Configuration of vascular services: a multiple methods research programme

Background Vascular services is changing rapidly, having emerged as a new specialty with its own training and specialised techniques. This has resulted in the need for reconfiguration of services to provide adequate specialist provision and accessible and equitable services. Objectives To identify the effects of service configuration on practice, resource use and outcomes. To model potential changes in configuration. To identify and/or develop electronic data collection tools for collecting patient-reported outcome measures and other clinical information. To evaluate patient preferences for aspects of services other than health-related quality of life. Design This was a multiple methods study comprising multiple systematic literature reviews; the development of a new outcome measure for users of vascular services (the electronic Personal Assessment Questionnaire – Vascular) based on the reviews, qualitative studies and psychometric evaluation; a trade-off exercise to measure process utilities; Hospital Episode Statistics analysis; and the development of individual disease models and a metamodel of service configuration. Setting Specialist vascular inpatient services in England. Data sources Modelling and Hospital Episode Statistics analysis for all vascular inpatients in England from 2006 to 2018. Qualitative studies and electronic Personal Assessment Questionnaire – Vascular evaluation with vascular patients from the Sheffield area. The trade-off studies were based on a societal sample from across England. Interventions The data analysis, preference studies and modelling explored the effect of different potential arrangements for service provision on the resource use, workload and outcomes for all interventions in the three main areas of inpatient vascular treatment: peripheral arterial disease, abdominal aortic aneurysm and carotid artery disease. The electronic Personal Assessment Questionnaire – Vascular was evaluated as a potential tool for clinical data collec