ArticleNursing research
Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits.
Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records.Cancer nursing · 2026Article
- More than Just a Bag-Purple Urine Bag Syndrome as a Manifestation of Vulnerability in Geriatric Patients: A Case Report.Clinical practice and cases in emergency medicine · 2026Article
- Community Health Nurses' Knowledge and Perceptions of AI in Canada: National Cross-Sectional Survey.JMIR nursing · 2026Article
- Decoding machine learning in nursing research: A scoping review of effective algorithms.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 Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.Journal of the American Medical Directors Association · 2024Article
- 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
- Natural Language Processing of Nursing Notes: An Integrative Review.Computers, informatics, nursing : CIN · 2023Review
- Modeling acute care utilization: practical implications for insomnia patients.Scientific reports · 2023Article
- Machine learning applied to electronic health record data in home healthcare: A scoping review.International journal of medical informatics · 2023Article
- 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
- 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
- Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Exploring Reasons for Delayed Start-of-Care Nursing Visits in Home Health Care: Algorithm Development and Data Science Study.JMIR nursing · 2021Article
- The Time is Now: Informatics Research Opportunities in Home Health Care.Applied clinical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 3 countries.
Funding
Abstract
backgroundAbout 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes.
objectivesThe aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
methodsThis study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation).
resultsBest performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. DISCUSSION: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.