Benefit assessment of digital health applications—challenges and opportunities

Abstract

Digital health applications promise to improve patient health and medical care. This analysis provides a brief overview of evidence-based benefit assessment and the challenges to the underlying evidence as prerequisites for optimal patient-oriented decision making. Classical concepts in study design, recent developments, and innovative approaches are described with the aim of highlighting future areas of development in innovative study designs and strategic evaluation concepts for digital health applications. A special focus is on pragmatic study designs. Evidence-based benefit assessment has fundamental requirements and criteria regardless of the type of treatments evaluated. Reliable evidence is essential. Fast, efficient, reliable, and practice-relevant evaluation of digital health applications is not achieved by turning to nonrandomized trials, but rather by better pragmatic randomized trials. They are feasible and combine the characteristics of digital health applications, classical methodological concepts, and new approaches to study conduct. Routinely collected data, low-contact study conduct (remote trials, virtual trials), and digital biomarkers promote useful randomized real-world evidence as solid evidence base for digital health applications. Continuous learning evaluation with randomized designs embedded in routine care is key to sustainable and efficient benefit assessment of digital health applications and may be crucial for strategic improvement of healthcare.