Integrated case management between primary care clinics and hospitals for people with complex needs who frequently use healthcare services in Canada: A multiple-case embedded study
Bibliographic record
Abstract
INTRODUCTION: Case management (CM) is recognized to improve care integration and outcomes of people with complex needs who frequently use healthcare services, but challenges remain regarding interaction between primary care clinics and hospitals. This study aimed to implement and evaluate an integrated CM program for this population where nurses in primary care clinics worked with a hospital case manager. METHODS: A multiple embedded case study was conducted in the Saguenay-Lac-Saint-Jean region (Québec, Canada), in four dyads including a clinic and a hospital. Mixed data collection included, at baseline and 6 months, interviews and focus groups with stakeholders, patient questionnaires (patient experience of integrated care and self-management), and emergency department (ED) visits in the previous 6 months. RESULTS: Integrated CM implementation was optimal when all stakeholders provided collective leadership, and were supportive of the program, particularly the physicians. The 6-month program enabled the observation of positive qualitative outcomes in most clinic-hospital dyads where implementation occurred. Full implementation was associated with improved care integration. DISCUSSION AND CONCLUSION: Integrated CM between primary care clinics and hospitals is a promising innovation to improve care integration for people with complex needs who frequently use healthcare services. Collective leadership and physicians' buy-in to integrated CM are important to foster the implementation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".