Digital biomarkers

Navigating the path from ideation to impact through deep phenotyping in precision medicine

Dr Dylan Powell

United Kingdom

Douglas McKinnell


Sebastien Burnett

United Kingdom

Sam Talibi

United Kingdom

Digital measurements – enabling a new era of deep phenotyping

Health, according to the World Health Organisation, is a dynamic state of well-being across physical, mental, and social domains, not just the absence of disease.1 It is a state shaped by the interplay of disease (the underlying pathophysiology), illness (the manifestation of symptoms) and subjective experience.2

Our understanding of health has evolved from clinical techniques linking signs and symptoms to disease phenotypes – the observable constellation of characteristics and traits – towards a nuanced recognition of their underlying endotypes: the principal mechanisms explaining the expression of disease in a cohort of patients.3

Digital measurements offer an objective way to capture deep phenotypes of health and wellness, as well as deviations from this such as illness, drug response, and risk. Combining these digital measurements with other data modalities such as bio samples (blood, histopathology, etc), imaging, and diverse types of ‘omic’ data, termed multi-omics, will usher in a new era in precision medicine underpinned by deep phenotyping and endotyping.4

As we stand at the threshold of this digital revolution in health care, life sciences leaders are confronted by both immense opportunities and significant challenges. There must be a concerted effort to orchestrate a comprehensive digital measurements strategy, assure regulatory compliance and demonstrate financial and user value and viability. These challenges must be successfully thought through to realise the operational and clinical benefits the new tools can bring to the entire value chain.

What are the opportunities offered by digital biomarkers and other digital measurements?

The advent of digital signals data has enabled its advocates to develop digital measurements that create objective, quantifiable, physiological, behavioural, and environmental measures. These measurements include:

  1. Digital biomarkers – digitally collected (set of) characteristic(s) measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions.
  2. Digital endpoints – digitally captured events or outcomes that can be measured objectively to determine whether the intervention being studied is beneficial.
  3. Digital clinical outcome assessments – digitally delivered instruments used to measure clinical outcomes, with instructions for participants, scoring models, and protocols for administration.5

Here we identify and highlight four key areas where adopting digital measurements can bring significant value across the biopharmaceutical value chain.

Enhancing clinical trials and post-market surveillance

Digital measurements can reshape clinical trials and post-market surveillance by providing more accurate patient-centric metrics. Traditional trial procedures can be streamlined using these novel methods while also reducing trial site variance and enabling standardised, remote data collection with richer impact. This means that trials can be more efficient, with reduced sample sizes. Servais et al provide an example, estimating that the required pivotal trial sample size in their Duchenne study would be reduced by 70% compared to using the traditional 6-minute walk test or North Star Ambulatory Assessment as the primary endpoint.6

Digital measurements allow the capture of metrics that can be both more meaningful and user-friendly for patients. These measurements can be more sensitive to and reflective of treatment response, enabling a better understanding of disease progression and dynamics. They therefore have the potential to replace traditional time-consuming assessments that are at times difficult for patients – for example, the 6-minute walk test (6MWT), which has been the gold standard in cardiopulmonary assessment within clinical trials and offer more nuanced ways to evaluate drug efficacy and safety.7

Digital measurements can enable cost-effective capture of (continuous) real-world data, providing deeper and richer insights into patients and their environment. While this technological shift may introduce risks to clinical trials if not used appropriately, the long-term benefits include increased patient relevance, accessibility, and depth of insight. Making this transition is a compelling investment for life sciences leaders given the long-term benefits. Clinical and development teams should be brought in early to ensure they are content with how digital endpoints can be effectively and safely developed, assessed, and adopted within their trials to minimise risk to patients and ensure success.

Driving innovation and precision

Digital measurements provide deep phenotyping data to enhance the understanding of disease pathophysiology and, subsequently, refine patient stratification, facilitating innovative clinical trial designs.

The accessibility of real-time, real-world data facilitates personalised treatment plans, the monitoring of treatment responses and rapid therapeutic adjustments. Digital measurements are instrumental to the creation of predictive measures for preventive health care. By continuously monitoring health we can identify early signs of disease, responses to treatments, and risk factors for deterioration. For example, research conducted into Parkinson’s Disease (PD) demonstrated that it is possible to automatically discriminate PD from healthy controls at an early stage via handcrafted features extracted from speech.8 The European Medicines Agency (EMA) have also recently qualified Stride Velocity 95th centile (SV95C) as the primary endpoint in superiority studies, as an alternative to the 6 Minute Walking Test.

Digital measurements are not just an auxiliary tool but a critical component of the quest to drive innovation and precision in health care. These tools are paving the way for an era of truly personalised, preventive medicine.

Activate, engage and empower patients

Where traditionally patients have been passive recipients in the clinical trial framework, the adoption of digital measurements provides the opportunity to empower them to become active participants. Patients can use these tools to develop a greater understanding of their health and well-being, helping to enhance their involvement in and adherence to the treatment they are receiving. Life sciences organisations can adopt digital measurements to widen their trials’ demographic reach and, in this way, reduce selection bias by engaging more diverse and remote populations.

Success in promoting these forms of measurement will depend on educating people within your life sciences or health care organisation, key opinion leaders in the community (such as knowledge experts and patient advocacy groups), contract research organisations (CROs), health care professionals, and patients about the value of the new tools. A relationship based on trust and understanding can thereby be cultivated, providing a solid foundation for the digital future in which adoption of digital measurements re-defines clinical trials, opening up new avenues for innovation, precision, and inclusivity.

Improve operational efficiency and compliance

Digital measurements can streamline trial data management, reducing errors and inefficiencies. By leveraging AI and rule-based algorithms within a robust digital infrastructure, governed by FAIR (Findable, Accessible, Interoperable, Reusable and Reproducible) data principles, real-time data flow becomes feasible, automating labour-intensive tasks and reducing manual time and effort in data wrangling. The alignment of digital data infrastructures with standardised data models can support data re-use initiatives, cross-study analytics, and regulatory compliance.

For compliance purposes it is important that leaders engage with regulatory, privacy, security, and legal teams, ensuring they are included by design.

Challenges to the adoption of digital measurements

A unified strategy and approach is needed

The potential for digital measurements in the life sciences has been recognised by some segments of the industry for over a decade. However, the path to widespread adoption is going to be challenging unless there is an integrated strategy and methodology across organisations.

Within organisations the approach to adoption is frequently disjointed. Isolated efforts from different departments delay development and create significant inefficiencies through duplication and lack of shared knowledge. Siloed behaviour often stems from concerns about maintaining departmental and budgetary autonomy, or an overcautious approach to risk, and general lack of communication. Without holistic, forward thinking from leadership, fragmentation and duplicated efforts will persist.

It is essential for leaders to critically evaluate industry and cross-industry trends to understand what the best practices are and where they can be introduced. Organisations should aim to learn from others’ mistakes and successes. It is vitally important to understand the current state of your organisation, address any silos within it, understand what people on the ground are focusing on, and see where lessons can be learned.

This evaluation should provide a basis for a comprehensive vision and strategy that can mobilise teams toward a unified goal: focused advancement of digital measurements within your organisation.

Foundational architecture and infrastructure

A current challenge facing life sciences organisations is the lack of modern enterprise architecture suitable for multi-modal data. Life sciences organisations need a framework that helps them align on business, data, and technology to achieve their multi-modal data goals.

Organisations must adapt their business architecture to include digital measurements. They should focus on unifying business processes, governance guidelines, the structure of the organisation, and business strategy in a way that reflects their current and future digital measurements needs – defining where they want to play and subsequently the specific process, governance, and approach required to achieve success.

Robust technology architecture is needed to facilitate efficient interaction between the diverse systems and tools within the space (a by-product of digital measurements). It should help your organisation map out what it needs to: succeed with digital measurements (sensors, wearables, implantable, etc); which technologies are missing and need to be built or bought, or where the technologies are present but have room for improvement. It should reflect how these different pieces will fit together in an interoperable and cohesive manner.

Finally, organisations must establish proficient data architecture to ensure a clear data strategy as well as appropriate and efficient management, processing, and governance of data. Factors such as data volume, velocity, and variety should be considered in the design to ensure scalability and flexibility. Methodologies like DevOps and MLOps can accelerate the development and deployment of digital measurements while maintaining compliance and system and data integrity and reliability. Life sciences leaders can accelerate progress by utilising their experience of setting up infrastructures for other data modalities.

Navigating the regulatory environment and compliance

Navigating the regulatory landscape for digital measurements in the life sciences is notoriously challenging, especially given the variations at local, national, and international levels. Regulatory considerations encompass a vast range of elements, from the digital health technologies themselves (software and hardware), to enabling functions such as AI, bias, data security, and privacy.

For seasoned life sciences leaders, certain regulations might be familiar, while others – particularly those relating to AI and data – pose unfamiliar challenges. The complexity of these regulations often leaves many organisations struggling to chart a clear course.

Regulators, including in the US and Europe, have started issuing draft guidance, frameworks, and Q&As, to address the use of digital health technologies within clinical trials. Some notable examples include “Digital Health Technologies for Remote Data Acquisition in Clinical Investigations”9 from the Food and Drug Administration (FDA) in the US and the “Guideline on Computerised Systems and Electronic Data in Clinical Trials”10 from the EU. Industry communities such as the Digital Medicine Society (DiME) and the Clinical Trials Transformation Initiative (CTTI) provide an array of playbooks and guidance documents that constitute much of the best current support on navigating the regulatory environment.

The lack of trained professionals who understand the intricacies of these new regulations and how to apply them to novel technologies presents a significant challenge. But regulators and health technology assessment (HTA) bodies are eager to collaborate and the number of experts knowledgeable in this field is growing.

Meaningful development, underpinned by evidence, verification, and validation

As with any new product, it is important to understand the problem being addressed. Engagement with a range of internal and external stakeholders, to truly understand the ins and outs of the current problems and opportunities, is therefore essential to long-term success. For patients, digital measurements should relate to aspects of their health in a meaningful way. For clinical and development teams the concept of interest (a simplified/narrowed element of a meaningful aspect of health that can be practically measured) should be relevant, useful, and informative. Finally, the outcome that is measured should be feasible i.e. technologically appropriate, cost-effective, scalable, and user-friendly. A cross-functional approach across internal and external stakeholders will be vital, from the conception of the idea all the way through the continuous product development lifecycle.

A cornerstone in the adoption of digital measurements is building a reliable evidence base to demonstrate utility, accuracy, and reliability. This process demands significant time, financial investment and continuous commitment, not just a one-off study.

The V3 framework developed by DiME provides a cohesive methodology for defining, measuring, and evaluating digital measurements.11 The evaluation process doesn't end with the initial verification and validation. Technological and algorithmic advances demand continuous verification and validation, again reflecting the iterative nature of the continuous product lifecycle.

Critical to the evidence generation process is recognising the diversity of patient cohorts and of the populations that make them up; a reflective cohort should be consulted in the design and approach to data collection. This may be more costly and time-consuming in the short term but will reduce the risk of bias and less effective digital measurements in the long term. This approach is also directly in line with the U.S. Food and Drug Administration’s new diversity plan requirement to ensure appropriate representation of traditionally underrepresented populations.12 It is vital to meticulously assess potential biases in data collection and algorithmic processing which could exacerbate health disparities among marginalised groups. Using biased data will result in biased measurements.

People and expertise

The adoption and advancement of digital measurements requires a multidisciplinary skill set. Cross-functional teams should receive inputs from many sources, including those with expertise across clinical, sensor technologies, data science, artificial intelligence and machine learning, product development, engineering, and data privacy and security. The integration of digital signals data with other data sets, such as omics, imaging, or clinical data, requires bioinformaticians and imaging experts to maintain high data quality standards and a high level of interoperability in current as well as prospective digital infrastructure.

Given the sensitive nature of health data, understanding and adhering to security, privacy, and ethical guidelines, laws and regulations is paramount. Compliance with local and international security and privacy norms, as well as maintaining encryption and anonymisation standards, ensures responsible data management, crucial for organisational reputation and trust. These are complex issues that should not be overlooked in the design of your strategy, processes, and products.

Change management

The evolution of digital endpoints requires a dynamic approach that often conflicts with established approaches within life sciences organisations. Therefore, a paradigm shift away from classical ways of working must be adopted to ensure their success.

Historically, life sciences organisations operate within rigid frameworks guided by intensive regulation, lengthy development cycles, and traditional drug models of success. These ingrained behaviours can lead to resistance against the agile, iterative, data-centric approach, necessary for digital transformation – although this is becoming less of a burden as life sciences organisations become more broadly data-driven.

Investment returns must be re-evaluated. The high upfront costs of digital initiatives, which enhance and achieve savings in the existing portfolio rather than generate net-new revenue streams, can be challenging for the risk-averse life sciences industry. However, these should be seen as long-term investments whose value lies in: improving patient outcomes and enhancing data collection, the return on biopharmaceutical investment, and health leadership.

Go-to-market and commercialisation

Digital measurements can deliver long-term value to life sciences organisations. Costs can be reduced through operational efficiencies, such as the time saved through automation of data ingestion and wrangling and reduced overheads from trials and staff. Value can, in addition, be drawn from new insights that guide research, and early development to improve the return on investment across R&D.

One thing not currently clear is the long-term potential for the commercialisation of digital measurements. Life sciences leaders should acknowledge this in their vision and strategy and determine whether they want to be at the forefront of new commercial models for digital measurements or run the risk of allowing others to play the pioneering role, seizing the first mover advantage within the life sciences market. It will all come down to deciding where value and the risk-reward balance is seen in individual organisations.

Bridging the gap: future perspectives

Life sciences leaders have a significant opportunity to bridge the gap between the potential and actual value achieved from digital measurements within their organisations. The potential benefits of digital measurements are vast but adoption and implementation have so far been limited. To bridge this gap, life sciences leaders need to take the following steps:

  • Craft a solid vision and strategy: Leaders at all levels should begin by mapping out where their organisation’s role lies or will lie within the digital measurements space. This should involve a careful assessment of existing capabilities, areas of excellence, and potential growth sectors. Do you aim to pioneer the development of novel digital biomarkers or leverage existing technologies to enhance patient care and clinical trials? Your strategic position will dictate your path and therefore requires careful consideration. Reflect on your current internal and external position and where you want to be in two years’ time. Define your long-term goals, interim milestones, and potential risks and mitigation strategies related to the development or application of digital measurements.
  • Define success in concrete terms: Be specific. What does winning in digital measurements look like for your organisation? This step should involve setting specific performance indicators such as achieving patient engagement rates, or successfully validating and implementing a given number of digital biomarkers and endpoints within a set timeframe. Other metrics could relate to broader strategic goals such as earning recognition as an industry leader or gaining a certain market share of digital health within a therapeutic area. Ensure your metrics of success include both short-term, achievable targets, and long-term strategic goals. They should be quantifiable and tied to key areas of your digital measurements. These success metrics will serve as the guiding star for your journey and will provide a framework to measure progress, identify areas for improvement, and celebrate successes.
  • Assess “Build, Buy, or Partner” approaches: Determining whether to build capabilities in-house, buy/acquire relevant technologies and businesses, or enter strategic partnerships is critical. This decision will be informed by your assessment of your current resources and capabilities, and the speed at which you want to gain market presence. Building in-house could provide complete control over the process but initially is time-consuming and requires significant investments in infrastructure and talent. Buying or acquiring can help you quickly gain advanced capabilities and an established market presence but may pose integration challenges; it also demands thorough due diligence – consider the number of failed start-ups. Partnerships could provide a balance, enabling you to leverage external expertise while maintaining some control over the process and sharing the risks and rewards. These decisions must align with your broader strategic objectives, risk tolerance, and available resources. You will need to regularly reassess your approach as your organisation evolves and the digital health landscape changes.
  • Fill the gap and invest in talent: Once you have defined your strategy and identified your opportunities and where you are lacking, you will need to focus on talent acquisition and upskilling efforts to strengthen your workforce cross-functionally. Implementing and adopting digital measurements across your organisation requires a diverse set of skills, spanning several domains – not just technical expertise such as data science, AI, and bioinformatics, but also understanding of clinical procedures, the regulatory environment, and commercialisation strategies specific to digital health. You will therefore need to develop clear job descriptions, identifying key skill sets, and establishing training programmes to bridge any knowledge gaps. Promoting this multidisciplinary approach will enhance the effectiveness of your strategy and better position your organisation to navigate the complexities in this field.
  • Leverage industry learnings: This is a rapidly evolving field where many will fail fast. By monitoring market movements and developments across technology, commercialisation, and regulation, you can learn from others’ mistakes, but also their successes, to help guide your own choices and decisions. Implement mechanisms that enable routine review of market advances, legal changes, and competitor activities. Use these insights to inform your own strategy, either by identifying emerging trends or recognising missteps to avoid.
  • Empower and nurture experts to drive innovative transformation: Successful adoption of digital measurements requires not just the application of new technologies but also a mindset change. Identify talented leaders and teams and enable them to drive this transition to new ways of thinking and working. Leaders must promote a culture that embraces technological advances and encourages experimentation with new ideas and solutions in this space. Acknowledge that innovation involves risks and that occasional failure is part of the process. Celebrate learning and progress, not just success. By empowering your talent, your organisation will more effectively learn to adapt and succeed within the rapidly changing digital health landscape.
  • Embrace collaboration: The success of digital measurements will be dependent on industry-wide agreed approaches and standards with shared learnings. You should therefore support intra- and extra-organisational collaboration. Internally, establish multidisciplinary teams or regular inter-departmental meetings to foster knowledge sharing. Externally, engage in collaborative initiatives, alliances, or consortia to shape industry standards, share best practices, and accelerate innovation in digital measurements. To innovate, look beyond the life sciences for lessons learned and future trends to adopt.
  • Engage stakeholders early: Engage with all stakeholders, including regulators, health care professionals, and patients, from the early stages of your digital measurement initiatives. This may involve establishing advisory boards, conducting focus groups, or soliciting feedback through multiple channels. Your approach should continually be aligned with the needs of your users and consumers: patients, clinicians, CROs, and family members. 

Towards a vision of revolutionised health care

The adoption of digital measurements across life sciences holds immense potential to revolutionise health care. Adoption and integration across the pharma value chain will support the drive to the four Ps of medicine – prediction, prevention, personalisation, and precision – ultimately improving patient outcomes and advancing the field of health and care.

In the long run, investing in digital measurements will support the aim of reducing R&D costs and improving the return on investment. Beyond this, opportunities to expand existing revenue streams and diversify into new ones should be thoroughly explored.

The integration of digital measurements into a multi-modal approach to health and disease is not just a possible future, it is the imminent reality. Forming a clear vision and strong foundations now, while in parallel driving forward with innovative initiatives, is crucial to success. The future of R&D will be reliant on digital building blocks. Now is the time to be bold and proactive. Your actions today will define the health care landscape of tomorrow.

Dr Dylan Powell

United Kingdom

Douglas McKinnell


Sebastien Burnett

United Kingdom

Sam Talibi

United Kingdom


  1. World Health Organization, "WHO remains firmly committed to the principles set out in the preamble to the Constitution," accessed 18 August 2023.

    View in Article
  2. Kenneth M Boyd, "Disease, illness, sickness, health, healing and wholeness: exploring some elusive concepts," Medical Humanities 24, no. 1 (2000): pp.9–17.

    View in Article
  3. Genomics Education Programme, "Phenotype," 7 June 2019; Ioana Agache and Cezmi A Akdis, "Precision medicine and phenotypes, endotypes, genotypes, regiotypes, and theratypes of allergic diseases," Journal of clinical investgation 129, no. 9 (2019): pp. 1493–1503.

    View in Article
  4. Kang Ning and Yuxue Li, Methodologies of Multi-Omics Data Integration and Data Mining (Singapore: Springer, 2023), pp.1–10.

    View in Article
  5. Andrea Coravos, Jennifer C. Goldsack, Daniel R. Karlin, Camille Nebeker, Eric Perakslis, Noah Zimmerman and M. Kelley Erb, Fast Facts: Digital Medicine (2020).

    View in Article
  6. Laurent Servais , Eric Camino, Aude Clement, Craig M McDonald, Jacek Lukawy, Linda P Lowes, Damien Eggenspieler, Francesca Cerreta and Paul Strijbos, "First regulatory qualification of a novel digital endpoint in duchenne muscular dystrophy: A multi-stakeholder perspective on the impact for patients and for drug development in neuromuscular diseases," Digital Biomarker 5, no.2 (2021): pp. 183–190.

    View in Article
  7. J Myers, L Voodi, T Umann and V F Froelicher, "A survey of exercise testing: methods, utilization, interpretation, and safety in the VAHCS," Journal of cardiopulmonary rehabilitation 20, no. 4(2000); pp. 251–8.

    View in Article
  8. Jan Rusz et al., “Speech biomarkers in rapid eye movement sleep behavior disorder and Parkinson disease,” Annals of neurology 90, no. 1 (2021): pp. 62–75.

    View in Article
  9. Food and Drug Administration, Digital health technologies for remote data acquisition in clinical investigations, December 2021.

    View in Article
  10. European Medicines Agency, Guideline on computerised systems and electronic data in clinical trials, 9 March 2023.

    View in Article
  11. Digital Medicine Society, "V3 | Verification, analytical validation, and clinical validation," accessed 18 August 2023.

    View in Article
  12. US Food & Drug, "Diversity plans to improve enrolment of participants from underrepresented racial and ethnic populations in clinical trials; draft guidance for industry; availability," April 2022.

    View in Article


Cover Art by: Mark Milward