Abstract

GOULD, Daniel J et al. Improving quality of care in total knee arthroplasty using risk prediction: a narrative review of predictive models and factors associated with their implementation in clinical practice. SA orthop. j. [online]. 2024, vol.23, n.1, pp.15-22. ISSN 2309-8309.  http://dx.doi.org/10.17159/2309-8309/2024/v23n1a3.

With the growing capacity of modern healthcare systems, predictive analytics techniques are becoming increasingly powerful and more accessible. Careful consideration must be given to the whole process of prognostic model development and implementation to improve patient care in orthopaedics. Using the example of risk prediction models for total knee arthroplasty outcomes, the literature was reviewed to identify evidence and examples of factors associated with successfully taking predictive models from the computer and implementing them in the clinical environment where they can influence patient outcomes. There were 164 articles included after screening 439 abstracts, 37 of which reported models which had been implemented in the clinical environment. Six of these 37 articles reported some form of clinical impact evaluation, and five of the six evaluated the Risk Assessment and Prediction Tool (RAPT) for arthroplasty. These models demonstrated some positive impacts on clinical outcomes, such as decreased length of stay. However, the findings of this review demonstrate that only a small proportion of developed risk prediction models have been successfully implemented in the clinical environment where they can achieve this positive clinical impact. Level of evidence: Level 5

Keywords : knee; arthroplasty; risk; predictive model; machine learning.

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