From Blueprint to Best Practice: Gauging the Efficacy of Digital Health Solutions

Abstract

The surge of AI-driven technologies in the digital health market demands a concurrent evolution in evaluation standards, a pace currently lagging behind innovation. This paper explores the pivotal inadequacies within existing evaluation models, highlighting the necessity for refined methodologies that align with the unique complexities of digital health. We critically examine the initiatives of key entities such as Health Canada, CADTH, and CNDHE, pinpointing the deficiencies in addressing the volatility and intricacies of AI applications. To bridge these gaps, we advocate for a nuanced evaluation paradigm, proposing the establishment of an oversight body, implementing detailed category-specific criteria, and a robust six-step evaluation framework tailored for AI health solutions. The paper culminates by underscoring the indispensable role of strategic leadership and agile policymaking in cultivating a resilient digital health environment that prioritizes patient care without compromising the ingenuity of technological advances.