed of an ensemble of decision trees for training and predicting, which is widely used in medical management [ 42 – 44 ]. It is known for the high predictive performance and ability to find complex interactions among features [ 41 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Zhang

Cui

Gong

et al. 2021

Journal of Healthcare Engineering

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This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.

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Section: Methodsmentioning

confidence: 99%

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Zhang

Cui

Gong

et al. 2021

Journal of Healthcare Engineering

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“…Unlike hybrid operating rooms that combine image guided interventional suites with traditional operating rooms [1] , AI operating rooms also use machine learning and deep learning to improve the workflow and resource utilization in the operating theatre, pre-operative area and post-anesthesia care unit [26] . Although the initial and most obvious benefit of operating room resource optimization is economic, increased economic resources translates into increased funds for more advanced AI platforms.…”

Section: Intra-operative Aids: Augmented Realitymentioning

confidence: 99%

What is Artificial Intelligence Surgery?

Gumbs¹,

Perretta²,

Dallemagne³

et al. 2021

AIS

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“…The problem of surgical room organization has become increasingly important in the last year; the current coronavirus disease pandemic challenged us to face a disruption of healthcare systems, with considerable consequences on surgery waiting lists and scheduling. In this context, it was hypothesized that ML models might have a substantial role in the optimization of operating rooms efficiency, allowing to save costs and maximize resources (59,60).…”

Section: Surgical Performance and Planningmentioning

confidence: 99%

Artificial intelligence in thoracic surgery: a narrative review

Valente¹,

Río²,

Bignami³

2021

J Thorac Dis

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The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background: Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods: We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion:Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decisionmaking process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice.

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

Cited by 60 publications

References 36 publications

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

What is Artificial Intelligence Surgery?

Artificial intelligence in thoracic surgery: a narrative review

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