ArticleNursing research
Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.
Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- From Conversation to Standardized Terminology: An LLM-RAG Approach for Automated Health Problem Identification in Home Healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.European journal of cardiovascular nursing · 2025Article
- Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Applying natural language processing to understand symptoms among older adult home healthcare patients with urinary incontinence.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2024Article
- Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.JCO clinical cancer informatics · 2024Article
- Interventional analytics in skilled nursing facilities associated with reduced readmissions.The American journal of managed care · 2024Article
- Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.Journal of the American Medical Directors Association · 2024Article
- Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study.Journal of the American Medical Directors Association · 2023Article
- Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Home Healthcare Patients With Distinct Psychological, Cognitive, and Behavioral Symptom Profiles and At-Risk Subgroup for Hospitalization and Emergency Department Visits Using Latent Class Analysis.Clinical nursing research · 2023Article
- The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care.Journal of advanced nursing · 2023Article
- Do nurses document all discussions of patient problems and nursing interventions in the electronic health record? A pilot study in home healthcare.JAMIA open · 2022Article
- Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.Journal of biomedical informatics · 2022Article
- Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Is Auto-generated Transcript of Patient-Nurse Communication Ready to Use for Identifying the Risk for Hospitalizations or Emergency Department Visits in Home Health Care? A Natural Language Processing Pilot Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Characterizing symptom clusters for older adults with urinary incontinence in home healthcare.Geriatric nursing (New York, N.Y.)Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
backgroundAbout one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode.
objectiveThe aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits.
methodsThis study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients ( n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017.
resultsA total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions.
conclusionOur findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.
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