SynthesisJournal of medical systems2019
Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.
Synthesis in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study.BMC health services research · 2024Pooled it
- Artificial Intelligence in Operating Room Management.Journal of medical systems · 2024Pooled it
- Operating Room Performance Optimization Metrics: a Systematic Review.Journal of medical systems · 2023Pooled it
- Role of Intelligent Management Systems in Surgical Punctuality and Quality of Care.Computational intelligence and neuroscience · 2022Trial
- Comparison of Machine Learning Algorithms for Predicting Spine Surgery Duration.Medicina (Kaunas, Lithuania) · 2026Article
- Predicting Pediatric Urological Surgery Duration Through Multimodal Patient-Physician Feature Fusion: Deep Learning Framework Incorporating Clinical Text Embedding.JMIR medical informatics · 2026Article
- Impact of the Federated Data Platform's digital surgery scheduling system on elective theatre utilisation at an NHS Trust: an interrupted time series analysis.BMJ health & care informatics · 2026Article
- A Plan-Do-Study-Act Cycle to Enhance Operational Efficiency in a Newly Established Paediatric Cardiac Operating Room.Interdisciplinary cardiovascular and thoracic surgery · 2026Article
- Reframing workplace safety, wellbeing, and performance among operating room nurses in contemporary healthcare systems.Frontiers in public health · 2026Review
- Artificial Intelligence Adoption in Orthopedics: A Multicenter International Survey From Germany and Greece.Cureus · 2026Article
- Could artificial intelligence accelerate progress in ambulatory anesthesia?Journal of translational medicine · 2025Article
- Impact of digital surgery scheduling systems on the quality of preoperative care: a systematic review protocol.BMJ open · 2025Article
- Predictive Factors Aiding in the Estimation of Intraoperative Resources in Gastric Cancer Oncologic Surgery.Cancers · 2025Article
- AI integration in energy management: enhancing efficiency in Italian hospitals.Health economics review · 2025Article
- Health Care Social Robots in the Age of Generative AI: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding.JAMIA open · 2025Article
- Review
- Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Impact of a Task-Grabbing System for surgical technicians on operating room efficiency.Scientific reports · 2024Article
- Defining digital surgery: a SAGES white paper.Surgical endoscopy · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
31823034What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.