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Record W4412115914 · doi:10.1177/13558196251358761

Evaluating patient characteristics and trends of avoidable emergency department visits: Informing community health services to reduce emergency department utilization

2025· article· en· W4412115914 on OpenAlexaffabout
Ryan P. Strum, Andrew P. Costa, Brent McLeod, Ravi Sivakumaran, Shawn Mondoux

Bibliographic record

VenueJournal of Health Services Research & Policy · 2025
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpact
Fundersnot available
KeywordsEmergency departmentMedicinePsychological interventionEmergency medicineWorkloadMedical diagnosisRetrospective cohort studyMedical emergencyFamily medicineNursing

Abstract

fetched live from OpenAlex

BackgroundThere is a growing debate on whether avoidable emergency department (ED) visits, those involving health issues that could have been managed in community settings, represent a significant workload for the department. Until recently an ED physician-validated measure of avoidable visits has not been available, hindering our understanding of these patients, services rendered in the ED and the nature of their conditions. We examined patient characteristics of ED visits retrospectively classified as avoidable and potentially avoidable at a Canadian academic hospital.MethodsWe conducted a retrospective cohort study using administrative ED data from an academic hospital in Hamilton, Canada from April 1, 2018 to August 31, 2023. We categorized all ED visits as avoidable, potentially avoidable, and not avoidable using the Emergency Department Avoidability Classification (EDAC). For each class, we analyzed patient characteristics and the top five physician interventions and diagnoses. We applied linear regression, locally weighted scatterplot smoothing (LOWESS) regression, and statistical process methods to examine monthly trends in avoidable and potentially avoidable visits. Additionally, we reported annual totals and length of stay for patients transported to the ED by paramedics.ResultsOverall, 58,528 (29.0%) of 201,741 ED visits were classified as either avoidable (11,302; 5.6%) or potentially avoidable (47,226; 23.4%). These patients were predominantly young-to-middle aged, with average visit durations of 3 hours 33 minutes (avoidable) and 4 hours 26 minutes (potentially avoidable). Their primary interventions were predominantly diagnostic imaging, skin repairs and mental health assessments. The proportion of ED visits in the study period that were avoidable increased from 2.1% to 7.7% and potentially avoidable from 18.2% to 21.2%. Approximately one-in-five paramedic transported patients were classified as having either an avoidable or potentially avoidable ED visit. Transported patients had an average length of stay of 4 hours 22 minutes for avoidable visits and 4 hours 35 minutes for potentially avoidable visits.ConclusionsA notable rise in the proportion of ED visits that could have been managed in non-ED settings was observed. Providing community clinicians with resources and capacity to manage and refer patients for diagnostic imaging, skin repairs and mental health assessments may reduce avoidable ED attendance. Further exploration of avoidable ED visits transported by paramedics could support refining ED diversion care models. Hospitals and health service policymakers could benefit from similar analyses using validated measures to identify care gaps that inform the development of new health services and models tailored to the specific needs of their communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.513
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes2
Has abstractyes

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