Every life sciences organization needs to consider how best to apply machine learning (ML) to RWE to support better patient outcomes. Here’s how they can ensure RWE is “ML-ready.”
Congress has already granted the FDA flexibility in evaluating therapies for rare diseases, including the use of real-world evidence and natural history data when traditional large-scale trials are not feasible. The question before the FDA now is not if those tools can be applied – they can – but if the agency has the courage to use them before more patients lose their autonomy, and ultimately, their lives, to rare disease.
Arbiter’s Anjali Jameson on hospital and payer alignment.
The non-responder gap matters for two reasons: First, patients who are in an advanced stage of cancer lose valuable time in ineffective therapy. Second, it increases the cost of therapy and resources for both patients and the broader healthcare ecosystem.
The way we measure ALS progression is failing patients. We are entering a new era of ALS therapeutics but still relying on the same blunt instruments of measurement.
Here are some of the structural and strategic causes behind that failure rate, and ways to improve launch outcomes.
When applied with a rigorous, subject-matter-expert guided process, AI-currated RWD is an essential resource giving life science organizations new power to monitor the diverse variety of ways drugs are actually utilized post-approval.
MedCity News was at the Vive conference and spoke with executives who shared their insights for the healthcare industry.
Some specialties, like oncology, have been quicker to embrace the promise of pharmacogenomics (PGx), and their successes can serve as a roadmap as others explore the impact it can have on quality, outcomes and patient satisfaction.
Personalized and highly-targeted therapeutics are becoming more common, making local or regional production increasingly valuable for faster patient access. This underscores how a hybrid network combining large, centralized plants coupled with flexible regional sites will define the next era of resilient biomanufacturing.
As RWE continues to influence the future of drug development, pharma companies will inevitably encounter hurdles. But with the right approach, they can be overcome to unlock critical insights and bring better therapies to patients faster.
It reduced cravings, dulled alcohol’s “buzz,” and carried no risk of addiction. By every measure, naltrexone should have immediately become a major triumph. Instead, it flopped because the institutions charged with treating addiction refused to use it. Now considered a gold standard, it survived because patients and communities kept it alive.
How to turn analytics into actual policy outcomes.
This year will be the turning point from AI “hype” to the adoption of meaningful AI and digital health solutions in pharma. Here four predictions.
Those who act first will lower cost to serve, boost rep productivity and deliver customer experiences that feel personal, not programmatic. Those who wait risk falling behind as competitors turn engagement into a true differentiator.
The same forces now at play in medical aesthetics will soon appear in every therapeutic category where treatment overlaps with identity, from hormones to nootropics to genetic optimization.
New tools can predict deadly reactions to common cancer drugs, but adoption lags despite evidence.
The technology already exists that would allow us to close the loop between physiological dysfunction and neural adaptation in real time. The question is not if, but how, rapidly this capability will reshape therapeutic paradigms and the economics that sustain them.