Using Predictive Intelligence to Uncover the Rare Disease Patient Journey
In this podcast interview, Dan Fisher, managing director and practice lead for IPM.ai, explains the use of artificial intelligence for stitching together key medical events that may happen over a rare and specialty disease patient’s lifespan that could predict whether or not a patient could be appropriate for a particular treatment or trial.
The rare disease patient journey can differ dramatically between disease states and individual patients, and the better the distinct patient point of view is understood, the more effective clinical trial recruitment or commercial effectiveness can be in reaching those patients.
IPM.ai collects data from 300 million patients which can help brands assess a patient population, accelerate clinical trial recruitment, and optimize commercial outcomes. By relying on real world health signals derived from claims data, machine learning, and artificial intelligence, it’s possible to create models that reveal the ideal de-identified patient and their healthcare ecosystem – regardless of their current diagnosis status.
Real-time alerts offer insight into potential patients who meet clinical criteria and their treating physicians. Using AI to identify patients who are likely to qualify for trial participation – as well as capturing patient race, ethnicity, and other demographics – creates a tremendous impact for funneling eligible patients. Then as commercialized products move to maturity, identifying those patients lost through conventional methods of HCP targeting and other patient level data applications adds value.
Listen below to learn about more use cases for applying patient findings to accelerate commercialization impact.