Experts say emerging tools could become as important to precision medicine as HTML is to the web.
By Bill Siwicki July 23, 2018
Precision medicine is something of a Holy Grail in healthcare: Being able to deliver personalized treatments to individual patients to best cure specific ailments is the ultimate in healthcare.
While precision medicine is still fairly nascent today, one can look forward and see what’s coming down the line to change the way personalized health can be delivered. And though precision medicine is a tricky arena to predict, experts have their ideas on where the complex healthcare field is heading, and what the next generation of precision medicine will look like.
The term “next-generation technology” has different connotations for different healthcare organizations, depending on where they are on the innovation continuum; but machine learning-enabled medical image analysis software should be at the top of the list, said Paul Cerrato, an independent healthcare writer who has collaborated on three books with Beth Israel Deaconess System CIO John Halamka.
“To date, machine learning algorithms are now capable of delivering more accurate interpretations of radiological images than human ophthalmologists, and interpretation of dermatological lesions that is just as accurate as that provided by dermatologists,” Cerrato said. “For instance, with the use of deep neural networks, it is now possible for computers loaded with the appropriate software to diagnose skin cancer as well as experienced dermatologists.”
A key function of operationalizing precision medicine is the ability to access genetic test results from the clinical context, within the existing workflow, whether the results are stored in the EHR or an ancillary system like a PACS or medication management system.
This will require interoperable IT tools and application programming interfaces that are able to integrate genomic data for use with existing systems without significant IT development or impact to existing system performance, said Don Rule, CEO of Translational Software, a genomics clinical decision support and precision medicine company.
“APIs developed using the Fast Healthcare Interoperability Resources specification, an open-sourced standard based on HL-7 for exchanging health information to ensure interoperability and security, can facilitate integration of genomics data and test results seamlessly and cost-effectively to deliver on this ability at the point of care,” Rule said.