Patient Perspectives on Communication Pathways After Orthopedic Surgery and Discharge and Evaluation of Team-Based Digital Communication: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: The transition from hospital to home after orthopedic surgery requires smooth communication and coordination between patients and their team of care to avoid fragmented care pathways. Digital communication is increasingly being used to facilitate easy and accessible asynchronous communication between patients and health care professionals across settings. A team-based approach to digital communication may provide optimized quality of care in the postoperative period following orthopedic surgery and hospital discharge. OBJECTIVE: This study was divided into two phases that aimed to (1) explore the perspectives of patients undergoing orthopedic surgery on current communication pathways at a tertiary hospital in Denmark and (2) test and explore patients' experiences and use of team-based digital communication following hospital discharge (eDialogue). METHODS: A triangulation of qualitative data collection techniques was applied: document analysis, participant observations (n=16 hours), semistructured interviews with patients before (n=31) and after (n=24) their access to eDialogue, and exploration of use data. RESULTS: Findings show that patients experience difficult communication pathways after hospital discharge and a lack of information due to inadequate coordination of care. eDialogue was used by 84% (26/31) of the patients, and they suggested that it provided a sense of security, coherence, and proximity in the aftercare rearranging communication pathways for the better. Specific drivers and barriers to use were identified, and these call for further exploration of eDialogue. CONCLUSIONS: In conclusion, patients evaluated eDialogue positively and suggested that it could support them after returning home following orthopedic surgery.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".