NHS
In collaboration with clinician Sam. Davies, dadoAI developed an AI-powered Long Covid symptom tracking and rehabilitation analysis platform that automatically identifies patient progress patterns, generates clinical summaries, and provides evidence-based rehabilitation insights.
Clinicians collected detailed symptom reports, activity levels, and recovery indicators but had no intelligent system to automatically identify patterns or generate evidence-based rehabilitation recommendations. Patients received inconsistent guidance because comprehensive data analysis was too time-consuming. The problem wasn't lack of clinical expertise but lack of AI-powered tools to process complex longitudinal data. Manual review of symptom trends took 2-3 hours per patient per month. Pattern identification across patient cohorts was impossible due to data volume. Progress summaries for referrals required hours of manual compilation. Evidence-based rehabilitation guidance relied on clinician memory rather than data analysis. Research insights required weeks of manual data cleaning before any analysis could begin.
Implementation involved intelligent AI design: Why this worked: - Automated pattern detection: AI identified improvement, plateau, or deterioration automatically by analysing symptom trajectories - Intelligent summarisation: Natural language generation created clinical summaries from structured data - Predictive insights: Machine learning identified which rehabilitation approaches worked for similar patient profiles - Longitudinal analysis: AI tracked patients over months, flagging concerning patterns early - Aggregated learning: Anonymised data revealed successful rehabilitation pathways across patient cohorts - Governance-first design: Built with NHS data protection and clinical safety standards from day one
