Selection of Quality Indicators in IBD: Integrating Physician and Patient Perspectives
Bibliographic record
Abstract
Background: Variation in clinical practice exists in many aspects of inflammatory bowel disease (IBD) care. Our aim was to develop a comprehensive set of quality indicators (QIs) to be measured in view of improving the quality of IBD care provided in clinical practice. This initiative was part of a global Canadian quality initiative PACE (Promoting Access and Care through Centres of Excellence). Methods: A modified RAND appropriateness method was used to identify and rate structure, process, outcome, and patient-derived QIs of IBD care. The process included a comprehensive literature search yielding a broad list of QIs, the online selection of QIs by a core expert panel, the selection of patient-derived QIs from 4 patient focus groups, and the subsequent selection of QIs by a multidisciplinary panel, followed by a moderated in-person multidisciplinary meeting during which each indicator was rated for importance and feasibility of measurement. Predetermined cutoffs for mean score and degree of disagreement were used to select the final list of QIs. Results: Forty-five QIs, including 6 that were patient-derived, were selected. Nine structure QIs addressed aspects related to the services and specialist care offered at an IBD unit or clinic. Thirty process indicators included administrative and workflow processes, features related to IBD therapy, surveillance, vaccination, and risk management. Six outcome QIs included measures of healthcare utilization, steroid use, and patient satisfaction. Conclusions: Forty-five QIs including patient-derived indicators were selected through an iterative process. These indicators can be used to measure and improve the quality of care provided to IBD patients. 10.1093/ibd/izy259_video1izy259.video15828250213001.
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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 | 0.155 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".