Bevey Miner, executive vice president of health care strategy and policy, Consensus Cloud Solutions, shares how extraction artificial intelligence (AI) is helping train machine learning models to recognize and extract information from documents, providing structured data with confidence scores.

This approach to extracting data is presented as a solution to address the challenges in health care and helps organizations comply with evolving standards, even with limited resources, Miner explains.

Transcript

What role do emerging technologies such as AI large language models play in shaping digital health strategy?

When you look at this movement to consume, send, and receive structured data, mostly using HL7 FHIR [Fast Healthcare Interoperability Resources] standard, which is the latest HL7 messaging, or even X12, a method that's been around forever for sending and receiving and sharing data, you can think about unstructured data being a document.

So, if you look at a fax, it's a PDF document. There's so much that's a document, like a fax, scanned images—there's lots of scanned images all over the place in health care—or even TIFF images where pictures are taken, that's all unstructured. So how do you get it to be structured?

The way I'm referring to structured data is that all of the different important fields are consumable as separate fields. Let's just start with the patient's demographics: first name, last name, address, their insurance number, etc. That is not structured data. Therefore, somebody has to data enter that in order for any of those documents to be attached to a patient's chart.

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