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Artificial Intelligence (AI) techniques, like Optical Character Recognition (OCR), Natural Language Processing (NLP), and others, promise to uncover a wealth of medical insights currently trapped in PDFs and the unstructured and semi-structured elements in clinical notes.  AI could be foundational to allow us to “read between the lines” in clinical documents, even detecting data that is missing entirely. The information surfaced from these techniques will greatly enhance applications such as data-driven decision support, healthcare analytics, and care gap mitigation.  However, to ensure usability, the results from AI need to be made available in industry standard formats. Further, to promote trust and ensure transparency, the results from applying AI models needs to be clearly indicated and readable by both humans and software systems.
In this Diameter Health industry brief, we propose the use of the HL7 Fast Healthcare Interoperability Resources® (FHIR®) standard for the results of AI models.   With the potential to unlock this treasure trove of new medical data, it is critical we pay attention to the lessons learned in healthcare, specifically, the importance of standards for interoperability. Download this paper to learn how to capitalize on these lessons and use FHIR as the standard for AI outputs.