Why pharma’s digital health tools miss the mark

Over the past decade, pharma has made digital health a strategic priority, launching patient apps, HCP platforms, adherence tools, and data-driven services. Yet, outcomes remain mixed. Many initiatives stall at pilot stage, while others struggle to sustain engagement, integrate into care pathways, or prove long-term value.

The issue is not a lack of innovation. It is that digital health has often been treated as a delivery project, rather than a strategic capability. When it sits outside brand objectives, clinical workflows, and enterprise value models, ownership fragments, success metrics blur, and scale becomes difficult to justify.

This article unpacks why these patterns persist, and what must change. Drawing on interviews with leaders at Roche, Amgen, AstraZeneca, and Takeda, and experts from The Stem, we explore cultural, operational, and value-definition shifts required for digital health to deliver durable impact.

1. Culture is the hardest barrier to digital success

Pharma’s risk-averse and process-heavy legacy culture inhibits innovation. Digital health requires rapid iteration, and cross-functional collaboration. Yet, innovation teams are often isolated, lack decision-making power, and operate outside core business functions.

Action: Build hybrid operating models where digital teams are embedded into brand teams without being constrained by brand-led decision cycles; ensure long-term product ownership and rapid experimentation for real-world impact. Promote internal champions who can translate across medical, commercial, and digital functions to sustain momentum and adoption.

2. Misaligned strategies: Digital health vs brand objectives

While pharma companies have made significant progress in adopting user-centred design and validating needs with patients and HCPs, a persistent challenge remains: aligning digital health initiatives with drug brand objectives.

Too often, digital health teams and brand teams operate with diverging priorities. This misalignment creates internal tensions and results in suboptimal tools that are neither commercially relevant nor clinically meaningful.

Even when initial user research is sound, the final product can suffer if brand teams push for features or positioning that dilute the user value. These solutions fail to gain traction, resulting in disappointing engagement metrics. Executives, seeing limited returns, then decide not to invest further, causing projects to stall at pilot stage.

Action: Establish shared success metrics across digital and brand teams. Begin co-creation at the concept phase and maintain joint ownership through launch. Create forums where commercial, medical, and digital leaders review alignment at each stage-gate.

3. Redefine ROI: Link return on experience to business impact

Pharma has traditionally evaluated digital health ROI through direct commercial metrics such as revenue uplift, adherence impact, or reach. These measures matter, but on their own they fail to explain why a digital solution succeeds or stalls.

Digital health creates value earlier and upstream: improving patient understanding, supporting better decisions, reducing friction, and building trust with patients and HCPs. These experiential and behavioural effects are not “soft” outcomes; they are leading indicators of commercial and clinical impact.

Ken Tubman, former head of digital health go-to-market at Takeda, captured this shift well:

“I don’t think of ROI in isolation. I think about whether we’re genuinely creating value for patients – because without that, sustainable return never materialises.”

He adds:

“If we earn a meaningful share of a patient’s attention in the right way, we create the conditions for long-term impact.”¹

Forward-looking organisations are moving toward blended measurement frameworks that connect experience to economics. Often described as Return on Experience, these models track how patient and HCP value translates into outcomes over time.

Action: Redesign ROI models to explicitly link experience to impact. Measure adoption, engagement quality, clinical relevance, and patient-reported outcomes, then connect them to business KPIs such as persistence, brand differentiation, and long-term value using real-world data alongside traditional metrics.

4. Leverage real-world data for strategic advantage

Digital health solutions generate rich streams of real-world data (RWD): usage logs, symptom tracking, engagement behaviour, and outcomes. Yet, pharma often underuses this information, either by not feeding it back into product iterations or by missing its broader value for commercial teams.

Opportunities to apply RWD include:

Refining patient experience and identifying drop-off points
Validating clinical and behavioural impact
Informing upstream R&D (e.g., new endpoints or hypotheses)
Strengthening market access narratives
Segmenting users and HCPs for tailored engagement

Thomas Boillat, digital health product lead at Roche, notes:

“We can’t treat these tools like static products. They generate evidence every day, we just need to act on it.”²

Action: Embed feedback loops into digital operating models. Treat data as a living asset. Combine qualitative signals, predictive models, and commercial data to understand where value is created.

5. Improve adoption and sustain engagement

Initial uptake is rarely the real problem in pharma digital health. The deeper issue is the use of tools that are not designed to become operationally necessary; tools that may see early usage, but fail to earn a role in clinical workflows or in patients’ routines.

Pharma often assumes adoption is a design or integration challenge, believing better UX or EHR integration will drive use. In reality, even strong tools fail if they remain optional. HCPs facing time pressure and accountability will ignore what doesn’t reduce workload or risk. Patients disengage if the tool doesn’t deliver visible benefit or a sense of progress and control.

Emma Vitalini, former head of global digital health technology innovation at Amgen highlights a structural weakness in pharma’s current approach: “As soon as a patient progresses or changes treatment, they must use a new tool, and continuity of data is lost, for both care and research.”³

Pharma has become more proficient at launching digital products, but less rigorous about owning adoption. Digital initiatives are often governed up to the point of launch, then handed to the field, affiliates, or end users, assuming that value, and therefore adoption, will materialise on its own. It rarely does.

As David Braun, head of commercial operations at Merck KGaA (EMD Serono), explains:

“Constructing a business case for a digital health solution is manageable. However, more challenging is determining whether the solution delivers sufficient value for patients. If the value proposition is not clearly established, and effective activation is not in place, the likelihood of achieving widespread adoption diminishes.”⁴

Action: Define adoption upfront as a core outcome. Build only for problems users can’t ignore, assign a single owner accountable for activation and sustained use, and track impact beyond launch. If the solution doesn’t deliver clear operational value for HCPs or patients, reconsider the investment.

6. What AI reshaping patient & HCP engagement means

In early 2026, two of the world’s leading AI labs launched health-focused versions of their platforms, signalling a major shift in how healthcare and pharma must think about digital solutions:

ChatGPT Health offers a dedicated health experience where users can upload medical records and link wellness apps to get personalised insights and context-aware explanations, combining health information with strict privacy protections.
Claude for Healthcare extends the Claude AI into regulated care environments with HIPAA-ready tools for providers, payers, patients, and life sciences workflows, integrating with healthcare data sources like coding systems and connecting to clinical and scientific evidence repositories like PubMed and clinical trial registries.

These developments reflect a new baseline for what users expect: AI that connects with real health data, operates regulated environments, and delivers value inside workflows, rather than as standalone apps.

For pharma, this evolution matters because it exposes, more clearly than before, the reasons many past digital health initiatives failed:

Tools that aren’t embedded in workflows fail to be used consistently.
Solutions that don’t earn trust are sidelined by MLR, risk, and governance teams.
Static tools with fixed utility lose relevance quickly in dynamic care settings.

AI doesn’t fix these problems by itself. But it forces a new definition of product–market fit: where success is measured not only by user interest, but by integration into care processes, value delivery, and alignment with regulatory, compliance, and commercial imperatives.

Pharma teams that want to succeed with AI need to think beyond the narrow “build a tool and ship it” mindset that limited earlier digital health efforts. They must:

Design for real workflows with clinicians, patients, and internal teams
Build trust and safety into the core experience so medical and legal reviewers can endorse use
Demonstrate measurable value across clinical, operational, and economic dimensions
Plan for continuous learning and iteration

AI is not just raising expectations; it is reshaping how digital health is experienced. Pharma organisations that respond by rethinking interaction, trust, and value creation can move beyond pilots toward sustained impact. Those that don’t risk AI amplifying the same adoption challenges that have held digital health back.

Ken Tubman summarises the opportunity well:

“Digital health shouldn’t be about shiny tools. It should be about reducing friction, showing value, and earning attention. That’s how we win.”¹

Action: Reframe AI around trust and value exchange, not features. Design AI for key patient and HCP decision moments, with clear governance on explainability, and data boundaries. Link early engagement to outcomes, and treat AI as an evolving capability, not a one-off launch.

From digital tools to digital capability

Pharma’s challenge with digital health has never been about ideas, technology, or investment, it’s been about execution as a capability. The leaders we spoke to all point to the same lesson: digital health only delivers value when it is embedded into how the organisation operates, linking experience, evidence, and economics. AI is now raising the bar on trust and integration, making these structural gaps harder to ignore.

The path forwards is not about more tools, but sharper choices on where digital matters, how value is measured, who owns adoption, and how solutions are sustained, because digital health is no longer an experiment, but a test of pharma’s ability to translate innovation into real-world impact.