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