An Effective Tool to Enhance a Culture of Patient Safety and Assess the Risks of Medication Use Systems
Bibliographic record
Abstract
INTRODUCTION Adverse events involving medication use represent a significant patient safety issue in Canada. This was most recently identified through the findings of the Canadian Adverse Events Study, released in May 2004 (Baker et al.) One strategy for addressing this issue is to utilize a systems approach to patient safety rather than focusing on individual performance. Practitioners, however, need tools to assist them in identifying system weaknesses as well as guidance and direction for improvement. This paper describes the Canadian experience with such a tool; namely, the acute care hospital Medication Safety Self-AssessmentTM (MSSA), which was designed to assist hospitals to identify areas of risk in their medication use systems. The MSSA, originally developed by the Institute for Safe Medication Practices (ISMP) in the United States, was adapted for use in Canada in 2002 by ISMP Canada (with support from the Ontario Ministry of Health and Long-Term Care). The MSSA is a comprehensive survey tool for use by a multidisciplinary hospital team. The tool consists of 195 evaluative characteristics that serve to assess the safety of medication practices within the hospital and identify opportunities for improvement. Most of the characteristics represent system improvements ISMP and ISMP Canada have recommended in response to analysis of medication errors or problems identified during on-site consultations. SURVEY FORMAT AND METHODOLOGY The MSSA is divided into 10 key elements of safe medication use and then subdivided into 20 core characteristics (see Appendix 1). Each core characteristic section is made up of representative individual characteristics. Hospitals are asked to rate their compliance with each individual characteristic using the following scale:
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".