An intervention to improve discharge summary completion rates within an Australian teaching hospital
Bibliographic record
Abstract
Objective: This study was designed to improve patient discharge summary completion rates directly following patient hospital discharge. The primary reason for this was to improve continuity of patient care and reduce hospital readmissions within 28 days.Methods: The researcher benchmarked the discharge summary completion rate before conducting individual feedback directly to clinicians. Content was deemed complete if the information was present and appropriate. Partially completed, unclear, or absent information was deemed outstanding. This information was gained by looking at the hospital’s patient records. The researcher benchmarked the readmission data. This data included establishing monthly patient discharges (excluding deaths) and the number of unplanned and unexpected readmissions within 28 days related to the primary admission. This information was used to compare pre-intervention to invention readmission rates.Results: The hospital’s total discharge completion rate statistically changed from 91.92% pre-intervention to 99.18% postintervention, with the biggest change occurring in Obstetrics and Gynaecology (O&G). O&G discharge completion rate improved from 46.94% pre-intervention to 98.84% post-intervention. A two sample t-test indicated that this difference was significant, t(2.0905) = 0.0458, p = .05. The readmission rates statistical changed from 0.49% pre-intervention to 0.26% during the intervention period. A two sample t-test indicated that this difference was significant, t(2.3679) = 0.04205, p = .05.Conclusions: This study provided evidence of the effectiveness of conducting audit and feedback sessions as it relates to patient discharge summaries and readmissions.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".