ArticleInternational journal of medical informatics2023
Machine learning applied to electronic health record data in home healthcare: A scoping review.
Article in International journal of medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Evaluating the Performance of Traditional Pharmacoepidemiologic and Machine Learning Models to Predict Pregnancies at Risk of Major Congenital Malformations.Birth defects research · 2026Article
- Community Health Nurses' Knowledge and Perceptions of AI in Canada: National Cross-Sectional Survey.JMIR nursing · 2026Article
- Design and validation of an integrated meta-instrument for nursing assessment in home hospitalization: A study protocol.PloS one · 2026Article
- Adverse Social Determinants of Health and Urinary Incontinence Among Older Adult Women in Home Health Care.Journal of the American Medical Directors Association · 2025Article
- Risk Factors for Emergency Department Visits or Hospitalizations Among Older Adults With Urinary Incontinence in Home Health Care.Journal of gerontological nursing · 2025Article
- Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024).JMIR public health and surveillance · 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
- INTegRated InterveNtion of pSychogerIatric Care: real-world application and implementation of an advanced integrated telehealth system incorporating machine learning.Frontiers in psychology · 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
- Sample preparation and detection methods in point-of-care devices towards future at-home testing.Lab on a chip · 2024Review
- Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.Scientific reports · 2024Article
- PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.Journal of the American Medical Directors Association · 2024Article
- A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days.Frontiers in digital health · 2024Article
- Predicting food insecurity in a pediatric population using the electronic health record.Journal of clinical and translational science · 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
- Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.Applied clinical informatics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveDespite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.
methodsDuring March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.
resultsThe final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).
conclusionFuture studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.
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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.