P035: Development of a province-wide audit program for return visits to the emergency department
Bibliographic record
Abstract
Introduction: Routine auditing of charts of patients with an emergency department (ED) return visit (RV) resulting in hospital admission can uncover quality and safety gaps in care. This feedback can be helpful to clinicians, administrators, and leaders working to improve clinical outcomes, increase patient satisfaction, and promote high-value care. Health Quality Ontario (HQO) has been tasked by Ontario’s Ministry of Health and Long-Term Care (MOHLTC) to manage the newly created ED RV Quality Program (RVQP), which mandates EDs participating in the Pay-for-Results (P4R) program to audit a minimum of 25-50 RVs/year. The goal of the first-ever ED-specific province-wide Quality Improvement (QI) initiative of this kind is to promote a culture of QI that will lead to improved patient care. Methods: Participating hospitals receive quarterly confidential reports from Access to Care (ATC) that show their and other hospitals’ rates of RVs, as well as identifying information for patients meeting RV inclusion criteria at their ED (within 72 hrs of index visit, or within 7 days with specific diagnoses). HQO has partnered with QI experts and ED physician-leaders to develop various guidance materials. These materials have been disseminated through various media. Hospitals are conducting audits to identify underlying quality issues, take steps to address the underlying causes, and submit reports to HQO. A taskforce will then analyze clinical observations, summarize key findings and lessons learned, and share improvements at a provincial level through an annual report. Results: Since its launch in April 2016, 73 P4R and 16 voluntarily enrolled non-P4R hospitals (which collectively receive approximately 90% of ED visits in the province) are participating in the RVQP. ED leaders have engaged their hospital’s leadership to leverage interest and resources to improve patient care in the ED. To date, hospitals have conducted thousands of audits and have identified quality and safety gaps to address, which will be analyzed in February 2017 for reporting shortly thereafter. These will inform QI endeavours locally and provincially, and be the largest source of such data ever created in Ontario. Conclusion: The ED RVQP aims to create a culture of continuous QI in the Ontario health care system, which provides care to over 13.8 million people. Other jurisdictions can replicate this model to promote high-quality 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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".