Guides
eCOA, AI, and Agentic AI: A practical overview and guide
Combining artificial intelligence (AI) and agentic AI with electronic Clinical Outcome Assessment (eCOA) systems fundamentally enhances how clinical trial data is collected, interpreted, and acted upon. At its core, eCOA captures structured data directly from patients, clinicians, or observers, such as symptom severity, quality of life, or functional outcomes. Modern platforms expand this further by supporting a full range of assessment types, including electronic patient-reported outcomes (ePRO), clinician-reported outcomes (eClinRO), observer-reported outcomes (eObsRO), and performance outcomes (ePerfO).
Back to Basics: Remote patient monitoring
Remote patient monitoring programs and technologies are becoming increasingly popular, backed by growing clinical evidence showing numerous benefits to patients and providers. While remote patient monitoring (RPM) isn’t new, it’s evolving quickly due to the regulatory push to expand access to care during the COVID-19 pandemic. Coupled with the fact that the digital health market is poised to more than double by 2026, providers and patients have a greater ability to track vitals between visits, and both centralized and decentralized clinical trials rely on remote data collection now more than ever. While there is tremendous potential upside, some barriers and risks are inherent in this digital process. Human-centered design and strategic implementation can ensure that RPM in clinical trials is both beneficial and cost effective.