Leveraging AI for Inclusive Home Healthcare (Case Study: United Kingdom)

In advancing the United Nations Sustainable Development Goals, SDG 10: Reduced Inequalities emphasizes the need to ensure social inclusion and equal access to essential services, particularly for vulnerable populations such as the elderly, chronically ill, and those with limited mobility. Across many healthcare systems, inequalities persist due to age, income, geography, and systemic inefficiencies, often leaving the most vulnerable with reduced access to timely and quality care.

A compelling example of how data and artificial intelligence can address these disparities is Cera Care, a London-based digital health provider that has transformed home healthcare delivery. By leveraging large-scale data collection, predictive analytics, and AI-driven decision-making, Cera Care has built a model that not only improves health outcomes but also reduces inequalities in access to care, enabling vulnerable populations to receive high-quality services in their own homes.

Context and Challenge

Healthcare inequality in the UK disproportionately affects older adults and high-risk populations, who account for a significant share of hospital admissions. Despite universal coverage, these groups often experience challenges in accessing continuous, preventative, and well-coordinated care. The system has historically been hospital-centric and reactive, with ongoing challenges related to fragmentation and interoperability of health data systems.

This creates several systemic challenges, such as: delayed identification of health risks, leading to avoidable hospitalizations, unequal access to consistent, high-quality care across regions, high dependency on institutional care, limiting independence, and resource constraints within the NHS and local government systems.

As populations age, these inequalities intensify. Older adults, especially those with chronic conditions, require continuous monitoring and early intervention, yet are often underserved by systems designed for episodic care. The need was clear: a scalable, data-driven model that could deliver personalized, preventative, and equitable care at home.

Data-Driven Solution

To address these disparities, Cera Care developed an AI-powered home healthcare platform that integrates real-time data collection with predictive analytics to deliver proactive care.

The initiative involves Cera Care as a HealthTech provider that develops AI-enabled care platforms and data infrastructure, supported by a large workforce of carers and nurses (10,000+), who collect real-time patient data and deliver care. It also involves the National Health Service and UK local government bodies, which integrate these services within public healthcare and social care frameworks. This collaboration enables technology to be embedded directly into frontline care delivery while aligning with broader public health objectives.

Methodology

Cera’s model is built on continuous data capture and predictive intelligence, transforming care from reactive to preventative.

1. Large-Scale Data Collection

During each home visit, carers use a digital application to record patient observations such as: mobility and fall risk, sleep patterns and hydration, and mood and behavioral changes.

This has resulted in a dataset of over 300 billion health data points, one of the largest home healthcare datasets globally.

2. Predictive AI Models

Machine learning algorithms analyze this data to identify early warning signs of deterioration. The system can predict over 80% of falls and hospitalizations up to 7 days in advance, and flag high-risk individuals for immediate intervention.

3. Preventative Intervention System

When risks are detected, alerts are sent to care teams, prompting timely actions such as: adjusting care plans, escalating cases to GPs or clinical teams, or delivering appropriate care and interventions at home where feasible.

4. Continuous Feedback and Learning

The system continuously updates patient profiles based on ongoing data input and supports iterative improvements in predictive accuracy, contributing to a dynamic and increasingly personalized care model.

Implementation and Results

Cera Care has scaled its model nationally, delivering:

• 2.5 million home visits per month across the UK
• Coverage across 150+ local authorities and two-thirds of NHS regions
• Services reaching 30 million of vulnerable individuals to date.

The quantitative results demonstrate significant impact:

• Falls predicted 7 days in advance with 83% accuracy by AI
• Falls reduced by around 20%, a major cause of elderly injury
• Hospitalizations predicted 7 days in advance with 80% accuracy
• Hospitalizations reduced by up to 70% among high-risk patients
• Up to five times faster hospital discharges
• At least 233 emergency admissions prevented per month through early intervention
• Over £1 billion in total savings for the NHS and UK Government, to date.
• £1.5 million in daily public cost savings due to reduced hospital demand
• Up to 10 times reduction in the cost of care

These outcomes reflect a shift from crisis-driven healthcare to predictive, community-based care, significantly improving system efficiency.

Outcomes and Impact

• Cera Care’s AI-driven model directly advances SDG 10 by reducing inequalities in healthcare access and outcomes, through:
• Delivering services at home removes geographic and mobility barriers, ensuring vulnerable populations receive consistent care regardless of location.
• Predictive analytics ensures early intervention for high-risk individuals, reducing disparities in emergency care and hospitalization rates.
• By preventing avoidable hospital admissions, patients can remain in their homes and communities longer, improving quality of life.
• Reduced strain on hospitals allows public resources to be redistributed more equitably across the healthcare system.
• The creation of large-scale datasets on older populations, which are often underrepresented in research, helps address long-standing gaps in healthcare knowledge and policy design.

Conclusion

Cera Care exemplifies how data, AI, and predictive analytics can reduce inequalities in access to essential services, directly supporting SDG 10. By leveraging over 300 billion data points, predicting health risks with over 80% accuracy, and reducing hospitalizations by up to 70%, the model demonstrates measurable, large-scale impact.

More importantly, it reframes healthcare delivery: from reactive and centralized to preventative, personalized, and inclusive. Through strong collaboration between technology providers, public institutions, and frontline workers, Cera Care shows that data can be a powerful tool for equity, ensuring that age, health status, or mobility no longer determine access to quality care.

As countries seek to build more inclusive societies, this case provides a clear blueprint: use data not only to improve systems, but to ensure those systems serve everyone, especially the most vulnerable.