On Demand Webinars

Launching an orphan drug without an approved treatment? Need to estimate a forecast without really knowing the true prevalence? Does the lack of an ICD-10 code for the disease make all this even more complicated? In this webinar, IPM.ai and X4 Pharmaceuticals discuss cutting-edge methodologies via analogies and case studies that address the above conundrums common to the industry.

In this webinar, Biogen and IPM.ai discuss how leveraging machine learning and artificial intelligence can help improve trial recruitment. Whether it’s selecting possible location sites, expanding the list of physicians who treat the disease (vs. focusing only on a smaller subset like your target list), utilizing de-identified longitudinal real world data and cutting edge machine learning techniques puts Biogen at the forefront of clinical trial recruitment efforts.

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 create a 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 significant commercialization 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.

Listen as we discuss the power of machine learning and artificial intelligence to engage rare disease patients who could benefit from new therapies and modalities of care, thus improving their outcomes. Together with Insmed, we discuss their business needs, illustrate our approach and relay how Insmed leveraged our partnership to facilitate earlier diagnosis and help physicians understand treatment regimes. We also review how we applied aggregated learnings from Arikayce patients against a broader medical claims database and identified patients most likely to have Refractory MAC lung disease. Finally, we reveal how Insmed leverages both personal and non personal activation to educate physicians about disease states and the drug that could effectively treat this condition.