A qualitative study exploring hospital-based team dynamics in discharge planning for patients experiencing delayed care transitions in Ontario, Canada
Bibliographic record
Abstract
Background: An increased focus has been placed on discharge planning, in order to reduce hospital length of stay and delayed discharges, and to improve continuity of care. Several benefits to team-based approaches for discharge planning have been noted; however, professional hierarchies remain. As such, challenges related to power dynamics are commonly experienced within teams who are dealing with care transitions for patients with delayed discharge. Further to challenges experiences, there remains a gap in understanding team dynamics across integrated care teams, specifically as they relate to discharge delays. Objective: The objective of this study was to explore experiences with team-based discharge processes, specifically identifying what was working well and challenges encountered to outline how teams can function to better support transitions for patients experiencing a delayed discharge. Methods: A descriptive qualitative study was conducted. Participants included hospital-based healthcare providers, managers, and organizational leaders who had experience with delayed discharges. Individuals were recruited from two diverse health regions in Ontario, Canada. Between December 2019 and October 2020, in-depth, semi-structured interviews were conducted in-person or virtually. The interviews were audio-recorded for transcription. Using a directed content analysis approach, data were analyzed both inductively and deductively. Results: Thirty individuals participated in this study. The majority of participants were based in-hospital and held the following roles: social workers, discharge planners, clinical and project managers, physicians, and team leads. Despite being situated in hospital, several providers interfaced frequently with community organizations. We organized our findings into three main categories: (1) collaboration with physicians makes a difference; (2) leadership should meaningfully engage with frontline providers and (3) partnerships across sectors are critical. Participants described the importance of regular physician engagement, as equal members of the team, to improve consistent communication, relationship building between providers, and accessibility. A dedicated senior leader, who advocated for the team and ensured members of the team were treated as equals, was described as contributing positively to team dynamics. Cross-sectoral partnerships were enhanced by having an integrated community-based provider within the discharge planning team, placing focus on collaborative practice with combined discharge planning meetings, and physically embedding care coordinators in the hospital. Implications: Based on our findings, recommendations for improving how teams function to support transitions for patients experiencing a delayed discharge include: consistent collaboration with physicians, engagement from senior leadership by seeking feedback from frontline providers through co-design, and active integration the community sector in discharge planning. Conclusions: Team-based approaches for improving delayed discharge and supporting care transitions can offer a number of benefits. However, to optimize team dynamics and functioning across sectors for discharge planning, increased emphasis is needed on authentic engagement and integration across sectors.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".