# Challenges and potential of using digital biomarkers in healthcare and clinical trials

## TL;DR

This paper explores how digital biomarkers can improve healthcare through continuous monitoring and personalized medicine, while addressing challenges in their adoption.

## Contribution

The paper proposes a framework to overcome barriers in translating digital biomarkers into clinical practice through harmonized pathways and adaptive evidence loops.

## Key findings

- Digital biomarkers are being used in real-world scenarios like glucose tracking and heart rhythm monitoring.
- Barriers include high costs, redundant regulatory reviews, and data silos.
- A proposed solution involves harmonized qualification and adaptive post-market evidence loops.

## Abstract

Digital biomarkers use sensors and analytics, to offer continuous monitoring and personalized medicine. In this Perspective, we describe real-world use cases, such as glucose tracking to optimise insulin dosing and wearables to measure heart rhythms to de-risk cardiovascular trials. We also discuss the issues preventing most candidate biomarkers from reaching clinical practice. Evidence generation is costly, regulatory reviews can be redundant, commercial incentives fall short, and data silos can be biased and/or nonrepresentative. By combining harmonized qualification pathways, value-based reimbursement, modular extensions for single-trial biomarkers, and adaptive post-market evidence loops, we propose a path from experimental signal to standard of care.

Lieberwirth et al. discuss the current landscape of digital biomarkers in healthcare and clinical trials, highlighting growth in the use of digital endpoints and the emergence of dedicated “digital biomarker” startups. They discuss key barriers to translation and outline practical priorities for wider adoption.