The promise of digital health technologies (DHTs) was immense. A future where managing our health, tracking our metrics, and engaging with our care was seamlessly integrated into our daily lives, empowering us like never before. Indeed, there has been a “rapid proliferation” of these tools, offering numerous potential benefits to patients.

However, as someone deeply invested in the future of healthcare, I must speak plainly: for the majority of patients grappling with the most complex challenge of our time – multimorbidity – digital health has, in its current fragmented state, largely failed us.

Multimorbidity, the coexistence of two or more chronic conditions in a single patient, has become perhaps the most pressing challenge in modern healthcare. As populations age, more and more individuals are managing a complicated web of chronic diseases. Yet, a recent study published in JAMA Network Open – the findings of which are truly alarming – lays bare the fundamental flaw in our current digital health ecosystem.

This research examined the digital health needs of a hypothetical 79-year-old woman managing five chronic conditions …. The findings revealed a critical shortcoming: despite the vast number of available DHTs (they identified 148 unique technologies applicable to this one patient), the overwhelming majority – a staggering 97% – were designed to address only single conditions or problems …. Only a tiny fraction (3.4%) were intended for two or more conditions.

What does this mean in practice for a patient with multimorbidity? The study’s conclusion is stark: this hypothetical patient would need to be prescribed up to 13 different apps and seven distinct devices just to benefit from 28 functions considered important by health professionals.

Let that sink in. Managing five conditions could require juggling twenty different technologies. This isn’t empowering; it’s overwhelming. The researchers rightly point out that this current state of DHTs “might generate an important burden” for these patients.

This burden is multifaceted:

• Having to familiarize oneself with numerous different interfaces.
• Creating entirely new, complex routines just to incorporate the technologies into daily life.
• Potentially dealing with contradictory information or requirements from different tools.

This fragmented, condition-specific approach directly “undermines the potential benefits of digital health interventions”. It fails to reflect the lived reality of patients whose conditions don’t exist in isolation but constantly interact and influence each other . We are stuck in a product-focused mindset, rather than one centered on the patient’s complex needs and capacities. This highlights a critical need to pursue “minimally disruptive digital medicine”.

So, if the current wave of fragmented digital health technologies isn’t the answer for our most complex patients, what is?

The answer lies in Artificial Intelligence (AI).

Unlike traditional DHTs that are siloed by condition, AI offers a promising avenue to address the complexities of multimorbidity precisely because of its capability to synthesize vast amounts of heterogeneous data . We’re talking about integrating everything from electronic health records and lab results to continuous data streams from wearable devices, socioeconomic factors, lifestyle choices, and even environmental exposures (the Exposome, which I’ve discussed previously).

AI can move beyond the fragmented view to provide a truly holistic view of a patient’s health. By analyzing these integrated datasets, AI-driven platforms can identify subtle, previously unknown patterns and predict health trajectories with greater accuracy. This enables something fundamentally different: proactive interventions rather than reactive sickcare.

Crucially for multimorbidity, AI systems can understand the interplay between multiple conditions and prioritize care plans accordingly, ensuring that managing one disease doesn’t negatively impact another. Furthermore, AI facilitates personalized care, tailoring strategies not just to conditions, but to the individual patient’s unique profile, preferences, and context .

While challenges exist – including data integration, ensuring equity and mitigating bias, fostering patient trust, and developing clear regulatory frameworks – the potential of AI to transform the management of multimorbidity is substantial.

We need a collaborative effort involving healthcare providers, researchers, developers, policymakers, and most importantly, patients themselves, to build and implement robust, transparent AI tools.

AI is not a miracle cure, but it is the essential tool that can cut through the “app avalanche” and the “multimorbidity minefield” created by our current digital health landscape. By integrating data, personalizing care, and facilitating proactive interventions, AI offers the most viable path to alleviating the immense burden on patients and healthcare systems alike . It’s time to move beyond fragmented tools and embrace the integrated power of AI to finally build a truly patient-centered, comprehensive digital health future.