Reducing COPD exacerbation frequency and severity through early and accurate diagnosis and risk stratification using integrated primary and secondary care data
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AI plain-English summaryEvery year, tens of thousands of people with COPD are rushed to hospital with a sudden, severe flare-up that could have been prevented with earlier warning. The problem is that current risk tools are blunt—they miss patients whose condition is quietly deteriorating until it is too late. This project will build smarter risk-stratification models by linking GP records with hospital data and applying natural language processing to spot subtle patterns in clinical notes that conventional coding overlooks. The researchers will also evaluate whether integrated Respiratory Hubs—centralised clinics that coordinate care across primary and secondary services—can reduce exacerbation frequency and severity. If successful, the work could give GPs a practical, data-driven tool to identify rising-risk patients months before a crisis, allowing targeted interventions such as medication adjustments or pulmonary rehabilitation. The project will also produce a blueprint for joining up COPD data pathways across the NHS, supporting the national Respiratory Data Strategy. This is applied health services research with a direct route to policy and practice, not fundamental science.
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