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Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice

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Abstract

Purpose

We aimed to improve OR efficiency using machine learning (ML) to find relevant metrics influencing surgery time success and team performance on efficiency to create a model which incorporated team, patient, and surgery-related factors.

Methods

From 2012 to 2020, five surgeons, 44 nurses, and 152 anesthesiologists participated in 1199 four joint days (4796 cases): 1461 THA, 1496 TKA, 652 HR, 242 UKA, and 945 others. Patients were 2461f:2335 m; age, 64.1; BMI, 29.93; and ASA, 2.45. Surgical Success was defined as completing four joints within an eight hour shift using one OR. Time data was recorded prospectively using Surgical Information Management Systems. Hospital records provided team, patient demographics, adverse events, and anesthetic. Data mining identified patterns and relationships in higher dimensions. Predictive analytics used ML ranking algorithm to identify important metrics and created decision tree models for benchmarks and success probability.

Results

Five variables predicted success: anaesthesia preparation time, surgical preparation time, time of procedure, anesthesia finish time, and type of joint replacement. The model determined success rate with accuracy of 72% and AUC = 0.72. Probability of success based on mean performance was 77–89% (mean-median) if APT 14–15 minutes, PT 68–70 minutes, AFT four to five minutes, and turnover 25–27 minutes. With the above benchmarks maintained, success rate was 59% if surgeon exceeded 71.5-minutes PT or 89% if 64-minutes procedure time or 66% when anesthesiologist spent 17–19.5 minutes on APT.

Conclusion

AI-ML predicted OR success without increasing resources. Benchmarks track OR performance, demonstrate effects of strategic changes, guide decisions, and provide teamwork improvement opportunities.

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Data availability

The data that support the findings of this study are available from the corresponding author, Dr. Paul Beaulé, upon reasonable request.

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Funding

This work was supported by the Ontario Ministry of Health.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, Canada

    Farid Al Zoubi

  2. Division of Orthopaedic Surgery, The Ottawa Hospital, Room W1640, 501 Smyth Road, Box 502, Ottawa, ON, K1H8L6, Canada

    Richard Gold, Cheryl Kreviazuk, Julia Brillinger & Paul E. Beaulé

  3. Faculty of Medicine, McGill University, Montreal, Canada

    Richard Gold

  4. School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada

    Stéphane Poitras

  5. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

    Cheryl Kreviazuk, Julia Brillinger & Paul E. Beaulé

  6. Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Canada

    Pascal Fallavollita

Authors
  1. Farid Al Zoubi
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  2. Richard Gold
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  3. Stéphane Poitras
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  4. Cheryl Kreviazuk
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  5. Julia Brillinger
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  6. Pascal Fallavollita
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  7. Paul E. Beaulé
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Contributions

Farid Al Zoubi: Methodology, Formal analysis, Investigation, Software, Validation, Visualization, Writing–original draft, Writing–review and editing

Richard Gold: Data curation, Investigation, Validation, Visualization, Writing–original draft, Writing–review and editing

Stéphane PoitrasMethodology, Resources, Writing–original draft, Writing–review and editing

Cheryl Kreviazuk: Data curation, Investigation, Writing–review and editing

Julia Brillinger: Data curation, Investigation, Writing–review and editing

Pascal FallavollitaConceptualization, Methodology, Project administration, Resources, Software, Writing–original draft, Writing–review and editing

Paul Beaulé: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing–original draft, Writing–review and editing

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This project was reviewed and deemed as exempt from research by the Ottawa Health Science Research Ethics Board.

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The authors declare no competing interests.

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Location: The work was performed at The Ottawa Hospital and The University of Ottawa. All authors have read and agree with the contents of this manuscript.

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Al Zoubi, F., Gold, R., Poitras, S. et al. Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice. International Orthopaedics (SICOT) 47, 343–350 (2023). https://doi.org/10.1007/s00264-022-05475-1

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  • DOI: https://doi.org/10.1007/s00264-022-05475-1

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