Identifying Current Practices and Areas for Improvement in Medication Management During Care Transition Through an Interprofessional Collaboration Framework
Bibliographic record
Abstract
Léa Solh Dost,1– 3 Gaëlle Maillard,1,2 Evelina Cardoso,1,2 Marie P Schneider1– 3 1School of Pharmaceutical Sciences, University of Geneva, Genève, Switzerland; 2Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Genève, Switzerland; 3Pharma24, Academic Community Pharmacy, Geneva, 1205, SwitzerlandCorrespondence: Marie P Schneider, School of Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Rue Michel Servet 1, Genève, 1211, Switzerland, Tel +41 22 379 53 16, Email marie.schneider@unige.chPurpose: Poor coordination and communication during care transitions can lead to medical errors, patient dissatisfaction, and hospital readmissions. The transition period from hospital to the first medical appointment is a high-risk and vulnerable time for patients, and a complex one for healthcare professionals. While interprofessional collaboration can improve the quality and safety of care, its implementation remains underexplored. This study examines the current state and areas for improvement in interprofessional medication management during the hospital discharge transition (from hospital discharge to first medical appointment) for patients self-managing their medications.Methods: A qualitative study was conducted using a serial focus group methodology with patients and healthcare professionals from hospital and community settings. Participants were sampled purposively. Discussions were audio-recorded, transcribed verbatim, and analysed using inductive thematic analysis. Thematic findings were categorised using the 2010 Canadian National Interprofessional Competency (CIHC) Framework, distinguishing between current practices and areas for improvement. Additionally, a classification questionnaire, adapted from the nominal group technique, was used to rank proposed improvement strategies based on their perceived impact and feasibility.Results: Twelve participants (10 healthcare professionals and two patients) contributed to four focus groups. The study identified strengths and areas for improvement in five of the six CIHC 2010 competency domains: 1. Interprofessional communication: present but needing better structure and proactivity; 2. Patient partnership: recognised but requiring more consistency; 3. Role clarification: unclearly defined, causing inefficiencies; 4. Team functioning: common in hospital settings, but inconsistent during transition; 5. Collaborative leadership: present but lacking clear coordination at handover. An overarching category, “Macro-level improvements” was introduced to highlight system-wide changes and the need for policy support to implement and sustain interprofessional collaboration.Conclusion: While existing practices emphasise interprofessional communication and patient involvement, role clarity and collaborative leadership remain significant challenges. Healthcare professionals are motivated and ready to collaborate, but policy and coordinated efforts among healthcare meso- and macro-entities are needed to implement sustainable interprofessional practice models, to increase quality of pharmaceutical care, and improve patient outcomes during care transition from hospital to home.Keywords: interprofessional collaboration, healthcare professionals, outpatient care, continuity of patient care, medication therapy management
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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 | 0.083 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.018 |
| 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".