Enabling NGS Testing and Precision Medicine with Fabric AI Technologies

I was invited to write about NGS on Xifin’s blog…

Next-Generation Sequencing (NGS) testing is experiencing tremendous growth driven at a high level by the promise of precision medicine and the life-changing power of applications in preventive genetic screening, somatic testing, and rare disease diagnosis. In all of these use cases, we see important clinical advances. Preventive genetic screening for risk factors such as BRCA mutations allows people to take preventative measures that save lives every day. Somatic mutation analysis allows for highly targeted therapies, and rare disease diagnosis is improving outcomes for babies in the NICU and providing hope for the 400 million people worldwide suffering from a rare disease.

For the full article please continue on Xifin’s site here

Accuracy is the New Speed

Second post as part of my work at Fabric Genomics

By Martin Reese & Laura Yecies

When caring for a critically ill child, two simultaneous thoughts are competing – the urgent need for a diagnosis to optimize treatment and the need for thoroughness – to carefully review all the possibilities.  Don’t jump to a conclusion but don’t get lost in the weeds keeping the patient, and the others behind them, in limbo.  We commonly see accuracy and speed as a dichotomy.  This has certainly been true in the past in genomics – how many variants to review? Review variants from less likely parts of the genome? Use a more restrictive filtering rule?

We had been operating in a world where deciding to use some of the heuristic shortcuts or to time limit review meant settling for less than optimal accuracy. Time-saving techniques left some diagnoses on the cutting room floor.  These simple Pareto prioritizations that are highly effective in dealing with everyday clinical situations are inherently problematic in the rare disease world.  We cannot eliminate the zebras when we know it’s unlikely to be a horse

Read more on the Fabric website here 

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 http://www.FabricGenomics.com

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