Effectiveness of Interventions to Decrease Emergency Department Visits by Adult Frequent Users: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Frequent emergency department (ED) users are high-risk and high-resource-utilizing patients. This systematic review evaluates effectiveness of interventions targeting adult frequent ED users in reducing visit frequency and improving patient outcomes. METHODS: An a priori protocol was published in PROSPERO. Two independent reviewers screened, selected, rated quality, and extracted data. Third-party adjudication resolved disagreements. Rate ratios of post- versus pre-intervention ED visits were calculated. Data sources were from a comprehensive search that included seven databases and the gray literature. Eligibility criteria for selecting studies included experimental studies assessing the effect of interventions on frequent users' ED visits and patient-oriented outcomes. RESULTS: A total of 6,865 citations were identified and 31 studies included. Designs were noncontrolled (n = 21) and controlled (n = 4) before-after studies and randomized controlled trials (n = 6). Frequent user definitions varied considerably and risk of bias was moderate to high. Studies examined general frequent users or those with psychiatric comorbidities, chronic disease, or low socioeconomic status or the elderly. Interventions included case management (n = 18), care plans (n = 8), diversion strategies (n = 3), printout case notes (n = 1), and social work visits (n = 1). Post- versus pre-intervention rate ratios were calculated for 25 studies and indicated a significant visit decrease in 21 (84%) of these studies. The median rate ratio was 0.63 (interquartile range = 0.41 to 0.71), indicating that the general effect of the interventions described was to decrease ED visits post-intervention. Significant visit decreases were found for a majority of studies in subgroup analyses based on 6- or 12-month follow-up, definition thresholds, clinical frequent user subgroups, and intervention types. Studies reporting homelessness found consistent improvements in stable housing. Overall, interstudy heterogeneity was high. CONCLUSIONS: Interventions targeting frequent ED users appear to decrease ED visits and may improve stable housing. Future research should examine cost-effectiveness and adopt standardized definitions.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".