Enhancing Emergency Department Experiences for Older Adults: A Rapid Review (2020-2025)
Bibliographic record
Abstract
OBJECTIVES: Older adults (65+) account for nearly a quarter of all emergency department (ED) visits, yet consistently report challenges in care experiences, including communication gaps, long wait times, and a lack of age-appropriate services. This systematic rapid review synthesizes recent evidence on ED interventions that improved older adults’ care experiences. METHODS: We conducted the review guided by the Joanna Briggs Institute convergent integrated approach. MEDLINE, CINAHL, Web of Science, and AgeLine were searched (January 2020-July 2025). Eligible studies examined ED-based interventions for older adults ≥ 65 years and reported patient experience outcomes. Three reviewers independently screened, extracted, and appraised studies using the Mixed Methods Appraisal Tool. Findings were synthesized thematically across intervention domains. RESULTS: Ten studies met the inclusion criteria (i.e., 5 quantitative, 2 mixed-methods, and 3 qualitative). Interventions included multidisciplinary geriatric teams, frailty-specific pathways, redesigned discharge tools, comfort carts, assistive devices, advance care planning, and elder mistreatment screening. Interventions improved satisfaction, dignity, independence, and perceptions of care quality across four countries (Australia, Ireland, the Netherlands, and the United States of America). Frailty-focused programs and trauma-informed screening were broadly acceptable when framed respectfully. Structured communication redesigns and environmental modifications enhanced patient trust and comfort. Common limitations included small samples and single-site designs. CONCLUSIONS: Embedding geriatric expertise, structured communication, environmental supports, and trauma-informed practices improves older adults’ perceptions of ED experiences. Future research should prioritize multi-site evaluations, cost-effectiveness, and integration of patient-reported experience measures into routine monitoring.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".