In the UK, multidisciplinary teamwork for patients with hip fracture has been shown to reduce mortality and improves health-related quality of life for patients, while also reducing hospital bed days and associated healthcare costs. However, despite rapidly increasing numbers of fragility fractures, multidisciplinary shared care is rare in low- and middle-income countries around the world. The HIPCARE trial will test the introduction of multidisciplinary care pathways in five low- and middle-income countries in South and Southeast Asia, with the aim to improve patients’ quality of life and reduce healthcare costs. Cite this article:
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
