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Learn more about how Clinovera worked with the Division of Endocrinology, Diabetes and Hypertension at a leading academic medical center to develop and validate new NLP technology to extract valid medical information from EHRs.
EHR data plays an ever more critical role in monitoring and improving the quality of care, conducting longitudinal studies, and designing clinical trials. One of the main challenges for healthcare organizations is that a significant amount of valuable data is found in narrative documents, such as provider notes or radiology reports.
Clinovera understands that natural language processing can illustrate complex concepts that represent the critical next frontier in the analysis of EHR data. Several important examples of complex concepts where NLP technologies could be beneficial include the decline of treatment by patients, discussion of bariatric surgery between patients with obesity and their clinicians, and documentation of smoking status.
As an additional step, our client intends to overlay insights extracted from unstructured narratives with structured information in the patient record. For example, the NLP and ML algorithms may identify that a provider has recommended a specific procedure – such as bariatric surgery. The patient record structured content may help determine if the patient followed up on the provider’s recommendations.
The Clinovera approach harnesses the power of natural language processing (NLP) to successfully analyze the client’s narrative EHR data, enabling the improvement of identification of diagnoses, medications, and other concepts. Our solution establishes connections between these elements in the patient record – to improve overall care.