The Development and Implementation of a Quality Improvement Review Committee (QIRC): An Ethical and Pragmatic Imperative
Bibliographic record
Abstract
Background: In complex academic healthcare systems, quality improvement (QI) projects designed to improve care and enhance learning proliferate, yet there is considerable variation with respect to how, or even whether, these projects receive ethical oversight. As a result of a high volume of projects that were submitted to one of our research ethics board (REB) panels, but deemed not-research and therefore not eligible for review, questions at our organization began to surface with respect to how the ethical dimensions of QI projects might be assessed, and which institutional approvals might be required to ensure compliance with emerging normative standards. Methods: A mixed-methods environmental scan led to a retrospective quantitative analysis of our organization’s QI projects coupled with in-depth qualitative consultations with staff, physicians, and learners across our health network. REB exemptions were analyzed via run charts to assess baseline QI project volumes and thematic analyses were conducted on field notes from 133 stakeholder consultations. Results: During a 34-month period, 117 REB exemption letters were issued for QI projects. Consultations identified the need for: a clearly defined ethical review process for QI projects, appropriate governance structures, and opportunities to identify and mitigate risk. Respondents also spoke to the ethical imperative to conduct QI initiatives. This paper discusses how these themes contributed to the development and implementation of our Quality Improvement Review Committee (QIRC). Conclusion: Since 2020, over 840 projects have been reviewed by our QIRC, with a view toward mitigating risks for patients, staff, and QI project teams across UHN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.687 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.035 | 0.025 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.026 | 0.036 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".