Implementation, spread and impact of the Patient Oriented Discharge Summary (PODS) across Ontario hospitals: a mixed methods evaluation
Bibliographic record
Abstract
Abstract Background Traditional discharge processes lack a patient-centred focus. This project studied the implementation and effectiveness of an individualized discharge tool across Ontario hospitals. The Patient Oriented Discharge Summary (PODS) is an individualized discharge tool with guidelines that was co-designed with patients and families to enable a patient-centred process. Methods Twenty one acute-care and rehabilitation hospitals in Ontario, Canada engaged in a community of practice and worked over a period of 18 months to implement PODS. An effectiveness-implementation hybrid design using a triangulation approach was used with hospital-collected data, patient and provider surveys, and interviews of project teams. Key outcomes included: penetration and fidelity of the intervention, change in patient-centred processes, patient and provider satisfaction and experience, and healthcare utilization. Statistical methods included linear mixed effects models and generalized estimating equations. Results Of 65,221 discharges across hospitals, 41,884 patients (64%) received a PODS. There was variation in reach and implementation pattern between sites, though none of the between site covariates was significantly associated with implementation success. Both high participation in the community of practice and high fidelity were associated with higher penetration. PODS improved family involvement during discharge teaching (7% increase, p = 0.026), use of teach-back (11% increase, p < 0.001) and discussion of help needed (6% increase, p = 0.041). Although unscheduled healthcare utilization decreased with PODS implementation, it was not statistically significant. Conclusions This project highlighted the system-wide adaptability and ease of implementing PODS across multiple patient groups and hospital settings. PODS demonstrated an improvement in patient-centred discharge processes linked to quality standards and health outcomes. A community of practice and high quality content may be needed for successful implementation.
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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 | 0.047 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".