Rethinking the Biohazardous Bodily Fluids Alert for Improved Workflow and Safety
Bibliographic record
Abstract
Ensuring clinician safety in health care settings is critical, particularly regarding exposure to hazardous drugs and bodily fluids, which can be carcinogenic, teratogenic, genotoxic, or cause organ toxicity at low doses. At SickKids a safety issue arose when a clinician was unknowingly exposed to hazardous bodily fluids due to inadequate communication of a patient's hazardous medication status.This clinical decision support (CDS) redesign aimed to reduce alert fatigue while ensuring timely team awareness to minimize hazardous bodily fluid exposure risk. This case study aims to explore how redesigning a CDS system addressed the dual challenge of maintaining safety communication while minimizing alert fatigue and improving workflow integration.In 2018, a biohazardous bodily fluids alert was introduced within the hospital's electronic patient record (EPR) to raise awareness. However, its frequent and disruptive nature resulted in a 0% alert action rate and 89 unactionable clinician hours over a 90-day period. Feedback collected over 42 months revealed clinician frustration and desensitization due to the alert's timing and frequency. Using a human-centered design approach, the alert was redesigned from an interruptive pop-up to a passive notification embedded within the patient's storyboard.The redesigned alert allowed clinicians to review hazardous status information without immediate interruptions, reducing workflow disruption while maintaining its critical safety function. This approach effectively balanced safety communication with clinicians' need for efficient workflows, addressing the root cause of alert fatigue.This case study highlights the importance of ongoing CDS evaluation and redesign to enhance clinician safety, minimize alert fatigue, and improve workflow integration. Future evaluations will assess the redesign's effect on personal protective equipment compliance and clinician burnout.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".