Application of a trigger tool in near real time to inform quality improvement activities: a prospective study in a general medicine ward
Bibliographic record
Abstract
BACKGROUND: Retrospective record review using trigger tools remains the most widely used method for measuring adverse events (AEs) to identify targets for improvement and measure temporal trends. However, medical records often contain limited information about factors contributing to AEs. We implemented an augmented trigger tool that supplemented record review with debriefing front-line staff to obtain details not included in the medical record. We hypothesised that this would foster the identification of factors contributing to AEs that could inform improvement initiatives. METHOD: A trained observer prospectively identified events in consecutive patients admitted to a general medical ward in a tertiary care academic medical centre (November 2010 to February 2011 inclusive), gathering information from record review and debriefing front-line staff in near real time. An interprofessional team reviewed events to identify preventable and potential AEs and characterised contributing factors using a previously published taxonomy. RESULTS: Among 141 patients, 14 (10%; 95% CI 5% to 15%) experienced at least one preventable AE; 32 patients (23%; 95% CI 16% to 30%) experienced at least one potential AE. The most common contributing factors included policy and procedural problems (eg, routine protocol violations, conflicting policies; 37%), communication and teamwork problems (34%), and medication process problems (23%). However, these broad categories each included distinct subcategories that seemed to require different interventions. For instance, the 32 identified communication and teamwork problems comprised 7 distinct subcategories (eg, ineffective intraprofessional handovers, poor interprofessional communication, lacking a shared patient care, paging problems). Thus, even the major categories of contributing factors consisted of subcategories that individually related to a much smaller subset of AEs. CONCLUSIONS: Prospective application of an augmented trigger tool identified a wide range of factors contributing to AEs. However, the majority of contributing factors accounted for a small number of AEs, and more general categories were too heterogeneous to inform specific interventions. Successfully using trigger tools to stimulate quality improvement activities may require development of a framework that better classifies events that share contributing factors amenable to the same intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".