Expert panel backs shared medical appointments to expand lifestyle care for chronic disease

A new American College of Lifestyle Medicine consensus statement says group-based consultations could widen access and support behaviour change, while acknowledging that evidence on their effectiveness remains mixed.

The American College of Lifestyle Medicine has issued an expert consensus statement setting out how shared medical appointments can be used to deliver lifestyle interventions for people with chronic disease, as health systems look for ways to handle rising demand and shorter visit times. The organisation said the model, which combines individual care with group support, is intended to give clinicians more room to address the behavioural and social factors that often shape long-term health.

Shared medical appointments bring more than one patient into the same clinical session, with one or more clinicians overseeing care. The consensus statement says the approach has been used in settings ranging from type 2 diabetes and cardiovascular disease to obesity, chronic pain, behavioural health and prenatal care. In its announcement, the college said a multidisciplinary panel reached agreement on recommendations after reviewing 106 proposed statements and endorsing 56.

The panel argued that the format can improve access to care, strengthen peer support and make it easier for patients to set goals and sustain healthier habits. Padmaja Patel, president of the college and a co-author of the statement, said the model could help reshape care delivery by widening access, supporting behaviour change and encouraging stronger links between patients and clinicians. Paula Gardiner, the first author, said the breadth of the panel added weight to the findings.

The new statement builds on earlier reviews that were more cautious in tone. A series of systematic reviews and assessments published over the past decade found that shared medical appointments may improve access, efficiency and some patient outcomes, but also noted that studies have varied widely in design and results. Those reviews concluded that more research is still needed to determine where the model works best and how it should be implemented at scale.