How to generate value with evidence-led digital health technologies

An analysis of the clinical trial activity of the top 15 pharma companies

Jonathan Sander
Manager, Commercial Strategy for Pharma and Life Sciences

Catijn Schierbeek
Manager, Commercial Strategy for Pharma and Life Sciences

Antoine Nguyen
Senior Consultant, R&D Pharma and Life Sciences

There’s no doubt that digital health technology (DHT) has grown significantly over the last decade. But a recent study reveals that many aren’t backed sufficiently by clinical evidence.[1] While this may reflect DHT’s relative youth, it’s nevertheless a missed opportunity, as evidence for clinical benefits are key adoption drivers for patients, providers and payers.

However, industry players are starting to recognise the importance of clinical evidence. A recent PwC survey of digital health professionals showed that 65% intend to shift their focus to more regulated solutions, with researchers and regulators building increasingly robust frameworks.[2,3,4]

Based on clinicaltrial.gov data we estimate that the use of DHT in clinical trials has grown by 97% within the last five years (Figure 1). And there are good examples of tangible outcomes from this effort. For example, this year the EMA qualified a digital endpoint in Duchenne muscular dystrophy, as an industry first.[5] Pharma and medical devices companies should watch this trend. Our industry research suggests that evidence-backed digital health solutions are more likely to achieve scale. In the future, evidence will be a fundamental enabler for digital health solutions.

To gain a deeper understanding, we analysed the clinical trials of the top 15 pharma companies to understand their focus for evidence generation in digital health, and from this analysis we’ve identified four recommendations for action by pharma leaders:

Build an integrated evidence strategy tailored to different user groups.
Rethink operating models to integrate digital health evidence generation.

Leverage remotely connected data to support stakeholders such as physicians or payers in decision-making.
Understand the evolving digital health landscape for each therapeutic area but make use of synergies to build a cross-therapy area portfolio.

Figure 1: Estimated number of clinical trials of the top 15 pharma companies using digital health technologies (*first six months)

(Estimate based on search syntax applied according to our methodology (see box) corrected by an estimated similar false positive rate of 8.5% as for the pharma trial subset)

For the purpose of this work, we define DHT as technologies that use computing platforms, connectivity, software and sensors for healthcare and related uses in line with the FDA’s definition of digital health.

Our analysis of the top 15 pharma companies’ clinical evidence generation in DHT shows they’ve significantly increased activity over the last five years (see Figure 2). However, the 269 trials we identified represent only a fraction of all clinical trials that include DHT. Overall, academic researchers lead activity, with 80% of trials not assigned to industry.

Most of the top 15 pharma trials use DHT to capture additional data points that are used either to measure a primary or secondary outcome or provide other information valuable to researchers (see Figure 3). However, we also see clinical trials used to assess either the clinical and functional utility of digital health solutions or the outcomes they achieve.

Figure 2: Number of trials of the top 15 pharma companies including digital health technology based on clinicaltrial.gov data (**first six months)

Figure 3: How the top 15 pharma companies apply digital health technologies in clinical trials

Evidence generation with DHT in clinical trials is not uniform amongst pharma companies. Mapping clinical trial activity by company (and correcting for company size with the average R&D budget of the last five years) reveals that some companies – typically those with a strong pipeline in an area with a higher digital health maturity (see Figure 4) – tend to be more active (see Figure 5).

Of course, clinical trials aren't the only way to generate evidence. For example, many digital health manufacturers choose the FDA 510k pathway, where demonstrating equivalency to existing technology can be shown without fully-fledged human trials, especially for lower-risk devices. Some researchers highlight the benefit of innovative approaches to evidence generation, such as simulations, as offering timelier and more cost-effective alternatives.[6] Nevertheless, our data clearly indicates that in the hope of building competitive advantage, some companies are advancing their efforts and making evidence a core pillar of their digital health strategy.

Figure 4: Use of digital health technologies of the top 15 pharma companies in clinical trials by therapeutic area

Figure 5: Clinical trials including digital health technology for individual companies compared to their average R&D budget of the last five years

Four recommendations

While it’s generally agreed that digital health solutions need to be evidence-backed to achieve scale, the cost and complexity involved raises the stakes for manufacturers. With this in mind, we make four recommendations for pharma companies to consider on their route to successful, evidence-backed digital health strategies:

Know which problem to solve for whom and then build a tailored evidence strategy

An often painful lesson from the past decade has been that many programs failed to scale because they were technology-led instead of addressing a key unmet customer need. There's now far more focus on solving key problems for patients, providers or payers. The next step is to define a clear (evidence-based) strategy aimed at all stakeholders – such as caregivers, providers or health system CFOs – that influence user adoption. This, in turn, requires additional research into stakeholders’ perceptions of value and what they need from evidence. The outcome should then guide the development of an integrated evidence plan for the digital health solution.

For example, we helped a major pharmaceutical company to understand the type of evidence and benchmark values HCPs would require to support a patient-facing digital health solution in neurology. We found that physicians rated operational efficiency as much more important than anticipated. This then helped the company to further shape their evidence generation study.

The collection of this data can be included in functional and usability studies that traditionally have a much narrower focus and are often already completed once companies realise the different evidence needs of all stakeholders. The resulting additional studies required mean higher costs and longer time to market.

Integrate DH evidence generation into the operating model

Over half (52%) of trials (see Figure 3) are now dedicated to gathering evidence for digital health, encompassing digital health interventions’ clinical usability, functional effectiveness and outcomes. In practice, it’s been challenging to adapt pharma companies’ clinical organisations that have been wired for drug development. We’ve seen fragmented efforts, duplicated responsibilities across departments and overdesigned studies. To overcome obstacles like these, we recommend redefining pharma digital health operating models to integrate the right expertise, roles, governance and quality management systems (QMS). Companies aspiring to seamlessly integrate digital health into their standard practices need to carry out a comprehensive self-assessment. This includes:

Understanding portfolio and DH ambition: Evaluate the value brought by DHT implementations across the portfolio (see Recommendation 4). Align the integrated evidence plans of current investigations (see Recommendation 1).
Assessing the current approach: Analyse how the organisation supports DHT evidence generation. Identify strengths, weaknesses and potential areas for improvement. Typical pitfalls include misalignment between different programs and limited alignment between different functions (e.g. market access, medical, commercial). A key focus needs to be on future readiness to enable digital health programs to scale.
Revising and adapting: Establish a long-term operating model incorporating vital roles, expertise, governance and processes for robust DHT evidence generation and value creation. This model should include dedicated evidence generation lead roles with expertise in evidence requirements from different stakeholders. Evidence generation leads need to be empowered to influence the evidence plan of both the drug asset and the DHT.

Leverage remotely connected data to address stakeholder needs

Our analysis shows that remote patient monitoring is by far the most prominent use case (Figure 6). This reflects an increasing trend to enrich investigations with real-life patient data. However, it’s essential to link the data to a clear use. Companies need to explore if and how such remotely collected data is becoming a stakeholder demand, e.g. to inform physician decision-making, support payer discussions or if it will be required in regulatory submissions. The specific use will determine the quality, type and quantity of data needed. In most cases, the raw data will have to be enriched or transformed into insights in the form of clinical decision support tools that also require validation. Such considerations need to be founded in a clinical data strategy.

Understand the evolving digital health landscape for each therapeutic area but make use of synergies to build a cross-therapy area portfolio

While remote monitoring is the most common use case, understanding the current development in each indication is important to anticipate future market needs. Different therapeutic areas present unique opportunities for digital health solutions to bring value in the development or use of drugs. By mapping the prevalence of various digital health use cases in different therapeutic areas we can understand where pharma companies are focusing (see Figure 6).

For example, in areas with complex decision-making with either multimorbid patients or unspecific symptoms like cardiovascular health or neurology, we expect increasing demand from physicians for data-driven tools to support their diagnosis and treatment decisions. Companies should then elevate these in-depth assessments to the portfolio level to identify opportunities for scaling across drug programs and therapeutic areas. This will also help define priority areas for investment while recognising differences between therapy areas.

Figure 6: Prevalence of digital health use-cases within trials of the top 15 pharma companes and relative higher or lower prevalence by therapeutic area

How to put a digital health strategy into practice

DHT has enormous potential to transform care delivery and augment traditional drug-based treatments. However, it’s a significant change requiring a comprehensive rethink of processes from evidence generation to go-to-market.

At PwC we work across functions to help our clients along this journey. Take a look here for more insights about how to develop and commercialise digital health solutions and selected case studies where we’ve helped companies to define a commercial and regulatory roadmap for a SaMD software or build a go-to-market strategy for a digital health solution.

Please reach out if you have any questions about your next steps, you want to discuss our findings or take a deeper dive into the data.

Research methodology

Clinical trials with DHT we identified based on clinicaltria.gov data based on a modified method from Marra et al. 2020 and Masanneck et al. 2023.

A search syntax was applied (see below) and results were filtered for the largest 15 pharma companies. Identified studies: 315.
Results were manually reviewed to exclude trials where keywords are used in a different context (e.g. android obesity doesn't refer to the operating system) or the DHT isn't a central enabler to data-driven or remote healthcare delivery (e.g. if an iPad is used by clinical research staff to record data). Remaining studies: 269.
Additional labelling was applied to the remaining studies (e.g. use case. Therapy area grouping etc.).

Keywords included in search syntax

digital health solutions, wearable, digital solution, digital technology, digital biomarker, mobile application, smartphone, artificial intelligence, remote monitoring, smart device, app (only searched within brief summary), Mhealth (only specific term, not concept), iPad, Fitbit, android, iPhone, Garmin, machine learning, actiograph, accelerometer.

Source
Marra, C., Chen, J.L., Coravos, A. et al. npj Digit. Med. 3, 50 (2020).
Masanneck, L., Gieseler, P., Gordon, W.J. et al. npj Digit. Med. 6, 23 (2023).