Improving Timeliness of Patient Discharge Summaries within Orthopaedic Surgery: A Quality Improvement Initiative
Bibliographic record
Abstract
Purpose The discharge summary is crucial for transitioning from inpatient to outpatient care, serving as a key communication tool between hospital and primary care providers. Our multi-site, high-volume tertiary care orthopedic division was identified as a low performer in completing discharge summaries within the institutional 48-hour target. This quality improvement initiative aimed to improve the timeliness of discharge summary completion, targeting >80% completion within 48 hours by June 30th, 2023. Objective This study aimed to achieve at least 80% discharge summary completion within 48 hours in the Division of Orthopaedic Surgery. Methods An interrupted time series study, based on the Institute for Healthcare Improvement’s ‘Model for Improvement’, was conducted. A quality committee, including representatives from each Clinical Teaching Unit (CTU), a resident representative, and two quality facilitators, utilized root cause analysis, stakeholder interviews, process mapping, and driver diagrams. Interventions included implementing an auto-authenticate option, an audit and feedback system, and engaging medical residents and nurse practitioners. Monthly data tracking used statistical process control charts. Results Pre-implementation, the completion rate within 48 hours was 48%. Auto-authentication significantly improved completion rates, with 63% completed within 48 hours compared to 16% with manual authentication. Overall, completion rates rose from 48% to 89%, with auto-authentication usage increasing from 50% to 91%. Conclusion This initiative significantly improved timely discharge summary completion, meeting targets. Key success factors included stakeholder engagement, timely performance data, and effective root cause analysis. Medical resident involvement and the audit and feedback system fostered improvements, enhancing patient transitions from hospital to outpatient care.
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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 | 0.041 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".