Applying Human Factors Methods to Improve Healthcare Risk Management Tools
Bibliographic record
Abstract
The Healthcare Insurance Reciprocal of Canada (HIROC) is a not-for-profit medical malpractice insurance reciprocal that has a vision of partnering to create the safest healthcare system. Each year, patients die from preventable patient safety incidents in Canada. A proactive focus on risk management and embedding safety into healthcare systems is key to improving patient safety. HIROC conducted semi-structured interviews to help identify usability areas of interest for two primary risk management tools: The Risk Assessment Checklist and the Risk Register. A total of 16 participants from HIROC Subscribers, all with experience in risk management, quality improvement or patient safety, volunteered to partake in the semi-structured interviews. A thematic analysis of the data collected informed usability improvements. For the Risk Assessment Checklist, participants indicated that the tool is informative as it helps create risk management awareness across their organizations. Participants found the Risk Assessment Checklist interface easy to use and are pleased that submitting their self-assessments is a streamlined process. For the Risk Register, participants reported that the tool is simple and easy to use. Specifically, they find value in having an electronic system that keeps them organized and provides a way for them to track and trend their progress. Participants identified some usability concerns that the research team addressed with proposed design reflections informed by Jakob’s Ten Usability Heuristics (Nielsen, 1994).
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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 | 0.162 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".