How we helped a clinical terminology company expedite the consistency of medical descriptions using machine learning.
Something consultants often experience as they go from client to client is that each company, department, or even team has its own jargon or dialect. Every organization has different assumptions, acronyms, interpretations, and shorthand based partly on the work its members do, partly on “someone said it that way once, and it stuck,” and partly on “this is just the way I say it.”
Healthcare teams are no exception to this, but they have unique concerns about continuity of care, patient safety, tracking diseases, learning the most effective treatments, regulatory compliance and even getting reimbursed for care provided. These concerns in healthcare make it critical to reach a common understanding across teams despite the difference in dialects. Our client, a clinical terminology company, is in the business of helping create that shared understanding. They help translate the dialects of more than 4,500 hospitals and 500,000 physicians into a consistent, common language. It’s a daunting task. Take medical procedure descriptions, for example -- our client maintains a growing list of over 350,000 distinct medical procedures, often dealing with nuances as minute as the difference between “physical therapy for 15 minutes” and “physical therapy up to 15 minutes.” Unsurprisingly, it took highly trained, hard-to-find experts to map millions of procedure descriptions into this shared vocabulary. The rarity of these experts presented a particular, quickly growing concern to our client. For medical procedures alone, it saw over 1000 new ways to describe medical procedures each month. To help sustain this growth, our client turned to natural language processing (NLP) and machine learning (ML) to make the task less labor-intensive and began to build a data science team to take this on.
