Dash Technologies Inc

Real-world data clinical trials are not a workaround but are a design upgrade. The traditional controlled trial model: controlled populations, site-based enrollment, manual review, built clear evidence slowly and at high cost , from populations that represented a narrow slice of those who actually take the drug, and the FDA recognized that gap. Pharma organizations building evidence programs around RWD generate faster data, at lower cost, from populations that look like the real patient population. The ones still running site-only trials are paying for constraints they no longer have to accept.   The cost difference is not marginal. Site-based enrollment for a Phase III program runs on years of referral chains, travel requirements, and geographic exclusions that systematically underrepresent rural and minority populations. RWD cuts those constraints. The trial runs on patients who already exist in the health system data.   What Is Real-World Data in Healthcare?   Re...

  The pharmaceutical and biotech sectors remain beset by unprecedented challenges in getting innovative treatments to market. With trial expenses running a projected $1.3 billion per approved drug and failure rates of more than 90% , there has never been a greater imperative to make wiser, data-informed choices. Enter predictive analytics in clinical trials, a revolutionary method that applies artificial intelligence to change the face of research study design, execution, and analysis. The Traditional Clinical Trial: Mess of Inefficiency To appreciate the scale of the AI-driven revolution, it's important first to understand the limitations of the conventional clinical trial model. For years, the process has been notoriously inefficient. A single drug can take years & billions of $ to bring to market, with a significant portion of that time & expense consumed by clinical trials. Historical outcomes, incomplete data sets, and even educated guesswork inform many of its...