Implementation of clinical decision support tools for treatment selection in knee osteoarthritis: a scoping review

Knee osteoarthritis (KOA) presents heterogeneous phenotypes, motivating a need for clinicians to deliver targeted therapies. There is a plethora of options that can be encompassed in KOA treatment regimes. Clinical decision support (CDS) tools that incorporate individual patient data have the capacity to tailor treatments to meet a patient's individual needs and assist with clinical decision-making. We aim to identify and evaluate CDS tools for individuals with KOA that use individualised prediction models to guide intervention decisions. A scoping review of the literature. A systematic search of six electronic databases, including Ovid Embase, Ovid Medline, Cochrane, CHINAL Ultimate, Scopus and Web of Science, was conducted for articles published between January 1, 2010 and May 17, 2024. Two reviewers independently screened articles and extracted data on study design, tool implementation and underlying prediction models. Eligible studies implemented personalised decision aids, designed to support clinical decisions regarding KOA interventions. The search yielded 5376 publications, of which 2445 were duplicates, leaving 2931 for screening. After title/abstract and full-text reviews, 14 studies were included in the final analysis, with one added through citation searching. Ten distinct decision aids were identified across the included studies. Most studies originated from the United States. Fewer than half of the decision aids included personalised information about non-surgical alternatives. Outcomes such as knee pain and physical function were the most commonly addressed, while psychosocial and financial impacts were rarely reported. Limited details were provided about the development and functionality of the underlying prediction models. Personalised decision aids for KOA show promise in supporting patient-centred decision-making. However, their clinical utility is constrained by limited transparency in model development and implementation. Future studies should emphasise the inclusion of non-surgical treatment options, early-stage KOA patients and personalised outcomes beyond pain and function to enhance their relevance and impact in clinical practice.

Keywords: clinical decision support tools; knee osteoarthritis; machine learning; scoping review; treatment outcome.

© The Author(s), 2026.

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