ABSTRACT
BACKGROUND: Treatment for anterior cruciate ligament (ACL) rupture may follow a surgical or nonsurgical pathway. At present, there is uncertainty around treatment choice. Two shared decision-making tools have been codesigned to support patients to make a decision about treatment following an ACL rupture. The shared decision-making tools include a patient information leaflet and an option grid. We report the protocol for a mixed-methods feasibility study, with nested qualitative interviews, to understand feasibility, acceptability, indicators of effectiveness and implementation factors of these shared decision-making tools (combined to form one shared decision-making intervention). METHODS: A single-centre non-randomised feasibility study will be conducted with 20 patients. Patients diagnosed with an ACL rupture following magnetic resonance imaging will be identified from an orthopaedic clinic. The shared decision-making intervention will be delivered during a clinical consultation with a physiotherapist. The primary feasibility outcomes include the following: recruitment rate, fidelity, acceptability and follow-up questionnaire completion. The secondary outcome is the satisfaction with decision scale. The nested qualitative interview will explore experience of using the shared decision-making intervention to understand acceptability, implementation factors and areas for further refinement. DISCUSSION: This study will determine the feasibility of using a newly developed shared decision-making intervention designed to support patients to make a decision about treatment of their ACL rupture. The acceptability and indicators of effectiveness will also be explored. In the long term, the shared decision-making intervention may improve service and patient outcomes and ensure cost-effectiveness for the NHS; ensuring those most likely to benefit from surgical treatment proceed along this pathway. TRIAL REGISTRATION: Pending registration on ISRCTN.
ABSTRACT
BACKGROUND: COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom's response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. OBJECTIVE: We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). METHODS: A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners' pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers' secure environments, and to support federated cohort discovery queries and meta-analysis. RESULTS: A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. CONCLUSIONS: CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.
