The success of the IBM Watson team and their automated question-answering system on Jeopardy opened the eyes of many to the potential of computerized "understanding" of human language. The victorious IBMers were quick to clarify that their goal isn't to create a robot army of game show champs but to apply these technologies to tackle important societal problems -- starting with healthcare.
They couldn't have picked a better target. By some estimates 70% of all clinically useful information is formatted as unstructured free text. And thanks to a gradual shift toward pay-for-performance and fee-per-patient models of reimbursement, healthcare organizations are finally getting serious about using their data to realize efficiencies. With so much useful information captured only in clinicians' narratives, hospital administrators and policymakers are grappling with what to do with all that free text.
Natural language processing, or NLP, is software designed to turn unstructured free text into structured values. The hope is that NLP can help answer doctors' questions at the point of care much in the way Watson responded to Alex Trebek. NLP ideally would automatically populate important variables in patient registries or as part of quality-improvement initiatives. Effective NLP could play a critical role in finally answering healthcare's most obvious and important unanswered questions: What are we doing, to whom are we doing it and is it working? All that leads to this question: Is natural language processing ready for wide use?
Vendors beyond IBM have recognized these needs and are responding with NLP-based systems ranging from the automated assignment of billing codes to "enterprise" natural language processing systems. In the Intro to Clinical NLP tutorial I teach at the annual meeting of the American Medical Informatics Association, I've noticed a shift in the audience from curious researchers and students to hospitalists and EMR vendors interested in implementing NLP. This exciting shift has led me to explain the fundamentals that people must know about clinical NLP to make decisions about using this technology in their organizations. I exclude important but familiar software-related concerns such as "buy versus build," product selection criteria and vendor lock-in. Think of these as the NLP-specific factors you need to keep in mind when considering NLP-based systems.
1. How good is good enough?
For over 50 years, researchers have shown that NLP can be applied with high levels of accuracy for any number of tasks, from extracting symptoms, treatments and tests from the texts of medial records to automatically assigning billing codes. So why hasn't NLP been widely adopted? There are economic reasons, of course, but the nature of the technology itself is largely to blame.
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