A mapping review of interventions to address patients who frequently seek care in the emergency department
Bibliographic record
Abstract
BACKGROUND: The high utilization of acute care services, particularly emergency departments (ED), continues to be a significant concern for healthcare providers. Numerous approaches have been studied to meet the care needs of patients who frequently seek care in the ED; however, there is no comprehensive review of the current literature base. As such, a current understanding of the interventions initiated within the ED to address the needs of frequent users is required. This mapping review had three objectives: identify the characteristics associated with the need to frequently seek care in the ED; identify interventions implemented to address the needs of this population; and identify gaps in the current evidence base. METHODS: A knowledge map was created by scoping the literature to identify characteristics associated with frequent ED use and interventions implemented to address frequent use. Then, a literature search was conducted to determine what has been implemented by EDs to reduce frequent ED use. The literature was searched from 2013 to January 2023. MeSH terms and keywords were used to identify relevant studies. Studies implementing an intervention for those with characteristics associated with frequent ED use and reporting on ED use were included. RESULTS: Twenty-three (23) controlled trials and 35 observational studies were included. The most common populations were older adults, those with chronic conditions, and generic "frequent users". No studies assessed Indigenous Peoples or racial minorities, and few assessed patients with a disability or patients experiencing homelessness. The most common interventions were referrals, care plans, case management, care coordination, and follow-up phone calls. Most studies reported ED revisits, hospitalization, costs, length-of-stay, or outpatient utilization. Few assessed patient or staff perspectives. About one-third of studies (n = 24) reported significant reductions in ED revisits. CONCLUSIONS: Similar interventions, mainly focused on care coordination and planning, have been implemented to address frequent use of the ED. There are still significant gaps in the populations that have been studied. Efforts now must be undertaken to study more diverse populations whose care needs are not being met elsewhere and thus frequent the ED often.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".