Alnylam Webinar

Utilizing AI in Conjunction with RWD to Discover Undiagnosed Patients Suffering From a Rare Disease

Tokenized Non-Traditional Data, Real World Evidence and Machine Learning: Improving Patient Outcomes at Scale

Classic approaches for patient discovery, treatment journey mapping, referral network intelligence, market assessment and audience engagement all begin with the common assumption that the combination of medical and prescription claims at the patient level can be deduced and combined to createa de-identified patient level cohort to anchor analysis. However, as the pharmaceutical landscape shifts from one dominated by primary care markets with high prevalence and a plethora of launch blueprints to draw upon, to one where diffuse specialty markets with low prevalence and a lack of analogs to anchor launch strategy, this assumption rarely holds true and creates significantcommercialization challenges. This conundrum is particularly acute in rare disease, where only about 500 of 7,000 have a diagnostic code in the International Classification of Diseases (ICD).

In this webinar, learn how the combination of genetic testing, the democratization of de-identified patient data, the rise of tokenization across entities in the healthcare eco-system and machine learning can enable the promise of precision medicine. We'll also share a case study by Alnylam anchored in patient outcomes where obstacles were overcome to effectively diagnose 66 previously un-diagnosed patients.


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Vice President Global & US Business Operations



Jonathan Woodring
Executive Vice President & General Manager



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