Implementing PODS (Patient Oriented Discharge Summary) in an acute medical urban health setting in Vancouver, Canada
Bibliographic record
Abstract
The transition from hospital to home or community is a vulnerable time for patients and families, who face risks associated with misunderstanding instructions about medications, self-monitoring and when to seek emergency care. The quality of the discharge process can have a significant impact on patient confidence, overall patient experience, ability to manage health at home, and hospital readmission rates. Patient Oriented Discharge Summary (PODS) is a standardized form and set of process changes, utilized to overcome communication barriers faced at discharge. We implemented PODS in two Acute Medicine units of a tertiary care hospital in western Canada and used a mixed-methods approach to evaluate the four process changes (PODS form, use of teach-back, engagement of caregivers in discharge teaching, follow-up phone calls). Evaluation showed that 60% of patients received PODS and 87% found the form helpful. There was a large increase in the percentage of patients who felt adequately prepared at the time of discharge, and a 10% increase in the number of patients who rated their overall hospital experience positively. Healthcare providers reported that using PODS they were more confident that patients were adequately prepared to return home. The update of PODS on the implementation units has been sustained at 60% for 18 months. Implementation of the PODS form and process can be accomplished with an interdisciplinary team, leadership support and by working closely with Patient Family Partners. PODS can improve the discharge process even in the complex urban acute medical environment in ways that offer wide-reaching benefits. Experience Framework This article is associated with the Quality & Clinical Excellence lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".