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. 2023 Mar 9:20:101116.
doi: 10.1016/j.artd.2023.101116. eCollection 2023 Apr.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

Bahar Entezari  1   2 , Robert Koucheki  3   4 , Aazad Abbas  3   5 , Jay Toor  6 , Jesse I Wolfstadt  1   6 , Bheeshma Ravi  6   7 , Cari Whyne  4   5   6   7 , Johnathan R Lex  5   6
Affiliations

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

Bahar Entezari et al. Arthroplast Today. .

Abstract

Background: There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

Methods: A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

Results: Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

Conclusions: High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Keywords: Artificial intelligence; Optimization; Predictive modeling; Surgical scheduling; Total hip arthroplasty; Total knee arthroplasty.

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Figures

Figure 1
Figure 1
Diagrammatic representation of (a) neural network models, demonstrating the transformation of a combination of input features in hidden layers to yield outcome predictions, and (b) types of optimization strategies. ASA, American Society of Anesthesiologists; BMI, body mass index.
Figure 2
Figure 2
PRISMA flow diagram of the search strategy.
See this image and copyright information in PMC

References

    1. HCUP Fast Stats . Agency for Healthcare Research and Quality; Rockville (MD): 2021. Healthcare cost and utilization project (HCUP) - PubMed
    1. Kremers H.M., Larson D.R., Crowson C.S., Kremers W.K., Washington R.E., Steiner C.A., et al. Prevalence of total hip and knee replacement in the United States. J Bone Joint Surg Am. 2014;97:1386–1397. doi: 10.2106/JBJS.N.01141. - DOI - PMC - PubMed
    1. Singh J.A., Yu S., Chen L., Cleveland J.D., Singh J.A., Yu S., et al. Rates of total joint replacement in the United States: future projections to 2020 − 2040 using the national inpatient sample. J Rheumatol. 2020;46:1134–1140. doi: 10.3899/jrheum.170990. - DOI - PubMed
    1. Inacio M.C.S., Paxton E.W., Graves S.E., Namba R.S., Nemes S. Projected increase in total knee arthroplasty in the United States – an alternative projection model. Osteoarthritis Cartilage. 2017;25:1797–1803. doi: 10.1016/j.joca.2017.07.022. - DOI - PubMed
    1. Klug A., Gramlich Y., Rudert M., Drees P., Hoffmann R., Weißenberger M., et al. The projected volume of primary and revision total knee arthroplasty will place an immense burden on future health care systems over the next 30 years. Knee Surg Sports Traumatol Arthrosc. 2021;29:3287–3298. doi: 10.1007/s00167-020-06154-7. - DOI - PMC - PubMed

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