Managing intergroup silos to improve patient flow
Bibliographic record
Abstract
BACKGROUND: Health care managers face the critical challenge of overcoming divisions among the many groups involved in patient care, a problem intensified when patients must flow across multiple settings. Surprisingly, however, the patient flow literature rarely engages with its intergroup dimension. PURPOSE: This study explored how managers with responsibility for patient flow understand and approach intergroup divisions and "silo-ing" in health care. METHODOLOGY/APPROACH: We conducted in-depth interviews with 300 purposively sampled senior, middle, and frontline managers across 10 Canadian health jurisdictions. We undertook thematic analysis using sensitizing concepts drawn from the social identity approach. RESULTS: Silos, at multiple levels, were reported in every jurisdiction. The main strategies for ameliorating silos were provision of formal opportunities for staff collaboration, persuasive messages stressing shared values or responsibilities, and structural reorganization to redraw group boundaries. Participants emphasized the benefits of the first two but described structural change as neither necessary nor sufficient for improved collaboration. CONCLUSION: Silos, though an unavoidable feature of organizational life, can be managed and mitigated. However, a key challenge in redefining groups is that the easiest place to draw boundaries from a social identity perspective may not be the best place from one of system design. Narrowly defined groups forge strong identities more easily, but broader groups facilitate coordination of care by minimizing the number of boundaries patients must traverse. PRACTICE IMPLICATIONS: A thoughtfully designed combination of strategies may help to improve intergroup relations and their impact on flow. It may be ideal to foster a "mosaic" identity that affirms group allegiances at multiple levels.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".