How NLP Tools Can Help Improve EMR Interoperability
April 15, 2021, 2 Minute Read
I’d like to talk about electronic medical record (EMR) interoperability. A couple of weeks ago, I posted about patient medical records (LINK).
Interoperability has been a noted policy agenda item for quite some time. Even President Joe Biden writes about the need for better solutions for interoperability in his 2017 book, “Promise Me, Dad”. President Biden, previously Vice President, faced the challenge of providing information needed by doctors for his son, Beau Biden. This became a difficult task as doctors were diagnosing then treating Beau from various locations. Their solution to the interoperability challenge? FaceTime through Apple iPads.
Most experts know legislation is already being implemented to help patients gain more control of their records and where they get ported to. For example, see relevant policy guidance at this link Policies and Technology for Interoperability and Burden Reduction | CMS. However, challenges remain.
Here’s a potential way to help you wade through masses of unstructured textual data. When you implement Lymba’s NLP platforms and build common Ontology, Patient Medical Records don’t have to be a cryptic, indecipherable series of notes. Use machines to help recognize concepts, automate data onboarding, and access information through natural language no-code queries. This helps medical professionals on-board and access bulk data much like a human speed reader would. Lymba’s text extraction works on semi-structured and unstructured datasets and can help you drive your patient analytics and accelerate the personalization of medicine. To learn more on how this can be achieved – feel free to reach out for a consultation and demo.
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