Striking the right balance between accountability and quality improvement: a discharge summary timeliness tale
Bibliographic record
Abstract
BACKGROUND: The timely distribution of discharge summaries within 48 hours can play an important role in ensuring safe patient care transitions and reducing readmission. Like other academic centres, we struggled with achieving a regulator mandated outcome of discharge summary authentication within 48 hours. STUDY AIM: To increase the percentage of discharge summaries authenticated within 48 hours from a baseline of 62% to 75% over 1 year on six acute medicine teams. METHODS: The model for improvement guided this quality improvement (QI) initiative. Outcome measures included the percentage of discharge summaries authenticated within 48 hours, and the average time from discharge to authentication. Balancing measures were a high-level process measure related to quality; editing behaviours before authentication. Data were analysed using a pre-post design and represented via statistical process control charts, P chart and XbarS charts. RESULTS: While the primary aim was achieved, it was not sustained. By contrast, the time to authentication decreased from 53 hours to 38 hours and was sustained. The percentage of editing of summaries also exhibited significant variability. The 38% who demonstrated considerable improvement in time to authentication had decreased rates of consultant and trainee editing. In contrast, those who edited before authentication took longer to authenticate with a median difference of 5 hours (p<2.2e-16) and were less likely to meet the 48-hour target (OR 0.67, 95% CI 0.6028, 0.7521). DISCUSSION: Our findings are important for both regulators and QI practitioners and highlight the importance of defining clinically meaningful targets while also considering their impact on quality and education. While we cannot be certain that summary quality was compromised in those without editing, the association between time to authentication and editing behaviour is highly suggestive. Moreover, it was also associated with a decrease in trainee editing, which is concerning from an educational perspective.
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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 | 0.065 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".