OBJECTIVES: Integrated care systems (ICSs) face several challenges when allocating resources from a system perspective, including balancing competing demands, operating within limited budgets, and ensuring transparency and accountability. The socio-technical allocation of resources (STAR) method is one approach that could these challenges by combining a health economic analysis with extensive stakeholder engagement. Chronic Obstructive Pulmonary Disease (COPD) is a resource intensive pathway costing the NHS an estimated £1.5 billion in 2011. This makes it an exemplar disease area to trial the STAR method. The aim of this piece of work was to support 5 ICSs to set funding priorities for the COPD pathway using the STAR method.

METHODS: For each ICS, two decision conferences were held involving clinicians, commissioners, patients, and public health practitioners. Consensus on the population benefit of current COPD pathway and potential pathway improvements were built during the decision conferences.

For each pathway improvement, we obtained costs estimates from NHS reference costs and interviews with ICS stakeholders. We obtained population health benefit estimates from literature review and the decision conferences. Then we modelled the additional pathway costs (NtC), population health benefit (PHB) and associated cost savings (RCv) for each scenario. Finally, interventions were prioritized based on their cost to population health benefit ratio ((NtC-RCv)/PHB) to ensure the most efficient allocation of resources.

RESULTS: There were common themes across all five of the ICSs in terms of their priority areas. Maximizing capacity in the virtual ward, implementing a case finding strategy (risk-based screening) to find undiagnosed COPD cases, initiating group consultations and improving patient education were all improvements that could generate population health whilst either being cost neutral or cost saving.

CONCLUSIONS: The STAR method can help ICSs to allocate funding systematically and transparently by identifying interventions which represent the most efficient allocation of resources.