Emerging lessons from experiences at transitions in care among hospitalised patients with cancer with postdischarge frequent emergency department use: a qualitative study using linked clinical and patient-reported interview data from Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: While teamwork is essential to providing high-quality patient-centred care, challenges in interprofessional collaboration and decision-making in hospital settings are common, especially for patients with cancer. The purpose of this qualitative study was to identify emerging themes and potential challenges related to hospital discharge experiences among patients hospitalised for cancer who became frequent emergency department (ED) users postdischarge. METHODS: A cohort of patients with cancer discharged from an academic health centre in Montreal (Canada) between October 2014 and November 2016 was assembled. Using health administrative claims from the provincial universal healthcare programme, frequent ED (FED) users were identified as patients who had a ≥4 ED visits in the year following hospital discharge. Qualitative analysis of transcripts from semistructured telephone interviews conducted with patients 25-30 days' postdischarge was used for in-depth exploratory analyses to characterise hospital discharge experiences and transition process from the hospital to the community. RESULTS: Overall, 182 (14.5%) of 1253 patients with cancer who became FED users were included in this study. The mean age was 69.1 (SD=11.5), 59.9% (n=109) were male, and the most frequent cancers were 80 (43.9%) respiratory and 52 (28.6%) upper digestive cancer. Content analyses revealed six emerging themes from the FED patient interviews. Overall, these included (1) incomplete communication of information, (2) hospital discharge planning, (3) coordinating care among team members, (4) follow-up with outpatient providers, (5) monitoring and managing symptoms after discharge and (6) enlisting help of social and community supports. CONCLUSIONS: Using integrated data from clinical, administrative claims and patient interviews, this study provided insights into the challenges related to hospital discharge experiences and transition into community among hospitalised patients with cancer with FED use. Application of our findings could assist in hospital discharge preparation and improvement in healthcare delivery and health outcomes. TRIAL REGISTRATION NUMBER: NCT01179867.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 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".