Identifying latent safety threats through varied modalities: bottom of form.

Authors

Mireille T Gharib, Patricia Trbovich, Walter Tavares, Andrew Petrosoniak, Seona Dunbar, Alison Dodds, Jabeen Fayyaz, Jonathan Pirie, Trevor Dell, Catharine M Walsh

Published in

BMJ quality & safety. Jul 23, 2026. Epub Jul 23, 2026.

Abstract

Patient safety and systems integration (PSSI) simulations help uncover hazards that jeopardise patient safety. Using direct observation, video analysis and systems-focused debriefing, they identify and categorise latent safety threats (LSTs). We aimed to compare the number, nature and potential risk of LSTs across the three modalities.
In a prospective observational cohort, six unannounced simulations were conducted in a paediatric emergency department during active shifts. Two raters independently identified LSTs, which were coded deductively using the adapted Systems Engineering Initiative for Patient Safety (SEIPS) framework and inductively for emergent themes. Harm potential was graded using Healthcare Failure Mode and Effect Analysis (HFMEA). LSTs were mapped onto a framework matrix for comparison within and across detection modalities. The primary outcome was differences in LSTs detected by each modality; number, nature (SEIPS) and proportion of critical threats (HFMEA score ≥8).
Seventy-two providers participated (mean 12.0±2.3/simulation). 2576 unique LSTs were identified. Video detected the most LSTs (n=1908), compared with observation (n=426) or debriefing (n=242, p<0.01). Although fewer overall, debriefing yielded the highest proportion of critical threats (54.9% (124/226), p<0.01) compared with observation (26.3% (109/414)) or video (23.6% (442/1875)). Debriefing often revealed task-specific and communication-related threats, whereas video excelled at physical workspace and infection control deficits. Inter-rater reliability for SEIPS coding was acceptable (κ≥0.7).
Although debriefing yielded fewer LSTs, it is valuable for surfacing critical LSTs (54.9% vs 23.6% video). We highlight the strengths of LST detection modalities and provide insights for institutions selecting strategies to mitigate harm and enhance system resilience.

PMID:
42493244
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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