Bench International

A Pathway to Deploy Digital Health Technologies in Life Sciences

A Pathway to Deploy Digital Health Technologies in Life Sciences

Insight by: Jon Warner

Digital technologies of many kinds are opening new opportunities for life sciences companies to add value in using them for faster and better research, more efficient clinical development, commercialization, and more effective clinician and patient engagement in clinical trials and beyond. These digital tools, systems and platforms, including apps, devices, wearables, telehealth platforms, remote patient monitoring (RPM) systems, and more, enable pharma, biotech, device, and diagnostics companies to both offer information and education to clinicians and patients and to collect real world data to help to better understand the health characteristics of different populations of people.

Using digital tools to collect more real-world data can significantly reduce costs and time to market in life sciences companies by helping to accelerate evidence generation, refining development pathways, and defining the effects of specific therapies. This can also dramatically increase the opportunity to enhance person-centric care -a key goal in life sciences companies in recent years, and even lay the foundations for greater precision medicine.

If life sciences companies want to increase the use of digital tools, particularly when conducting clinical trials, it is important to recognize that effective digital patient monitoring systems need to be put in place (often sensors, wearable and smartphone tracking for example) and that data capture systems are properly established to then analyze results and start to look for new and interesting insights (HIPAA compliant cloud-based storage, for example). This means that life sciences companies must choose the right devices, deploy them among the right users, and have the skilled and/or training staff to make sense of the data sets. Although there is no ‘one-size-fit-all’ way to do this, we would suggest that a useful 4 -step approach is as follows:

A) Identify the team to manage the digital technology and data. Increasingly, life sciences companies have staff who have knowledge and experience in digital health tools and solutions. However, where there are gaps, specialist knowledgeable staff may need to be hired or internal staff trained. Another alternative is to have outside advisers and vendors supervise early projects over a limited time frame to educate internal staff as needed.

B)  Find and deploy a fit-for-purpose digital technology solution.The first consideration in terms of a fit-for-purpose solution, targeted to its user, captures data at the right level of accuracy, quality, and relevance (and in a way that meets safety and security standards). This means the first step here is clearly to define the data that is desired and then how this will be integrated and drawn upon during and at the end of the trial. A sensor-based technology, whether a wearable or other item, must therefore be capable of recording the right data in the right place and at the right time. The second consideration in deploying a digital solution is to carefully determine who will use it. This often is best done by evolving a model for the behavioral, clinical, economic, locational, relational, and social factors that may affect users’ ability and willingness to wear or have close to them a sensor-based technology or digital health tool. Understanding how these factors impact attitudes and behavior (and positive or negative adherence) creates a basis for defining cohorts and any personalized engagement necessary. Thirdly, after the right digital health system has been identified, training is often essential for successful deployment. If the chosen technology is poorly understood or difficult to use, adherence will suffer, and the value of the technology may be undermined or lost. Instructions for use should therefore be simple, direct, and empowering.

C) Assemble individual data inputs in a wider cohort. The main benefit of deploying digital health technology is the ability to capture substantial information over time using a modest sample size and also the ability to diversify that sample to reflect an affected population. However, it must be recognized that single or individual data sets are not sufficient to draw conclusions. Digital health technologies provide a means of collecting data from multiple single data points and then aggregating them into a wider cohort, thereby allowing for statistically significant patterns to emerge. An effective digital solution will then augment and complement finite data from standard clinical trials, registries, and electronic medical records to create a comprehensive understanding of the factors that affect outcomes—as well as the variations in factors that potentially influence outcomes across cohorts over time.

D) Clearly show the therapeutic value.  The end of a part or full virtual trial using digital tools or solutions to assist, should refine understanding about the effects of interventions and outcomes among specific cohorts. Life sciences companies should therefore consider integrating data such as standard clinical trial outcomes, claims, and medical records in a scalable platform that supports assessing the value and risks of therapeutic treatments over the long term. Analyses may also include applying information derived from digital health technologies to improve knowledge and awareness about the effects of social, economic, and environmental factors on clinical outcomes (the co-called ‘social determinants of health’), which can make a real impact in clinical practice. Evaluating these factors can help clinicians and researchers identify co-factors for adherence, adoption, risk, and success that promote personalized approaches to overall care.


The purpose in this brief article is to suggest that there is considerable scope to deploy digital health tools and solutions to significantly reduce research costs and the overall time to market in life sciences companies by using digital technology (such as RPM) to help to accelerate evidence generation, refine development pathways, and define more effective therapies.