In his latest white paper, Oodaye Shukla, EVERSANA’s Chief Data and Analytics Officer, outlines the revolutionary impact that artificial intelligence and machine learning can have on improving patient outcomes and access to therapy.
Rare disease statistics:
- The average time to get an accurate rare diagnosis is 6-8 years.
- There are 7,000 distinct types of rare and genetic diseases, but 95% of rare diseases lack an FDA-approved treatment.
- Globally, 400 million people are living with a rare disease.
- Rare diseases impact more people than cancer and AIDs combined.
Patient Perspective: Searching for a rare disease diagnosis is often a frustrating process that can take many years. Patients with rare diseases and their families often face a long journey of misdiagnoses and limited access to research and clinical trials compared to those living with more common conditions. Predictive modeling can glean insights from hundreds of millions of datasets, allowing therapies to proactively find patients rather than patients needing to seek out therapies.
Commercialization of Rare Disease Drugs: The use of predictive data analytics can also inform business decisions, such as the number of patients available to support the investment required to develop a drug, execute a successful clinical trial, and bring the drug to market; and whether an orphan designation can be sought for a therapy. This in turn drives efforts to increase access to these treatment options, and we deliver on the overall goal of making sure that we get the right treatment to undiagnosed rare disease patients in a timely manner.
Download Oodaye’s white paper to learn how applying predictive modeling techniques to healthcare data accelerates the accuracy and speed of rare disease diagnosis and drug commercialization.
Chief Data and Analytics Officer
Oodaye Shukla serves as EVERSANA’s Chief Data and Analytics Officer. His broad experience in such industries as health care, telecom, the U.S. Department of Defense, and U.S. intelligence Community, spans more than 20 years. Oodaye started his career at the Johns Hopkins Applied Physics Lab, building optical and digital computers and developing neural network models […]