A qualitative study exploring hospital-based team dynamics in discharge planning for patients experiencing delayed care transitions in Ontario, Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,018 | 0,008 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».