Interventions to Reduce Repeat Presentations to Hospital Emergency Departments for Mental Health Concerns: A Scoping Review of the Literature
Bibliographic record
Abstract
BACKGROUND: The number of readmissions to the emergency department (ED) for mental health services each year is significant, which increases healthcare costs and negatively affects the morale and quality of life of patients and their families. OBJECT: This scoping review aimed to establish a better understanding of interventions that have been implemented to reduce psychiatric patient readmission and ED use within the ED, to identify areas for improvement, and therefore to assist in the development of more effective actions in the future. METHOD: The scoping review was conducted on several bibliographic databases to identify relevant studies. Two researchers independently screened and reviewed titles, abstracts, and full-text articles that met the inclusion criteria. Using Covidence software, 26 out of 6951 studies were eligible for inclusion in this scoping review based on the PRISMA checklist. Data were extracted, collated, summarized, presented, and discussed. RESULT: This review identified 26 studies which examined interventions aimed to reduce ED visits, such as the High Alert Program (HAP), the Patient-Centered Medical Home (PCMH), the Primary Behavioral Health Care Integration (PBHCI), and the Collaborative Care (CC) Program, etc. Twenty-three of the studies were conducted in North America, while the rest were conducted in Europe and Australia. A total of 16 studies examined interventions directed to any mental health conditions, while the rest addressed specific health conditions, such as substance use disorders, schizophrenia, anxiety, depression. Interventions involved comprehensive and multidisciplinary services, incorporation of evidence-based behavioral and pharmacological strategies, and emphasized the case management that was found to be effective. Additionally, there was a marked consideration for diverse mental health groups, such as those with substance use disorder and of young age. Most interventions showed a positive effect on reducing psychiatric ED visits. CONCLUSION: Various initiatives have been implemented worldwide to reduce the number of emergency department visits and the associated burden on healthcare systems. This review highlights the greater need for developing more accessible interventions, as well as setting up a comprehensive community health care system aiming to reduce frequent ED presentations.
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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 | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".