Reducing Frequent Visits to the Emergency Department: A Systematic Review of Interventions
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to establish the effectiveness of interventions to reduce frequent emergency department (ED) use among a general adult high ED-use population. METHODS: Systematic review of the literature from 1950-January 2015. Studies were included if they: had a control group (controlled trials or comparative cohort studies), were set in an ED or acute care facility, and examined the impact of an intervention to reduce frequent ED use in a general adult population. Studies reporting non-original data or focused on a specific patient population were excluded. Study design, patient population, intervention, the frequency of ED visits, and costs of frequent ED use and/or interventions were extracted and narratively synthesized. RESULTS: Among 17 included articles, three intervention categories were identified: case management (n = 12), individualized care plans (n = 3), and information sharing (n = 2). Ten studies examining case management reported reductions in mean (-0.66 to -37) or median (-0.1 to -20) number of ED visits after 12-months; one study reported an increase in mean ED visits (+2.79); and one reported no change. Of these, 6 studies also reported reduced hospital costs. Only 1 study evaluating individualized care plans examined ED utilization and found no change in median ED visits post-intervention. Costs following individualized care plans were also only evaluated in 1 study, which reported savings in hospital costs of $742/patient. Evidence was mixed regarding information sharing: 1 study reported no change in mean ED visits and did not examine costs; whereas the other reported a decrease in mean ED visits (-16.9) and ED cost savings of $15,513/patient. CONCLUSIONS: The impact of all three frequent-user interventions was modest. Case management had the most rigorous evidence base, yielded moderate cost savings, but with variable reductions in ED use. Future studies evaluating non-traditional interventions, tailoring to patient subgroups or socio-cultural contexts, are warranted.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".