Bringing the Promise of Precision Medicine to Rare Disease

My latest post as part of my work at Fabric Genomics.

Precision medicine is a form of medicine that uses information about a person’s own genes or proteins to prevent, diagnose, or treat disease.1 When we think of precision medicine, we often focus on cancer and the recent efforts to specifically target treatment based on the genetic composition of a tumor (somatic) or the person’s germline. While it is early days, the health gains of this approach are quite significant with better outcomes shown in multiple studies2 and even data showing lower overall health costs.3 However, the point of precision medicine is not only to optimize treatment for cancer, but also to target health interventions generally for all conditions. In fact, we see the application of precision medicine to high volume diseases such as diabetes and heart disease and even lifestyle interventions such as diet and exercise.

Nowhere is this more challenging than for patients suffering from a rare disease. Ironically it is common to have a rare disease. More than 25 million Americans and more than 400 million people worldwide suffer from one of over 7000 rare conditions, defined as those conditions having an incidence of 1 in 200,000 or less. Precision medicine has focused on big data approaches to studying more common conditions, thereby leaving out those with rare conditions. Lacking a diagnosis, most of these patients and their families are on a “diagnostic odyssey.” These patients typically spend more than five years seeking accurate diagnosis and might see up to eight doctors, often receiving many misdiagnoses and differing opinions on their journey. Along the way, they may be exposed to harmful treatments and invasive testing. It is clear that comprehensive genetic testing gives the best possibility of getting to the exact diagnosis efficiently. Still, historically, genetic data have been available to a minority of patients: only those referred to a clinical geneticist for testing.

For the complete post see

Delivering Better Care at a Lower Cost – a Case Study of Project Baby Bear at Rady’s Children’s Hospital

My first post as part of my work with

By Martin Reese & Laura Yecies

The power and cost-effectiveness of AI are calling into question many of our assumptions about healthcare.  The most important dichotomy proving to be false is that providing the latest and most thorough diagnostic technology to optimize clinical outcomes is more expensive.  When we use AI to more comprehensively analyze cases we benefit from Moore’s law rapidly and continuously reducing costs.  By contrast, hospital-based care, especially when in an intensive setting such as the NICU is continuously increasing in cost. It is not surprising that when more extensive testing produces clinically actionable results that actually decrease hospital days we can accomplish the holy grail — better care and less expensive simultaneously…

Read more on the Fabric website here