Keele Research Repository

The overall quality of care for musculoskeletal pain conditions is suboptimal, partly due to a considerable evidence-practice gap. In osteoarthritis and low back pain, structured models of care exist to help overcome that challenge. In osteoarthritis, focus is on stepped care models, where treatment decisions are guided by response to treatment, and increasingly comprehensive interventions are only offered to people with inadequate response to more simple care. In low back pain, the most widely known approach is based on risk stratification, where patients with higher predicted risk of poor outcome are offered more comprehensive care. For both conditions, the recommended interventions and models of care share many commonalities and there is no evidence that one model of care is more effective than the other. Limitations of existing models of care include a lack of integrated information on social factors, comorbid conditions, and previous treatment experience, and they do not support an interplay between health care, self-management, and community-based activities. Moving forwards, a common model across musculoskeletal conditions seems realistic, which points to an opportunity for reducing the complexity of implementation. We foresee this development will use big data sources and machine-learning methods to combine stepped and risk-stratified care and to integrate self-management support and patient-centred care to a greater extent in future models of care.