Free-text documentation hinders practices from getting credit for the quality of care they are providing.
Value-based care is a focus now more than ever in the constantly evolving healthcare landscape. Each year somewhere between 700,000 people fall or hurt themselves (Source). Griffin Faculty Physicians, a multi-specialty medical group in Connecticut, and their EMR allowed them to create custom templates that provided the consistency of documentation for changing quality needs and questionnaires.
These templates are embedded into the EMR as free-text within their encounters. When Griffin hit a roadblock extracting Morse Fall Scale summaries from their encounters, they sought help quantifying and reporting out to maximize revenue and increase the quality of patient care.
Emerge’s natural language processing (NLP) enables free-text documentation to behave as structured data.
Using natural language processing (NLP), Emerge was able to create a data point that pulls Morse Fall Scale scores from free-text to make them actionable and quantifiable. This means with Emerge, Morse Fall Scale scores are normalized and function as if they are structured data points within the EMR. Via Emerge, the scores can be searched, pulled into dashboards, and reported on.
Emerge helped Griffin prove 96% adherence to Morse Fall Risk scoring using natural language processing (NLP).
Griffin went from not being able to track their scores to now, using Emerge, Griffin can track metric adherence and pull the risk assessment value to better care for their patients. Working together with our customer, Emerge has improved quality and revenue for Griffin Faculty Physicians.
While this is one use case for Griffin, Emerge’s NLP can be applied in multiple different ways over free-text templates, reports, or scans. Some other applications of Emerge’s NLP within the EMR include Depression Screenings, Anxiety Screenings, Echocardiograms, Ultrasounds, etc. Anywhere within the EMR where unstructured data is stored, Emerge can help.