Patient preference predictors revisited: technically feasible, ethically desirable, yet must be clinically relevant.

Although goal-concordant care is central to patient-centered medicine, determining treatment preferences for incapacitated patients remains a challenge. Nearly two decades ago, algorithms were proposed to estimate the most likely treatment preferences in the absence of advance directives, aiming to support surrogate decision-making. This idea has evolved into a race toward increasingly complex models, driven by the assumption that expanding data collection and refining predictive methods will yield more accurate approximations of patients' unknown treatment preferences. Despite extensive debate on the epistemic, ethical, and clinical challenges of these algorithms, none have been successfully implemented in clinical practice. We contend that this failure does not stem from any of these challenges but, rather, from conceptualizing these models simply as technically sophisticated replicas of advance directives, abstracting a few high-level treatment preferences across all clinical contexts while ignoring setting-specific, temporal, and relational factors. The barrier to the implementation of these models is fundamentally a technology design problem that requires a novel design perspective to ensure their clinical relevance. We discuss this perspective using neuro-intensive care as a case study and examine how algorithmic models could support time-sensitive decision-making for patients with severe acute brain injury. The success of patient preference predictions depends not only on their being technically feasible and ethically promising but on ensuring clinical relevance.