Integrating care for frequent users of emergency departments: implementation evaluation of a brief multi-organizational intensive case management intervention
Bibliographic record
Abstract
BACKGROUND: Addressing the needs of frequent users of emergency departments (EDs) is a health system priority in many jurisdictions. This study describes stakeholder perspectives on the implementation of a multi-organizational brief intervention designed to support integration and continuity of care for frequent ED users with mental health and addictions problems, focusing on perceived barriers and facilitators to early implementation in a large urban centre. METHODS: Coordinating Access to Care from Hospital Emergency Departments (CATCH-ED) is a brief case management intervention bridging hospital, primary and community care for frequent ED users experiencing mental illness and addictions. To examine barriers and facilitators to early implementation of this multi-organizational intervention, between July and October 2012, 47 stakeholders, including direct service providers, managers and administrators participated in 32 semi-structured qualitative interviews and one focus group exploring their experience with the intervention and factors that helped or hindered successful early implementation. Qualitative data were analyzed using thematic analysis. RESULTS: Stakeholders valued the intervention and its potential to support continuity of care for this population. Service delivery system factors, including organizational capacity and a history of collaborative relationships across the healthcare continuum, and support system factors, such as training and supervision, emerged as key facilitators of program implementation. Operational challenges included early low program referral rates, management of a multi-organizational initiative, variable adherence to the model among participating organizations, and scant access to specialty psychiatric resources. Factors contributing to these challenges included lack of dedicated staff in the ED and limited local system capacity to support this population, and insufficient training and technical assistance available to participating organizations. CONCLUSIONS: A multi-organizational brief intervention is an acceptable model to support integration of hospital, primary and community care for frequent ED users. The study highlights the importance of early implementation evaluation to identify potential solutions to implementation barriers that may be applicable to many jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".