Yes, Doctors, You Were Right. The Data Were Wrong: One Organization’s Data Quality Journey
Bibliographic record
Abstract
In 2012, publicly released reports indicated that the health outcomes at St. Joseph's Health Centre, Toronto (SJHC), may not be of the same quality when compared with those at peer hospitals. This surprised the leaders within the organization given that SJHC had a sound reputation for quality and patient safety within the sector. As a result, SJHC's senior management and medical leadership identified clinical outcomes and data quality as items to be addressed within its enterprise risk management framework with a focus on the methods by which data were collected, coded and used by clinicians. The following article describes the approach SJHC used to improve the quality of its clinical data and how it changed physician participation in examining data designed to help inform and improve 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 | 0.101 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.030 | 0.027 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.010 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".