ArticleBMC emergency medicine2016
A practical method for predicting frequent use of emergency department care using routinely available electronic registration data.
Article in BMC emergency medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed.
- Impacts of Residential Segregation and Comorbidity on Racial Disparities in Acute Care Utilization.Journal of racial and ethnic health disparities · 2026Article
- Ascertaining cause-specific emergency department demand using forecast combinations.BMC emergency medicine · 2026Article
- The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection.Journal of medical Internet research · 2022Article
- National travel distances for emergency care.BMC health services research · 2022Article
- Can the adverse childhood experiences (ACEs) checklist be utilized to predict emergency department visits among children and adolescents?BMC medical research methodology · 2021Article
- Adherence to the Use of Home Telehealth Technologies and Emergency Room Visits in Veterans with Heart Failure.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2021Article
- Statistical tools used for analyses of frequent users of emergency department: a scoping review.BMJ open · 2019Article
- Evaluating the effect of data standardization and validation on patient matching accuracy.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Factors Associated With Emergency Department Use by Patients With and Without Mental Health Diagnoses.JAMA network open · 2018Article
- Towards a Learning Health System to Reduce Emergency Department Visits at a Population Level.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Predicting Emergency Department Visits.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundAccurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits.
methodUsing routinely collected registration data in the state of Indiana, U.S.A., from 2008, we developed multivariable logistic regression models to predict frequent ED visits in the subsequent two years. We assessed the model's accuracy using Receiver Operating Characteristic (ROC) curves, sensitivity, and positive predictive value (PPV).
resultsStrong predictors of frequent ED visits included age between 25 and 44 years, female gender, close proximity to the ED (less than 5 miles traveling distance), total visits in the baseline year, and respiratory and dental chief complaint syndromes. The area under ROC curve (AUC) ranged from 0.83 to 0.92 for models predicting patients with 8 or more visits to 16 or more visits in the subsequent two years, suggesting acceptable discrimination. With 25 % sensitivity, the model predicting frequent ED use as defined as 16 or more visits in 2009 and 2010 had a PPV of 59.5 % and specificity of 99.9 %. The "adjusted" PPV of this model, which includes patients having 8 or more visits, is 81.9 %.
conclusionWe demonstrate a strong association between predictor variables present in registration data and frequent ED use. The algorithm's performance characteristics suggest that it is technically feasible to use routinely collected registration data to predict future frequent ED use.
Indexed as
Identifiers
What OpenQuestion holds
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.