Identification of Medication Safety Indicators in Acute Care Settings for Public Reporting in Ontario
Bibliographic record
Abstract
In healthcare settings, indicators are useful tools to assess the structure, process and outcomes of care. Moreover, when used to report to the public, indicators ensure greater transparency for our healthcare system. The purpose of this study was to identify in acute care settings three medication safety indicators that are suitable for public reporting in Ontario. A multi-phase process was developed that included a literature review, compilation and evaluation of possible indicators and a consensus-generation process involving a focus group (modified nominal group technique) with Ontario healthcare experts from various disciplines. More than 300 potential medication safety indicators were identified through the literature review. Two analysts, working independently and using a defined set of selection criteria, narrowed the focus to 49 and subsequently 12 candidate indicators. A focus group of leading experts across the healthcare fields in Ontario was convened and reached consensus on three indicators. These three indicators focused on the areas of venous thromboembolism prevention, acute myocardial infarction discharge medications and medication reconciliation. This report describes a multi-phase process undertaken by the Institute for Safe Medication Practices Canada to identify in acute care settings three medication safety indicators suitable for public reporting in Ontario. These indicators point to important areas in medication safety at which deficiencies can result in significant patient harm. There is a potential for these indicators to provide hospitals and healthcare providers with tangible and realistic mechanisms for measuring performance and, ultimately, improving the quality of 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 | 0.039 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".