Overview of the Problem Australia’s ageing population and rising dementia prevalence place unsustainable pressure on the healthcare system. Current home care models rely heavily on reactive interventions like fall detectors or scheduled welfare checks, that respond only after a crisis occurs. This crisis-driven approach compromises dignity and safety, accelerating premature admission to residential care and preventable hospitalisations. Why It Is a Problem Reactive models miss early indicators of functional decline, such as increased fall risk or infections. For individuals with dementia, lacking continuous, non-invasive monitoring increases caregiver burden, causing sleep deprivation and burnout. Without objective data, clinicians rely on subjective recall, missing critical windows for early intervention that could preserve independence. Main Topics to Be Discussed This presentation introduces Everkin - a technology-supported living model powered by a predictive AI platform. Using a multi-modal ecosystem of non-invasive sensors (monitoring motion, sleep, and environment without cameras), the system learns individual baselines and predicts health events 24 to 48 hours before emergencies escalate. We present quantitative outcomes from real-world deployments: a 56% reduction in falls, a 67% decrease in wandering incidents, and significant caregiver wellbeing improvements. We also discuss the clinical governance required to integrate predictive insights into practice, ensuring technology enhances human-centered care. Target Audience Service providers, clinicians, policymakers, and innovators seeking scalable solutions to transform the care economy.