Digital intelligence for the clinical trial: How the IoMT is streamlining clinical operations

Wearable sensors, activity trackers, and mobile apps are being implemented in novel ways to collect data in clinical trials, transforming operations and improving the patient experience. 

These connected devices within the healthcare industry, also known as the Internet of Medical Things (IoMT), are being applied across all phases of clinical research, including trials sponsored by pharmaceutical and medical device companies, government bodies and nonprofit organisations. According to a recent report conducted by the Harvard Business School, in 2017, more than 1,000 trials launched that used at least one connected digital tool, representing a more than a ten-fold increase over the same count in the early 2000s ⁽¹⁾. 

Industry stakeholders are acknowledging the IoMT’s potential to streamline clinical operations. Forty-three percent of medtech companies are using real world evidence (RWE) to drive business decisions, demonstrating the potential of the IoMT to influence industry business models ⁽²⁾. RWE produced by the IoMT supports trial safety, early decision-making and adaptive changes, which can all accelerate development. 

Informing study design and protocols with the IoMT

Study designs that include electronic health records can offer potential participants better access to trials. Using a trial matching system can increase enrollment, accelerating study start up. In addition, EHRs can lower the amount of amendments to trials, therefore increasing trial efficiency. Moreover, EHR informs feasibility and study design by creating protocols that meet the needs of patients and sponsors, and enabling the creation of combined-stage adaptive studies. 

Using automated data collection and monitoring can generate RWE, which in turn can supplement protocol feasibility assessment and site/physician identification. The data collected can be applied to simulations and external control arms as a way to predict the safety and efficacy of proposed therapies for future trials. RWE can also inform go/no-go decisions. 

Accelerating development with RWE and big data

Harnessing the IoMT in clinical trials allows for data to be collected and transmitted into cloud databases, where artificial intelligence (AI)-driven platforms analyse data in real time. Gathering real-time behavioural and physiological data from patients can form the basis for clinically relevant insights. An example is the creation of digital biomarkers, which can help explain or predict clinical outcomes in clinical trial design.

Incorporating a digital, cloud-based platform can provide automatic collection of consistent, unbiased data and remote monitoring for data analysis. Further, the constant data stream from continuous monitoring provides valuable insights into the safety and efficacy of an experimental drug in a real-world environment, accelerating regulatory approval. Using data for these purposes can reduce or eliminate the need to enroll control participants, increasing efficiency, reducing delays, lowering trial costs and speeding therapies to market. 

While the volume of data from a variety of sources can cause its collection to be fragmented, AI-enabled software can bridge data silos and facilitate analytics. 

Improving the patient experience with the IoMT

Incorporating the IoMT within clinical trial design has the potential to improve the patient experience of participating in studies. Patient-centric technologies include electronic informed consent, at-home digital monitoring and virtual visits.

Using electronic informed consent enhances patient comprehension of the commitments of joining a study, offering greater patient protection. When patients understand their participation in a clinical trial, it empowers them to make informed decisions and have more ownership of the consent discussion. This structure also increases a patient’s trust in his or her clinician and the study. 

The IoMT also supports patient-centricity by informing protocol adherence and helping patients manage medications, perform structured tests and report symptoms. Also, mobile apps can enhance convenience for patients when reporting electronic diaries and outcomes. Transmitting data to a secure cloud platform for storage and analysis allows for machine learning and other AI methods to assess and quantify the impact of therapies. Identifying drug dose response or any safety or efficacy issues allows the sponsor to adapt protocols to mitigate risks, ensuring the safety of the patient.

The connectivity and interoperability of the IoMT can facilitate remote patient monitoring, enabling virtual trials. In a virtual trial, participants no longer have to live in close proximity of, or travel to, a centralised research site. Whether using a hybrid model, or a fully virtual trial, when patients only visit sites when required, trial participants experience less interruptions in their day-to-day responsibilities. Improving the patient experience, increases study retention and patient adherence. 

Choosing the right partnership 

While integrating the IoMT into clinical trials can be costly and requires standardisation, adopting a partner with expertise in data science, management and analytics could provide the necessary skills for success. The integration of connected digital tools and their growing use in clinical trials holds great promise for improving the study experience for patients and other stakeholders, while streamlining clinical operations.

To learn more about how the IoMT can make clinical trials more efficient, effective and patient-centric contact our experts today.

References:

1. Stern, Ariel Dora and Marra, Caroline. The growing role of connected tech in medical research. (2019). Digital Initiative. Harvard Business School. https://digital.hbs.edu/data-and-analysis/the-growing-role-of-connected-tech-in-medical-research/
2. MarketsandMarkets. (2018). IoT Medical Devices Market Global Forecast to 2023.