When it comes to building the ideal patient cohort, the breadth and variety of demographic and clinical criteria available to select from are critical. The more you have to choose from, the more specific and targeted you can get in selecting the patient group most aligned with your therapy.
With many of today’s pharmaceutical brands treating second-line, third-line, or later stage patients as well as those with or without a certain biomarker, lab data is a key RWD asset. Making lab data “analytics-ready” at scale requires machine learning (ML), natural language processing (NLP), artificial intelligence (AI) and other significant domain expertise to harmonize unstructured and unstandardized diagnostic results.
Join Prognos’ Chief Medical Information Officer, Jason Bhan, M.D., Chief Data Scientist, Adam Petranovich, and Datavant's Head of Data Strategy, Su Huang as they share:
- How easily accessing and linking a variety of RWD can accelerate time to value
- Why RWD managed and enhanced by ML, NLP, and AI makes RWD more clinically meaningful to your specific patient profile
- How to apply insights to a myriad of use cases, increasing data value