Identifying and managing latent safety threats though a zone-wide emergency department in-situ multidiscipline simulation program: A quality improvement project
Bibliographic record
Abstract
ABSTRACT Background Latent safety threats (LSTs) have been defined as system based issues that threaten patient safety that can materialize at any time and were previously unrecognized by healthcare providers, unit directors, or hospital administration. While LSTs such as system deficiencies, equipment failures, training, or conditions predisposing medical errors are frequently reported in the literature, a paucity was noted in the management and mitigation of these threats. The purpose of the translational simulation quality improvement project study was to utilize translational simulations to identify, manage, and mitigate future latent safety threats in our EDs. METHODS In 2017, 18 in-situ inter-professional simulation sessions were conducted at 11 EDs. Following each session, a survey assessment tool, created by the research team, was completed by participants to identify latent safety threats. Findings were shared with site clinical nurse educators and managers to help facilitate institutional follow up. For reporting, latent safety threats were categorized thematically and coded as either (i) resolved, (ii) ongoing, or (iii) not managed. Follow-up with sites was completed 1 year following the simulation. RESULTS A total n=158 LSTs were identified. The number and percentage by theme was: staff 48 (30.4%), equipment 41 (25.9%), medications 33 (20.9%), resuscitation resources 24 (15.2%), and information technology (IT) issues 12 (7.6%).Site follow-up identified that 149 LSTs were resolved and ten required ongoing work to manage. No occurrences of a LST ‘not managed’ were identified. CONCLUSIONS Translation simulation effectively identified latent safety threats and assisted interdisciplinary teams in the creation of a structured plan and systematic follow-up to enhance the health system and patient care. Through use of a threat mitigation strategy all identified threats were addressed while some require ongoing management.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".