By Jon Buckley, Director of Experience, Inizio Evoke Europe
Digital health and connected technologies are reshaping healthcare. Wearables track our vitals, smart devices remind us to take medication, and AI-driven platforms diagnose conditions remotely. The vision? A more personalised, accessible, and data-driven healthcare system for all members of society.
Digital health should be about ideas that thrive in real lives. Digital technology should offer solutions that don’t just exist in theory but work for everyone, regardless of their background, income, or digital literacy. But the digital divide—the gap between those who can and cannot access digital technology such as smartphones, laptops, and the internet—is challenging health equity.
So, if we want to help make health more human™, here’s the critical question we must ask: Does digital health truly reduce disparities, or does it risk reinforcing the very inequities it aims to eliminate?
The Digital Divide: Who Gets Left Behind?
While digital technology is more widespread than ever, ownership alone does not equal access. Digital health capital—the skills, trust, and resources needed to engage with digital health—can vary widely.
For one thing, not all devices are created equal. A high-end smartwatch with advanced health-tracking features provides more insights than a basic mobile phone but is not affordable for all. Then there’s data and connectivity. Telehealth and app-based healthcare rely on stable internet and mobile data, which remain inaccessible for many.
And when we think about digital technology, an inherent part of this is digital literacy and trust. A sophisticated remote monitoring system is useless if patients don’t understand how to use it, or don’t trust it.
So, digital health can end up benefitting only the digitally privileged, unless steps are taken to address these disparities.
From Launch to Lasting Change: The Role of COM-B
A common pitfall in digital health is the "launch and leave" approach. An attitude that once a solution has gone live, this is the end of the process. But this is an unhelpful mindset. Long-term strategies, ongoing investment, and continuous learning and improvement are crucial for sustainable adoption of and engagement with digital technology.
"Don’t just launch and leave. Launch and land. Land and make lasting change."
For ideas to truly thrive in real lives, they must be built with behaviour change in mind.
This is where the COM-B (Capability, Opportunity, Motivation – Behaviour) model becomes invaluable.
The COM-B model asks many questions to help drive behaviour change. For example, if we’re thinking about Capability, we may ask: does the patient have the knowledge and skills to use a connected device of app? Do they understand what their data really means and how to act on it?
If we’re thinking about Opportunity, can they afford the device? Do they have reliable connectivity to sync data? Is the solution culturally and linguistically accessible?
And if we’re thinking about Motivation, we may want to find out if they trust the technology. Do they see the value in using it consistently? Do they have the support when they encounter challenges?
These are just some of the questions that the COM-B model can help us to answer. The answers to these questions can change and vary between different groups. To drive meaningful behaviour change, solutions must be iterative, not static. This means:
Providing ongoing training and education to help users use the digital tools with confidence, particularly in underserved communities.
Offering continuous support and adaptation to ensure patient experiences shape updates and improvements.
Embedding digital health into daily life to move beyond one-off interactions and towards sustained engagement through nudges, reminders, and real-world incentives.
Bias in AI, Devices, and Connected Health
AI-driven diagnostics, smart devices, and virtual health assistants are only as good as the data they are trained on. Many algorithms are developed using biased datasets that underrepresent marginalised communities.
Many wearable devices, for example, struggle to accurately record heart rate or oxygen saturation readings on darker skin tones. What’s more, AI-based tools may not account for genetic, environmental, or cultural health variations, leading to biased clinical recommendations.
On top of inaccurate readings for diverse populations and the risks of misdiagnosis, if connected health solutions are designed for the tech-savvy, they leave behind those who need them most. Exclusion from remote monitoring is another big problem.
So, What’s the Solution?
Digital and connected health can reduce bias, but only if designed with equity at its core. What we mean by this is:
Offering inclusive design—ensuring connected devices and AI models work for all demographics, not just the default populations in training datasets.
Strengthening digital health capital—using COM-B principles to drive long-term adoption and engagement rather than one-time use.
Bridging the infrastructure gap—expanding affordable internet access, ensuring devices work in low-resource settings.
Undergoing continuous evaluation—through auditing AI and connected health tools for unintended biases and gaps in care.
A Call to Action
Connected health and digital tools can be a force for good, but only if we build them for real people in real lives.
We’re fortunate to be in an industry where we can make a real difference—where our skills as designers, strategists, and technologists help make the complex simple. Our understanding of how individuals engage with digital products can ensure they are accessible to all. And our ability to listen and empathise can shape solutions that truly serve the people who need them most.
So, let’s never forget the human beings we’re working to help. Let’s create ideas that thrive in real lives.