Comparison of an Artificial Intelligence–Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis

Importance  Decision aids can help inform appropriate selection of total knee replacement (TKR) for advanced knee osteoarthritis (OA). However, few decision aids combine patient education, preference assessment, and artificial intelligence (AI) using patient-reported outcome measurement data to generate personalized estimations of outcomes to augment shared decision-making (SDM).

Objective  To assess the effect of an AI-enabled patient decision aid that includes education, preference assessment, and personalized outcome estimations (using patient-reported outcome measurements) on decision quality, patient experience, functional outcomes, and process-level outcomes among individuals with advanced knee OA considering TKR in comparison with education only.

Design, Setting, and Participants  This randomized clinical trial at a single US academic orthopedic practice included 129 new adult patients presenting for OA-related knee pain from March 2019 to January 2020. Data were analyzed from April to May 2020.

Intervention  Patients were randomized into a group that received a decision aid including patient education, preference assessment, and personalized outcome estimations (intervention group) or a group receiving educational material only (control group) alongside usual care.

Main Outcomes and Measures  The primary outcome was decision quality, measured using the Knee OA Decision Quality Instrument (K-DQI). Secondary outcomes were collaborative decision-making (assessed using the CollaboRATE survey), patient satisfaction with consultation (using a numerical rating scale), Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) score, consultation time, TKR rate, and treatment concordance.

Results  A total of 69 patients in the intervention group (46 [67%] women) and 60 patients in the control group (37 [62%] women) were included in the analysis. The intervention group showed better decisional quality (K-DQI mean difference, 20.0%; SE, 3.02; 95% CI, 14.2%-26.1%; P < .001), collaborative decision-making (CollaboRATE, 8 of 69 [12%] vs 28 of 60 [47%] patients below median; P < .001), satisfaction (numerical rating scale, 9 of 65 [14%] vs 19 of 58 [33%] patients below median; P = .01), and improved functional outcomes at 4 to 6 months (mean [SE] KOOS JR, 4.9 [2.24] points higher in intervention group; 95% CI, 0.8-9.0 points; P = .02). The intervention did not significantly affect consultation time (mean [SE] difference, 2.23 [2.18] minutes; P = .31), TKR rates (16 of 69 [23%] vs 7 of 60 [12%] patients; P = .11), or treatment concordance (58 of 69 [84%] vs 44 of 60 [73%] patients; P = .19).

Conclusions and Relevance  In this randomized clinical trial, an AI-enabled decision aid significantly improved decision quality, level of SDM, satisfaction, and physical limitations without significantly impacting consultation times, TKR rates, or treatment concordance in patients with knee OA considering TKR. Decision aids using a personalized, data-driven approach can enhance SDM in the management of knee OA.