Human Factors Analysis of Latent Safety Threats in a Pediatric Critical Care Unit*
Bibliographic record
Abstract
OBJECTIVES: To identify unique latent safety threats spanning routine pediatric critical care activities and categorize them according to their underlying work system factors (i.e., "environment, organization, person, task, tools/technology") and associated clinician behavior (i.e., "legal": expected compliance with or "illegal-normal": deviation from and "illegal-illegal": disregard for standard policies and protocols). DESIGN: A prospective observational study with contextual inquiry of clinical activities over a 5-month period. SETTING: Two PICUs (i.e., medical-surgical ICU and cardiac ICU) in an urban free-standing quaternary children's hospital. SUBJECTS: Attending physicians and trainees, nurse practitioners, registered nurses, respiratory therapists, dieticians, pharmacists, and patient services assistants were observed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Conducted 188 hours of observations to prospectively identify unique latent safety threats. Qualitative observational notes were analyzed by human factors experts using a modified framework analysis methodology to summarize latent safety threats and categorize them based on associated clinical activity, predominant work system factor, and clinician behavior. Two hundred twenty-six unique latent safety threats were observed. The latent safety threats were categorized into 13 clinical activities and attributed to work system factors as follows: "organization" (n = 83; 37%), "task" (n = 52; 23%), "tools/technology" (n = 40; 18%), "person" (n = 32; 14%), and "environment" (n = 19; 8%). Twenty-three percent of latent safety threats were identified when staff complied with policies and protocols (i.e., "legal" behavior) and 77% when staff deviated from policies and protocols (i.e., "illegal-normal" behavior). There was no "illegal-illegal" behavior observed. CONCLUSIONS: Latent safety threats span various pediatric critical care activities and are attributable to many underlying work system factors. Latent safety threats are present both when staff comply with and deviate from policies and protocols, suggesting that simply reinforcing compliance with existing policies and protocols, the common default intervention imposed by healthcare organizations, will be insufficient to mitigate safety threats. Rather, interventions must be designed to address the underlying work system threats. This human factors informed framework analysis of observational data is a useful approach to identifying and understanding latent safety threats and can be used in other clinical work systems.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".