Reshaping healthcare: The convergence of digital therapeutics and point-of-care diagnostics

By 2030, over 70 per cent of the global disease burden will stem from chronic conditions, many of which are manageable—if detected early and managed continuously (1).

Healthcare is being re-architected—not just digitised. The most significant developments are not based on standalone innovations but revolving around how technology is redefining care delivery. This is not just a matter of technological evolution. It represents a strategic opportunity for health systems, policy makers, and industry leaders to rethink how care is delivered, accessed, and scaled—especially in the context of chronic disease, aging populations, and resource-constrained geographies.

From episodic to continuous care

For decades, healthcare has centred on episodic care—reactive interventions, delayed diagnoses, and fragmented follow-ups. With chronic diseases accounting for over 80 per cent of global healthcare costs, this model is unsustainable. A more viable approach lies in decentralised care enabled by real-time data and personalised interventions.

PoC diagnostics are enabling this shift. Devices such as connected ECGs, Continuous Glucose Monitors, and multi-analyte platforms now deliver clinical-grade diagnostics at home, in pharmacies, and in low-infrastructure settings. AI-powered interpretation and cloud integration are making these devices not just accessible, but powerful tools in primary care and chronic disease management. 

Digital Therapeutics are emerging as prescription-grade interventions for conditions like diabetes, depression, insomnia, and substance use disorders. These platforms are already embedded into formal care pathways offering behavioural interventions, therapy tracking and medication adherence support—all on a smartphone. Several groundbreaking examples highlight this momentum. Rejoyn, an FDA-authorised app, delivers cognitive behavioural therapy (CBT) for major depressive disorder alongside pharmacotherapy (2). Somryst, approved for chronic insomnia, offers a fully digital CBT-I protocol that sustains sleep improvements over time (3). Circadian AI, an innovative early detection tool for heart abnormalities, exemplifies how next-gen diagnostics are emerging from even unexpected quarters.

The real power lies in their synergy. Together, PoC diagnostics and DTx form an intelligent, closed-loop model—where diagnostics and therapy reinforce each other continuously and intelligently.

AI and the rise of companion apps

Companion apps have evolved from basic tracking tools to intelligent digital health platforms. When integrated with point-of-care diagnostics, these apps can detect anomalies and trigger clinician alerts based on patient behaviour, clinical parameters, and predictive insights. 

For diabetic patients, AI models embedded in mobile apps analyse CGM data to suggest dosing adjustments or dietary recommendations. In mental health, natural language processing supports mood tracking through passive inputs and journaling. In specialised areas like ostomy care, pain check apps help patients self-report symptoms—enabling early detection of complications and personalised interventions.

As AI-enabled apps become more context-aware and integrated into the clinical workflows, companion apps are emerging as smart care assistants—enhancing engagement, adherence, and patient outcomes.

 

Real-world impact

Evidence for this convergence is growing. In diabetes care, pairing continuous glucose monitors with DTx interventions has shown HbA1c reductions of over 1.0 per cent (4) —a clinically significant improvement. In hypertension, real-time monitoring coupled with DTx-based coaching improves adherence (5).

Let’s examine how health systems are leveraging DTx and PoC devices. In hospitals, PoC diagnostics shorten time to diagnosis and improve bed utilisation. Remote monitoring, backed by DTx, helps reduce readmissions and facilitates post-discharge recovery with fewer clinical touchpoints, thereby reducing healthcare costs.

For pharmaceutical and medical device companies, the convergence offers new pathways for companion diagnostics, personalised therapy models, and real-world evidence generation. As value-based care models gain traction globally, models that enable better outcomes with lower costs are gaining favour.

The road to integration

Despite strong momentum, challenges remain. Interoperability with legacy systems is a persistent barrier. Many electronic health record (EHR) platforms aren’t built to ingest or act on continuous, patient-generated data. Digital literacy gaps—especially among older adults and in low-resource settings—add further friction.

On the regulatory front, ambiguity remains—particularly around Software as a Medical Device (SaMD) and AI-based diagnostic tools. However, progress is evident. Regulatory bodies are actively addressing this need for updated frameworks. In the United States, the FDA’s Digital Health Center of Excellence (6) is actively defining pathways for evaluating DTx and PoC solutions that operate outside traditional clinical environments.

Industry alliances and academic partnerships are playing a pivotal role in lowering entry barriers for digital health innovation through the development of validation protocols, standard APIs, and reimbursement frameworks. The NIH-funded Mobilize Center at Stanford exemplifies this, bringing together data scientists, clinicians, and engineers to define robust validation methodologies for sensor-driven and AI-powered diagnostics. Similarly, the Digital Therapeutics Alliance (DTA) unites manufacturers, providers, and payers to establish clinical evidence standards and shape global reimbursement models across the U.S., EU, and Asia.

Engineering intelligence into the care continuum

At Innominds, we see this not just as a theoretical trend but as a transformation in motion. Whether it’s transforming a connected device into an insight engine, or embedding DTx into clinical workflows, our goal is to create health systems that are not just digitally enabled, but intelligently orchestrated systems where patients become active participants in their care.

To realise this transformation, stakeholders must:

• Prioritise interoperability standards
• Invest in digital literacy and access
• Align incentives with continuous care outcomes

• Invest in digital literacy and access

Emerging trends – Shaping the future of healthcare AI GenAI in diagnostics for synthesising multimodal data Voice biomarkers for early detection of neurological and respiratory conditions through a patient’s speech Federated Learning to train AI models through institutional collaboration while safeguarding patient data

• Align incentives with continuous care outcomesAs we look to the future, the question is no longer whether to adopt digital health tools. It is whether we are ready to integrate them meaningfully. Now is the time to not just improve outcomes, but to fundamentally redefine care and save lives.

 

References:

1. IQVIA Institute (2024) – Digital health tools are expanding in scope
2. Nature Communications (2025) – Machine learning in point-of-care testing
3. McKinsey – Health benefits and business potential of digital therapeutics
4. BCPHR – Digital Therapeutics and Healthcare Integration
5. Markets & Markets – Future of Point-of-Care Diagnostics
6. European Medicines Agency – Regulatory Science Strategy 2025