A consortium of healthcare providers and medical informatics vendors are working to apply natural language processing (NLP) algorithms to unstructured text documents to make them more relevant to clinical decision support and quality reporting protocols, reports Information Week.
The industry wide Health Story initiative aims to apply NLP logic to unstructured text-based documents such as patient visit notes and dictated reports. Advocates say that NLP algorithms, more commonly found in speech recognition technology, could not only facilitate improved access to these documents within medical informatics systems, but also enable physicians to share data more easily across different electronic health record solutions.
“There are about 1.2 billion clinical documents out there, and it’s relatively easy to turn those into minimally structured documents,” Robert Dolin, leader of the Health Story consortium, told the news source. “If you do that, the next year you can add a little more structure. With that strategy, you embrace the front-line clinicians right from the get-go, and you minimize the disruption to clinical workflow. You get all of this data flowing.”
Health Story has developed a series of nine document templates that conform to the Clinical Document Architecture of the data standards organization Health Level Seven using the extensible markup language format.
The group has been working with healthcare IT specialists from M*Modal, vendor of the Nuance speech recognition software used in certain medical informatics solutions, to further refine the process. Officials say NLP algorithms could be used to extract information from unstructured text documents in the same manner as some clinical informatics systems extract data from dictated reports using speech recognition.
According to HealthAffairs, interoperability initiatives such as this are the future of healthcare IT. Effective sharing of patient health information across a range of separate networks, health information exchanges and clinical informatics systems has the potential to radically improve the quality of patient care.