ArticleJournal of biomedical informatics2022
Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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
24 citing papers in PubMed.
- Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.International journal of medical informatics · 2026Article
- 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
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Adverse Social Determinants of Health and Urinary Incontinence Among Older Adult Women in Home Health Care.Journal of the American Medical Directors Association · 2025Article
- Challenges and Contributing Factors to Emergency Department Overcrowding in Taiwan After the Lunar New Year Holiday: A 2024 Survey.Journal of acute medicine · 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
- Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- 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
- 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
- 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
- Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Lightweight transformers for clinical natural language processing.Natural language engineering · 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
- FHIR-up! Advancing knowledge from clinical data through application of standardized nursing terminologies within HL7® FHIR®.Journal of the American Medical Informatics Association : JAMIA · 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
- Future advancement of health care through standardized nursing terminologies: reflections from a Friends of the National Library of Medicine workshop honoring Virginia K. Saba.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
- Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.Yearbook of medical informatics · 2023Article
- Self-reported gaps in care coordination and preventable adverse outcomes among older adults receiving home health care.Journal of the American Geriatrics Society · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
BACKGROUND/
objectiveBetween 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC.
methodsFour risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics.
resultsDuring the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC.
conclusionAll models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.
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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.