Contributed Commentary by Marie McCarthy

May 15, 2019 | Between 1995 and 2015, pharmaceutical R&D spending nearly quadrupled, while the rate of FDA-approved drugs has stayed roughly the same (Pharmaceutical Market Europe). To compound the issue, R&D returns have diminished by nearly 7% between 2010 and 2017 (Deloitte). In other words, sponsors are spending more to get the same results. And the cause? Oftentimes, poor trial design.

Digital Health is emerging as a solution to these challenges. By incorporating technologies such as AI, big data, wearables, sensors, and smartphone apps, Digital Health has the potential to improve trial design from start-up to close-out, increase R&D productivity, and provide the data that sponsors need for better decision-making.

Pulling Clinical Trials Into The Digital World

Many challenges have arisen from traditional clinical trial designs, including inadequate protocol design, slow enrolment, poor site performance and low patient engagement and retention. As a result, this has often led to extended trial timelines and increased costs.

However, in conjunction with predictive analytics, the use of big data from various sources—including historical trial data, lab tests, drug prescriptions, electronic health records, and patient input—has the potential to increase trial efficiency by reducing enrolment and retention issues, and time-consuming amendments. Also, big data can influence the types of data collected, enabling more innovative trials designs, such as incorporating meteorological data in a COPD trial to determine whether exacerbations are caused by atmospheric conditions. According to recent research, digitization will fuel one-third of the growth and approximately 40% of the profitability in the pharmaceutical market by 2020 (Accenture).

Taking A Digital Health Approach To Study Start-up

Information gathered from real-world evidence can enhance the design of protocols and methods, in addition to predicting clinical trial outcomes. Insights from this data allow sponsors to consider patient needs before even enrolling the first patient. Considering input from patients during study design can increase participant recruitment and retention—both major drivers of clinical trial costs.

At trial start-up, innovative digital strategies can optimize site identification and engagement. During site selection, insights from digital technologies, AI, and big data can help identify investigators, localities, or clinics with a high occurrence of target patient populations versus underperforming sites to determine the most optimal trial site for patient recruitment. Upon site selection, web-based portals can then be utilized to quickly deliver study-related content to sites and investigators, and to improve overall site management and sponsor interaction.

Other technologies that can speed study start-up include digital tools for screening, and consent and cohort randomization, which can increase patient understanding and compliance during a trial. Leveraging predictive analytics and big data can improve protocol design and lead to expanded access to patient populations and accelerated patient enrolment.

Digitalization Beyond Data

Once a clinical trial is underway, big data can guide the design of smartphone apps and web interfaces that streamline study data to ensure that necessary milestones are on schedule. Throughout a study, Digital technologies allow for direct data capture, providing access to real-time data collection.

Wearable and mobile sensors are increasingly used as a means of gathering relevant data about patients’ daily activities, including steps taken, sleep patterns, and heart rate. Ongoing interim data analyses can lead to early insights and data trends.

In addition, the use of wearables and sensors allows sites to closely monitor patients for protocol compliance and adherence. Further, machine learning can be used to develop algorithms that can utilize data from sensors and can enhance the accuracy of the study data. For example, analysis from sensitive motion data collected can demonstrate whether a patient has opened his or her medicine bottle. Over time, the data can determine whether the patient or someone else handled the dispenser. These compliance-related applications are critical, since they can decrease early patient withdrawals and reduce patient retention issues.

Digital health can also solve patient transportation issues, which greatly impacts trial success. In a recent survey, travel burden was cited by 79% of respondents as a barrier for clinical research participation (Pharmaceutical Market Europe). Through telemedicine, ePRO apps, and sensor data, patients can be connected to trial supervisors remotely, greatly reducing the need for patient travel, along with associated costs and inconveniences. Additionally, integrating travel arrangements and reimbursement into apps can streamline this process, ultimately encouraging participation.

While the increased use of various digital technologies can lead to a burden of multiple touch points for patients and sponsors, taking a platform approach that creates a single point of access can reduce burdens, enabling better site management and patient experience.

Leveraging Technology To Accelerate Study Close-Out   

The constant data stream from patients’ mobile devices and sensors generate large datasets that are difficult to analyze manually—just one motion sensor gathering data from a few dozen patients creates nearly one billion data points per day (ICON).

To understand these large datasets, AI and machine learning are necessary to automate analyses. Incorporating this technology can lead to faster and more transparent access to data. In addition, it can help sponsors collect the right data to support submission and filing for regulatory review, and, in the end, speed a product’s path to approval and reduce time to market.

The Future Is Digital

The healthcare industry has fallen behind other industries in the digital technology era. To stay afloat in an increasingly competitive market, companies will need to develop the ability to use digital health to improve R&D productivity. Whether it is through automating repetitive tasks or employing machine-learning algorithms for informed decision-making and cost reduction, digital health tools an assist across all aspects of a study. Further, incorporating these technologies into trials early in the process is essential to clinical trial success, which is increasingly dependent on patient retention and engagement.

Current integrations of digital health into clinical trials barely scratch the surface of its potential—incorporating other technologies such as blockchain, virtual trials, and digital biomarkers are on the horizon. Looking forward, more companies will invest in AI and software developers to uncover additional insights and bring more value to health data, further increasing efficiency in R&D.

Marie McCarthy is part of the multidisciplinary Innovation Team at ICON plc. Seen as a key Intrapreneur within the organization she has specific responsibility for developing solutions in the direct to patient paradigm. Her focus is primarily on the use of wearables and sensors in clinical trials, with particular emphasis on the potential these devices have to monitor the physical behaviors of the digital patient. She can be reached at Marie.McCarthy@iconplc.com.