P034: Pediatric emergency department return visits: a proactive approach to quality improvement
Bibliographic record
Abstract
Introduction: Emergency Department (ED) return visits leading to admission (RVs) are a well-recognized quality metric that can potentially signal gaps in patient care. Routine capture, investigation and monitoring of monthly ED RVs provides a better understanding of patient and visit- level factors associated with a return, which can then inform system-level quality improvement (QI) opportunities. The objective of this study is to develop a sustainable database that routinely tracks and analyzes pediatric ED RVs in a large Canadian children’s hospital to understand recurring themes and inform QI initiatives. Methods: Using a computerized record system, all 72-hour RVs are collected and reviewed for patient and visit-level variables. Clinicians receive monthly notification of their RVs and assist with completing root cause analyses. Ongoing cumulative analyses using descriptive statistics and t-test analysis are reviewed to identity trends and predictors of RVs. Targeted solutions are sought to address system-level themes through educational, quality, safety and administrative avenues. Results: The RV database contains almost three years of data analyzing approximately 1,500 cases, equaling 0.75% of our annual ED patient volumes. RVs have higher acuity scores on both their index and return visit (P=0.001) and children under 12 months of age have significantly higher rates of return (24% vs 16%, P<0.001). A consultation service was involved during 31% of the index ED visits, with the top three consultants being Hematology/Oncology (23%), General Surgery (12%), and Neurology (8%). The root cause of the majority of RVs were related to disease progression (65%), while 8% were call-backs for positive blood cultures or discrepant results, and 6% were categorized as a misdiagnoses. Completed quality improvement initiatives to date include the ED Sickle Cell Optimization Program, the Culture Follow-up and Escalation Algorithm, and the Young Infant Fever Pathway and Order Set. Conclusion: Routine monitoring and investigation of ED RVs provides a proactive approach to seeking improvement opportunities. With a better understanding of specific patient and visit-level factors associated with RVs, future system-level quality improvement initiatives can be targeted.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".