An mHealth application framework to achieve personalized health integrating n-of-1 clinical trial with digital twins

Abstract

The past few decades have marked a technological upheaval of the entire healthcare industry. The mandate to transition from paper-based medical records to electronic-based records has digitized patients’ entire medical histories, acting as a pivotal catalyst for ushering healthcare into the digital era. The explosive growth in digital health continues to be fueled by technology-driven solutions including mHealth, telemedicine, electronic health records (EHRs), AI-powered diagnostics, and health data analytics. Digital health includes any digital tool or platform used to enhance healthcare delivery and management. Armed with an arsenal of copious data, the transition from population-based health practices to personalized health is starting to take hold. Traditional evidence-based medicine and its key concepts of randomization and generalizability are the backbone of advancing medical treatments. However, the very pillars of randomized clinical trials that support population health contradict the aim of personalized medicine. Traditional randomized clinical trials are not viable approaches for personalized medicine, especially given that clinical trials often fail to meet enrollment criteria and face a dearth of participant diversity. Recent emerging trends and technologies have evolved to support and allow a more personalized approach to medicine. Patients with chronic conditions may benefit the most from or experience an improvement in the understanding of their condition by participating in a different type of evidenced-based practice - specialized n-of-1 clinical trials. This paper aims to examine digital health trends that have shaped the landscape to view n-of-1 clinical trials as an attractive alternative. The rise in the use of artificial intelligence, machine learning, and digital twins has allowed for a more granular look at an individual patient. This paper explores and provides a comprehensive framework for building a digital health application designed to support n-of-1 clinical trials integrated with digital twins technology and incorporating AI/ML components as a mechanism to achieve personalized medicine.