Gout patients in England are not receiving the simple, cheap treatments that NICE recommends, and a new data-driven approach aims to close that gap. The problem is that NICE has issued over 300 clinical guidelines, but nobody can easily tell whether doctors are actually following them. Current monitoring relies on manual data collection, which is slow and gives only a snapshot. This makes it hard to spot where care is failing or to target improvements. The researcher will use OpenSAFELY, a system that already holds anonymised health data for 58 million people—over 99% of England’s population—to track real-world uptake of NICE’s gout guideline in near real-time. If this works, the impact is direct: fewer gout flare-ups, less pain and disability, and fewer avoidable hospitalisations. The researcher will also design a digital intervention—embedded in GP software and paired with educational content—and test it in a cluster-randomised trial across 38 general practices. Because the entire analysis framework is built on routinely collected data, the same method could be adapted to monitor any NICE guideline, giving policymakers a continuous, low-cost way to reduce healthcare inequality across dozens of disease areas.
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Background The National Institute for Health and Care Excellence (NICE) has published over 300 clinical guidelines, containing evidence-based recommendations for best practice care. However, the ability to monitor real-world uptake of these recommendations and demonstrate impact for patients is limited. Existing approaches rely upon manual data collection, which is time-consuming and provides only a snapshot of performance. This makes it challenging to detect variation in care and target interventions towards reducing healthcare inequality. Many of these limitations could be overcome by using electronic health data that are captured routinely in clinical practice. Through the OpenSAFELY Trusted Research Environment, we now have the ability to analyse health data in near real-time for 58 million people, covering more than 99% of England's population. This creates the potential for continuous evaluation of care quality across multiple disease areas. Aims and objectives I will develop a data-driven approach to monitoring the uptake of NICE guidance, demonstrating real-world impact for patients, and facilitating efficient evaluation of quality improvement interventions. To develop this methodology, I have chosen gout as an exemplar disease with a persistent evidence-practice gap. I will complete three workstreams: Develop a reproducible analytic framework to monitor the implementation of NICE guidance; Design a multi-faceted intervention to maximise uptake of guideline-recommended care; Evaluate this intervention using a data-enabled, cluster-randomised trial in OpenSAFELY. Methods (timelines for delivery) Workstream 1 (months 0-12): Using anonymised GP and hospital data for 58 million people in OpenSAFELY, I will systematically map recommendations from the NICE Gout Guideline and analyse temporal trends in their implementation. I will use regression-modelling approaches to identify predictors of sub-optimal care, and evaluate the impact on outcomes including comorbidities and hospitalisations. Data-driven dashboards will feedback performance against NICE standards at national, regional and local levels. Workstream 2 (months 12-24): I will convene focus groups with broad stakeholder representation to identify barriers and facilitators of optimal gout care. I will collaborate with implementation science experts at King's College London to develop an evidence-based intervention package that provides NICE-recommended care for people with gout, incorporating digital solutions within electronic health record software and educational content. Workstream 3 (months 24-60): I will conduct a data-enabled, cluster-randomised trial in OpenSAFELY to evaluate the intervention from Workstream 2. General practices (n=38) will be randomised 1:1 to receive this intervention or usual care. All study data will be automatically captured in OpenSAFELY and analysed using the framework developed in Workstream 1. Outcomes will include: The uptake of NICE-recommended gout treatments (e.g. allopurinol); The incidence of gout-associated comorbidities (e.g. kidney disease); Consultation/hospitalisation rates. Anticipated impact My research will directly benefit patients with gout, by reducing pain and disability. Better disease control will prevent avoidable hospitalisations and complications. For policy makers, it will help target strategies towards reducing healthcare inequality. I will be collaborating with NICE throughout my Fellowship to ensure this methodology is readily adaptable for other guidelines and diseases. I will share all of my analysis code openly, and communicate my findings through conferences, research papers, social media, and PPIE events.
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