A New Era for Digital Health 2026

Together, these capabilities allow the PHI platform to operate as the population-level decision layer of Abu Dhabi’s intelligent health system, linking data to policy and investment choices. –Integrated population dataset: De-identified data from clinical, behavioural, financial and environmental sources are aggregated across the health ecosystem. The dataset provides a single, longitudinal view of how health outcomes, service use and lifestyle patterns evolve across communities. –Risk and behaviour modelling: Statistical and ML models stratify the population by geography, age, socioeconomic profile and modifiable risk factors such as obesity, activity levels and dietary habits. These models identify clusters of elevated risk and forecast where disease burden or service demand will rise without intervention. –Programme performance tracking: The Healthy Living Unit uses PHI to evaluate the performance and reach of lifestyle and environmental health initiatives. It analyses which programmes, such as community fitness schemes, workplace wellness initiatives or nutrition campaigns, achieve sustained participation and where redesign is needed. Insights guide whether interventions should be scaled, amended or geographically expanded. PHI also highlights structural barriers to participation, including distance from facilities, limited public transport and socioeconomic constraints, ensuring that preventive policy remains equitable and achievable. –Policy alignment: Insights from PHI guide prevention policy, workforce and infrastructure planning, and cross-sector partnerships addressing social and environmental determinants of health. Forecasts of population need are linked directly to service capacity and financing decisions, to ensure system alignment.2.4 How Abu Dhabi’s Population Health Intelligence platform works A New Era for Digital Health: Abu Dhabi’s Leap to Health Intelligence 18