ArticlePloS one2014
Risk prediction of emergency department revisit 30 days post discharge: a prospective study.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 52 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Real-Time Emergency Department Electronic Notifications Regarding High-Risk Patients: A Systematic Review.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2019Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Article
- Acquisition of patients' EHR information under ED congestion - an empirical investigation.Health systems (Basingstoke, England) · 2025Review
- Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.PLOS digital health · 2024Article
- Prediction Models Using Decision Tree and Logistic Regression Method for Predicting Hospital Revisits in Peritoneal Dialysis Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.BMC emergency medicine · 2024Article
- Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.BMC medical informatics and decision making · 2024Article
- Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.BMC emergency medicine · 2024Article
- Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.JAMIA open · 2023Article
- ED Revisits Within 72 Hours to a Tertiary Health Care Facility in Dubai: A Descriptive Study.Cureus · 2023Review
- Machine learning to improve frequent emergency department use prediction: a retrospective cohort study.Scientific reports · 2023Article
- Patient-Centered Outcomes of an Emergency Department Social and Medical Resource Intervention.The western journal of emergency medicine · 2022Article
- High-risk Return Visits to United States Emergency Departments, 2010-2018.The western journal of emergency medicine · 2022Article
- Article
- Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system.Scientific reports · 2022Article
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
- A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
19 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmong patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. METHODS AND
findingsA decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns.
conclusionsOur ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.
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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.