Maxim Topaz1, Theresa A Koleck2, Nicole Onorato3, Arlene Smaldone4, Suzanne Bakken5. 1. Center for Home Care Policy and Research, Visiting Nurse Service of New York, New York, NY; Columbia University School of Nursing, Columbia University Data Science Institute, New York, NY. 2. University of Pittsburgh School of Nursing, Pittsburgh, PA. 3. Center for Home Care Policy and Research, Visiting Nurse Service of New York, New York, NY. Electronic address: Nicole.Onorato@vnsny.org. 4. Columbia University School of Nursing, Columbia University College of Dental Medicine, New York, NY. 5. Columbia University School of Nursing, Columbia University Department of Biomedical Informatics, Columbia University Data Science Institute, New York, NY.
Abstract
BACKGROUND: Nurses often document patient symptoms in narrative notes. PURPOSE: This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare. METHOD: NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States. FINDINGS: NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission. DISCUSSION: Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.
BACKGROUND: Nurses often document patient symptoms in narrative notes. PURPOSE: This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare. METHOD: NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States. FINDINGS: NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission. DISCUSSION: Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.
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