Perceived discharge quality and associations with hospital readmissions and emergency department use: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: At hospital discharge, care is handed over from providers to patients. Discharge encounters must prepare patients to self-manage their health, but have been found to be suboptimal. Our study objectives were to describe and determine the correlates of perceived discharge quality and to explore the association between perceived discharge quality and postdischarge outcomes. METHODS: We conducted a prospective cohort study in medical inpatients admitted to a tertiary care hospital in Calgary, Canada. Perceived discharge quality was measured by the Care Transitions Measure (CTM). Linkage to administrative databases provided data for the composite outcome-90-day hospital readmission or emergency department visit. Logistic regression modelling was used to determine the association between global CTM scores, and the individual CTM components, and the composite outcome. RESULTS: A total of 316 patients were included in the analysis. The median CTM score was 80.0 (IQR 66.6-100.0). The distribution of CTM scores were significantly different based on comorbidity burden, with the median and maximum CTM scores being lower and the IQR being narrower, for those with six or more comorbidities compared with those with fewer comorbidities. CTM scores were not associated with the composite outcome, though a single CTM item-not understanding warning signs and symptoms-was (adjusted OR 3.46 (95% CI 1.02 to 11.73)). CONCLUSION: Perceived quality of discharge varies based on patient burden of comorbidities. While global perceived discharge quality was not associated with postdischarge outcomes, lack of patient understanding of warning symptoms was. Discharging healthcare teams should pay special attention to these priority patient groups and specific discharge process components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".