ArticleBMJ health & care informatics2026
Smart scheduling of arthroplasty surgery with machine learning and optimisation improves operating room utilisation.
Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
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
objectivesTotal hip and knee arthroplasty (THA and TKA, respectively) are effective procedures but are costly and resource intensive. As such, scheduling can have a significant impact on hospitals. The aim of this study was to evaluate the efficiency of a surgical schedule generated using machine learning (ML) and mathematical optimisation to current gold-standard scheduling practices.
methodsAll primary and revision TKA and THA cases between April 2012 and February 2022 from a single institution were included (n=15 267). Data was split by year into training/validation and test sets. Procedure-specific models were trained for predicting operative time. Integer linear programming optimisation of operating room (OR) utilisation using these ML predictions was compared with historic scheduling (using surgeon-surgery-specific rolling average values). Weekly simulated schedules were generated and compared based on OR underutilisation, overtime and cases completed.
resultsThe neural network models performed the best for all four procedures (median MSE: 594.6). This was a 7.1% improvement in 15 min buffer accuracy compared with rolling average times. The ML-predicted and optimised schedule reduced OR underutilisation (p<0.0001) and increased the number of cases (p<0.0001). OR underutilisation was reduced by 56.2% (13.3 minutes/day), while only increasing overtime by 17.2% (3.6 minutes/day), compared with the rolling mean. Overall, there was a 6.1% decrease (31 OR days) to complete the cases in the test set. DISCUSSION: ML-predicted operative times and optimisation has the potential to reduce idle OR time and improve patient throughput.
conclusionApproaches to surgical scheduling leveraging data maximises utilisation of existing resources.
Indexed as
Identifiers
What 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.