Nurses’ Experiences of Interprofessional Collaboration in Digitally Supported Hospital Discharge Planning: Qualitative Study
Bibliographic record
Abstract
Background: Effective interprofessional collaboration (IPC) in patient discharge planning is essential for ensuring continuity of care, improving patient outcomes, and strengthening coordination among health care professionals. Nurses often serve as primary coordinators due to their continuous engagement in patient care. However, the implementation of IPC continues to face barriers at the individual, team, and organizational levels. Many hospitals have adopted digital tools, such as integrated patient progress notes (IPPNs), to facilitate information sharing. Nevertheless, the use of these tools to support IPC remains suboptimal and has been insufficiently explored, particularly within the Indonesian digital health context. Objective: This study aimed to explore how IPPNs support IPC during patient discharge planning, particularly from the nursing perspective. Methods: A qualitative phenomenological study was conducted at a hospital in Bukittinggi, West Sumatra. Data were collected through in-depth interviews and a focus group discussion involving 9 purposively selected health care professionals. Thematic analysis was used to identify key patterns related to IPC practices and communication dynamics involving the use of IPPNs. Results: The findings revealed 3 main themes: (1) individual understanding and motivation in IPC, encompassing motivation, role expectations, personality style, and professional strengths; (2) team dynamics, including leadership, management, communication, and social support; and (3) organizational support for IPC, comprising collaborative culture, institutional goals, organizational structures, and the organizational environment. Participants perceived IPC as essential yet inefficiently utilized for coordinating patient care across disciplines, with limitations in standardization, accessibility, and clarity of digital documentation hindering effective collaboration. Conclusions: This study demonstrated that IPC practices were shaped by individual, team, and organizational factors, with digital communication holding a potentially transformative role in facilitating collaboration. These findings contribute to existing knowledge by highlighting context-specific challenges in Indonesian digital health settings, including digital literacy, system usability, and institutional support, which influence IPC and discharge planning outcomes. Integrating digital optimization within IPC frameworks may represent a valuable strategy for advancing digital health practices.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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 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".