Key result
An open-source natural language processing tool identified lifestyle modification documentation from EHRs with 99.27% recall, 94.44% precision, and 88.15% correct classification.
Why the study?
Lifestyle modification data are not easily retrieved or used in research due to their textual nature in electronic health records.
Can natural language processing accurately identify lifestyle modification documentation from electronic health records of hypertension patients?
Can natural language processing accurately identify lifestyle modification documentation from electronic health records of hypertension patients?
Answers are grounded in this paper and cite their sources.
Natural language processing can accurately extract and classify lifestyle modification documentation from electronic health records, enabling its use in research and quality improvement.
Lifestyle modification, including diet, exercise, and tobacco cessation, is the first-line treatment of many disorders including hypertension, obesity, and diabetes. Lifestyle modification data are not easily retrieved or used in research due to their textual nature. This study addresses this knowledge gap using natural language processing to automatically identify lifestyle modification documentation from electronic health records. Electronic health record notes from hypertension patients were analyzed using an open-source natural language processing tool to retrieve assessment and advice regarding lifestyle modification. These data were classified as lifestyle modification assessment or advice and mapped to a coded standard ontology. Combined lifestyle modification (advice and assessment) recall was 99.27 percent, precision 94.44 percent, and correct classification 88.15 percent. Through extraction and transformation of narrative lifestyle modification data to coded data, this critical information can be used in research, metric development, and quality improvement efforts regarding care delivery for multiple medical conditions that benefit from lifestyle modification.
No takes yet. Share an insight, caveat, or question.
Shoenbill et al. (2019) studied Hypertension. Natural language processing tool was evaluated on Combined lifestyle modification (advice and assessment) recall, precision, and correct classification. An open-source natural language processing tool identified lifestyle modification documentation from EHRs with 99.27% recall, 94.44% precision, and 88.15% correct classification.
Synapse has enriched 10 related papers. Consider them for comparative context: