Mixed methods evaluation of a hospital group model using an embedded research approach: study protocol

Introduction Hospital group models represent an organisational form that aims to bring together multiple provider organisations with a central headquarters and unified leadership responsible for locally managed operating units, standardised systems and a value-set shared across the group. These models seek to improve outcomes by reducing unwarranted variations in care provision and reducing costs through economies of scale. There is limited evidence on the impact and processes of implementing these models, so this study aims to evaluate one case study of a hospital group model. Methods and analysis We will conduct a formative, mixed-methods evaluation using an embedded research approach to analyse the implementation of the model and its impact on outcomes and costs. We will carry out a multisited ethnography to analyse the programme theory for model design and implementation, the barriers and facilitators in the implementation; and wider contextual issues that influence implementation using semi-structured interviews (n=80), non-participant observations (n=80 hours), ‘shadowing’ (n=20 hours) and documentary analysis. We will also carry out an economic evaluation composed of a cost-consequence analysis and a return on investment analysis to evaluate the costs of creating and running the model and balance these against the potential cost-savings. Ethics and dissemination The study protocol was reviewed by the local R&D Office and University College London Ethics Committee and classified as a service evaluation, not requiring approval by a research ethics committee. We will follow guidelines for informed consent, confidentiality and information governance, and address issues of critical distance prevalent in embedded research. Findings will be shared at regular time points to inform the implementation of the model. The evaluation will also generate: an evaluation framework to evaluate future changes; recommendations for meaningful baseline data and measuring improvement; identification of implementation costs and potential cost-savings; and lessons for the National Health Service on implementing these models.