“Not only are we able to measure many more things with many different modalities, but we’re able to measure it to levels of resolution that we could never achieve before.” 

So noted Sezin Palmer, mission area executive for national health at Johns Hopkins University Applied Physics Lab (APL), in a recent panel discussion at the Dec. 12 CXO Tech Forum: AI and Big Data in Government in Arlington, Va.

With a focus on how healthcare stakeholders can tap into AI technologies and access rapidly growing US government data sets, participants looked from a number of angles at how the vast amounts of computing power and data generation today means more accuracy and intelligence in algorithmic models and, by extension, the prospect for developing new cures.

For example, data availability is helping the Food and Drug Administration expedite approvals for drugs and medical devices, said Roselie Bright, epidemiologist in the FDA’s Office of Health Informatics.

In November, the FDA issued a “Prescription Drug-Use-Related Software” notice, which announced a docket asking for public input on the agency’s proposed framework for prescription drug-use-related software. The FDA is hoping this approach brings in a software that won’t require FDA review before dissemination.

“The idea,” Bright explained, “is . . . if a company is mainly just producing software that’s its medical product, can we trust that if they have good processes in place that they’re going to be good actors, that their software is going to be, basically, reliable and not hurt people?”

FDA is still exploring how this would work, as questions remain about statutory authority, but Bright said FDA is also thinking about extending the current use of the initiative for supporting randomized clinical trials.

Another panel participant, George Chambers, deputy chief information officer of the Health and Human Services Department, sees the evolution of AI and data availability from an infrastructure and operational standpoint. 

He noted that AI provides visualization and predictive analytics tools that can be applied to an application or data set, but he added that without data integrity, the outcomes won’t be useful or accurate.

“Is the same data element coming from this database comparable in terminology to this one over here?” Chambers questioned. “AI can help us do that, but once again, these are the challenges from an infrastructure and support standpoint, especially with an organization 88,000 strong, like HHS.”  

In addition to challenges in data management and governance, there's the challenge of structured and unstructured data. Palmer and the APL’s partners at Johns Hopkins Medicine focus on how best to leverage all the unstructured data that is now in electronic health records and on how to pull useful data from the electronic health record and combine it with other datasets, like imaging, genomics data and wearable tech data. Another area Palmer and APL are working on is natural language processing and generalization.

Finally, Dr. Don Rucker, HHS’ national coordinator for health IT, noted that structured and unstructured data have different uses, and he explained how HHS has been working on getting physician notes better coordinated through a process called the Clinical Document Architecture Standard that will facilitate the electronic exchange of clinical documents such as progress notes, patient histories and other relevant data.