Improving Timeliness in Surgical Discharge Summary Distribution: A Quality Improvement Initiative
Bibliographic record
Abstract
Objective To implement a quality improvement initiative to achieve an institutional targeted discharge summary distribution metric of 50% within 48 hours of patient discharge from hospital within an academic tertiary care otolaryngology–head and neck surgery department. Methods A pre‐ and postintervention study was conducted. Process mapping was performed. Interventions included education and engagement, implementation of auto‐authentication (distribution immediately following transcription without review by the most responsible physician), and audit and feedback. The percentage of discharge summaries dictated with the auto‐authentication code was evaluated. Process measures were collected for 12 months pre‐ and postimplementation. Balancing measures included workload and revisions to auto‐authenticated notes. Analysis included summary statistics, statistical process control charting, and unpaired t tests. Results The mean ± SD percentage of discharge summaries distributed within 48 hours increased from 19% ± 6.4% preintervention to 54% ± 20% postintervention (P <. 0001). Seventy‐four percent of discharge summaries were dictated via the auto‐authentication code. The target metric was met in 71% of discharges with the auto‐authentication codes as compared with 26% with non–auto‐authentication. The interventions did not result in any change to perceived workload, and the incidence of auto‐authentication revisions was <1%. The results were sustained with an increase of 72% the following quarter. For fiscal year 2021‐2022, performance remained sustained with an 85% completion rate. Discussion Our surgical department exceeded and sustained the targeted metric for timely discharge summary distribution using a quality improvement approach. Implications for Practice Timely distribution of discharge summaries optimizes patients’ transitions of care and can be achieved through stakeholder education and engagement, auto‐authentication, and audit with feedback.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".