Background and purpose — Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short-and long-term mortality in a large cohort of patients with hip fractures. Methods — In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018–2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, including logistic regression, were trained with internal cross-validation and evaluated on discrimination (c-statistic), sensitivity, specificity, calibration, and interpretability. Results — 74,396 patients (median age 84, IQR 78–89; 68% female) were analyzed. Most patients lived at home (69%) and high malnutrition risk was seen in 10%. 18% had dementia. Mortality rates were 9.1% (30-day), 15% (90-day), and 26% (1-year). Logistic regression performed comparably to other algorithms, but was chosen as the preferred algorithm due to its superior interpretability (c-statistic: 30-day 0.82, 90-day 0.81, 1-year 0.80). Conclusion — We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. This information may inform SDM.

Machine learning algorithms show promise in assisting clinical decision-making; however, only a few have been successfully implemented in practice. To bridge this gap, it is essential to analyse the clinicians’ perspective on the compatibility of Artificial Intelligence-based clinical decision support systems (AI-CDSSs) with their clinical tasks. We therefore conducted a literature review of 21 empirical qualitative studies that examined the interaction between health professionals and AI-CDSSs. We synthesised the research through the lens of the Task-Technology Fit (TTF) model, analysing task, technology and individual characteristics of AI-CDSS applications, to identify design elements that are (mis)aligned with clinicians’ needs. Three key findings emerged from our analysis. First, clinicians often expressed scepticism about the clinical judgements of AI-CDSSs, particularly questioning the system’s ability to compete with clinical expertise in the absence of contextual information. Users valued AI primarily for specific strengths, such as identifying trends in patient trajectories, consolidating large datasets and pattern recognition, and comparing similar patient cases, but were hesitant to rely on it for clinical decisions. Second, actionability emerged as a desired characteristic of AI-CDSSs. For instance, clinicians particularly appreciated features of AI-CDSSs that enabled them to explore how different clinical actions might influence outcomes, as well as Explainable AI for identifying modifiable variables that impacted prediction scores, allowing them to take informed action. Third, we identified various ways AI-CDSSs could be used in clinical practice, including for patient prioritisation, patient monitoring, care acceleration, risk communication and workflow efficiency. In essence, AI-CDSSs functioned either as an alert system, preventing oversights, or as a tool for more informed decision making. Our analysis challenges the assumption that AI-CDSSs add little value when clinicians disregard its predictions, as it frequently prompts them to critically reassess their judgments through additional testing, consultation with colleagues, and other actions. Overall, our findings underscore the importance of an in-depth understanding of how AI-CDSSs are used in clinical practice. To optimise for effectiveness, the design of AI-CDSSs should prioritise supporting clinicians’ cognitive processes and information needs. This approach ensures that we move beyond the hype, focusing on the responsible integration of AI-CDSSs, and ultimately enhancing patient care.

In opdracht van Zorginstituut Nederland heeft een consortium bestaande uit de Universiteit Maastricht, Ecorys Nederland, VU Amsterdam en UMC Utrecht, onder leiding van het Radboudumc, onderzoek gedaan naar verdringing binnen de ziekenhuiszorg door opname van dure, veelal niet kosteneffectieve medische technologieën in het basispakket.