Growth hormone deficiency affects approximately 1 in 5,000 individuals and, if left untreated during childhood, results in permanent stunted growth. Treating the condition can enable height gains of over 20cm, as illustrated by the case of soccer star Lionel Messi: at age 13, Messi stood less than 1.40m tall, but after receiving treatment while training at FC Barcelona’s youth academy, he reached a height of 1.69m.
Success, however, hinges on years of disciplined daily injections, from infancy to adulthood. Aside from the considerable emotional and social ordeal of coping with the condition itself, young patients and families must navigate a host of treatment challenges: needle anxiety, maintaining routines through adolescence, and sustaining motivation when progress feels invisible.
In recent years, we have seen healthcare companies embracing advances in digital technology to improve outcomes in the field of medicine – from drug discovery to diagnosis. But how can emerging tech improve patient experiences for treatment journeys as complex as those for rare conditions like growth hormone deficiency? And what can others learn from these initiatives to unlock value across the healthcare ecosystem?
Let’s take the example of Merck Healthcare, which developed a device called Easypod, as well as a digital ecosystem to support the delivery of growth hormone deficiency treatment. The device automated aspects of the injection process, such as needle placement and management, dosage, and wireless data sharing between the needle, a base station, and the patient’s phone. It incorporated motivational features for patients through an app, with messages of encouragement and reminders. A web portal enabled healthcare providers to monitor their patients’ treatment regimens. Children could also change the look and feel of the device – a feature that helped with the emotional side of treatment adoption.
How did this digital approach to drug delivery help in the real world? Merck reported a 93.7% median rate of adherence throughout the first year of treatment, and an 89.3% rate over the next four years.
Evidence from across healthcare shows that AI-driven and digital solutions are benefiting patients, providers, and other stakeholders with valuable, transferable lessons for leaders in the industry.
CVS Health, one of the largest healthcare providers in the world, has invested in AI-enabled clinical solutions that give nurses 90 more minutes a day to spend with patients. A dedicated app saves administrative time by taking care of vaccine scheduling, cost claims, patient chats, and doctor selection.
Bayer and Huma created a digital heart risk assessment tool, which facilitates evaluation without the need for blood tests or physical examinations. As a result, 80% of the audience learned about their heart risk, and 50% intended to consult with a doctor.
Sanofi’s commercial teams work with Turing for personalized engagement with healthcare providers. Their bespoke data and machine learning system provides recommendations on what to communicate, when to communicate it, and through which channel. The company also uses an AI agent to monitor and assess data to improve operational efficiency. For example, it can predict 80% of stock disruptions through probabilistic planning, relating 65% of risks to root causes.
Roche has expanded its digital pathology open environment with the integration of more than 20 AI algorithms from eight new collaborators in cancer research and diagnosis. Philips leverages AI for prostate, rectal, breast, and cardiac treatments: it can perform MRI exams up to three times faster, saving five million liters of non-renewable helium.
Success depends on establishing clear objectives before starting any initiative.
While these examples demonstrate the transformative potential of digital health, implementation remains challenging. Research across pharmaceutical and healthcare organizations reveals consistent barriers that leaders must address.
Set meaningful KPIs. Success depends on establishing clear objectives before starting any initiative. Companies should decide which stakeholders they want to focus on, and what specific problems they intend to tackle. They should then track results based on KPIs that will signal any need for correction during execution and eventually indicate the achievement (or not) of the desired outcome. We have created a free online tool to help companies set effective KPIs for digital and AI transformation.
Start with pilot projects, not grand transformations. Rather than a global launch, Merck introduced its electronic auto-injector in select markets, gathering patient and healthcare professional feedback before a broader rollout. This pilot phase proved critical, as early feedback informed device iterations, including customizable injection speed and depth settings.
Address digital literacy gaps systematically. Patient digital literacy represents the most frequently cited barrier to digital health adoption. However, provider digital literacy also matters. Healthcare professionals who lack adequate training in digital tools experience low self-efficacy and develop negative attitudes toward technology. Merck developed a multi-layered support program that gave nurses on-the-job training, an augmented reality phone app to guide patients through device operation, and embedded consultants to support initial rollout. A 2023 validation study of the app found that nurses rated it highly for supporting patient education.
Patient concerns about data privacy and security represent a significant barrier to adoption.
Design for integration, not innovation theater. The industry faces a particular risk: pursuing innovation purely for its own sake. Machine learning and AI carry significant potential, but embracing them without clear value creation represents an expensive error. A critical success factor for provider adoption is seamless integration with existing clinical workflows. Rather than replacing nurse judgment, CVS Health automated administrative documentation, giving nurses additional time to spend with patients. Technologies that provide actionable data within preexisting processes enable intervention to improve patient outcomes. Solutions that disrupt workflow without a clear benefit face resistance regardless of their sophistication.
Build trust through transparency and security. Patient concerns about data privacy and security represent a significant barrier to adoption. Organizations must ensure compliance with regulations like GDPR and HIPAA, implement robust encryption and access controls, and communicate how patient data will be used. However, security alone proves insufficient. Patients need provider endorsement. Multiple studies have found that patients trust their healthcare providers, meaning provider explanation of why digital tools are in use and how patients can utilize them significantly increases engagement. Participatory design approaches that incorporate patient perspectives throughout development build trust through involvement.
Personalize the experience, not just the data. Research across digital health interventions reveals four factors that drive adherence: personalization of content to individual user needs, reminders through individualized push notifications, user-friendly and technically stable design, and personal support complementary to the digital intervention. However, personalization extends beyond just features. Patients of different ages and cultural backgrounds have different expectations for digital tools. Following the 2020 pandemic, 90% of patients surveyed wanted communications that reflect where they were in their healthcare journey. Generic digital experiences risk annoying rather than helping patients.
Solve the interoperability problem early. Digital health solutions create vast amounts of data from wearable devices, electronic health records, apps, and sensors. However, stakeholders cannot access the full benefit unless that data can be integrated and utilized for meaningful insights. Novo Nordisk confronted this challenge when building a digitalized batch release system: it needed to consolidate clinical documentation from multiple systems while maintaining GxP compliance for regulatory requirements. Working with AWS Professional Services, it built a decentralized data mesh architecture serving over 2,000 internal users across more than 30 business data domains. This 2.3 petabyte centralized data lake enables real-time production insights while standardizing data access patterns across domains. The lesson: data integration strategies and governance structures must be established from the outset, not retrofitted after silos have formed.
Navigate regulation proactively, not reactively. The growing demand for pharmaceuticals with digital devices or software interfaces, such as smart injectors, creates regulatory challenges. The absence of clear regulatory guidance regarding implementation creates uncertainty for companies. Organizations need to strengthen their digital infrastructure through strategic investment while building relationships with regulators who are themselves learning to assess new technologies. Regulatory requirements vary by geography and care type, so companies need sector-specific strategies.
Converging evidence from industry illustrates how digital technologies can enhance traditional medical treatments: healthcare companies have not only improved patient outcomes but established a competitive advantage in mature markets.
The examples in this article succeeded not because the companies involved owned superior technology, but because they addressed the implementation challenges systematically. They started with clear objectives tied to specific stakeholder problems, tracked results through dedicated KPIs, engaged end users in design, and built organizational capabilities for continuous adaptation. Digital health transformation requires both technical and organizational change, The former is relatively straightforward; the latter determines success.
The content of this article is solely the responsibility of the authors. They wish to thank Merck’s Emre Ozcan and Fulvio Michelis for their support in the preparation of this article.
AI efficiency gains dominate business cases, but capability losses are systematically underestimated and often ignored. Vsevolod Shabad explains how to mitigate this critical risk. ...
While humor boosts likability and success with human recruiters, it risks backfiring with AI interviewers. Candidates must adopt a ‘bilingual’ strategy to avoid being penalized, say Jamie Gloor, Cecily Cooper, T Bradford...
Audio available
How CHROs can help the workforce navigate AI-driven change by building trust, improving the employee experience, and leading cultural change in HR and talent management. ...
Companies seeking to automate middle management risk eliminating capabilities that algorithms cannot replace. Leaders must identify tasks requiring practical and ethical judgment....
Explore first person business intelligence from top minds curated for a global executive audience