Barriers and success factors to the implementation of a multi-site prospective adverse event surveillance system
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility of implementing a clinical observation method for adverse event detection. METHODS: Prospective adverse event surveillance was conducted from February to April 2012. We implemented this adverse event prospective surveillance system on the general internal medicine units of five sites within two teaching institutions and one community hospital. Following surveillance, we assembled provider and decision-maker focus groups to understand the barriers and success factors related to our implementation. We used a structured interview guide with facilitated discussion. RESULTS: We performed six focus group interviews in June and July 2012. In total, 31 individual participated including senior executives (15), managers (7) and care providers (9). We identified the following success factors: the overall design of the system including the clinical observer and clinical reviewer functions; the credibility of the data and the opportunity to make changes to practice in 'real-time'. We identified the following opportunities for improvement: the need for clear guidelines on the type of information to collect for each event trigger, and for an action plan to ensure accountability and follow through on improvement efforts once the adverse event data have been analyzed. CONCLUSIONS: This work supports a conclusion that prospective surveillance is viewed as beneficial and acceptable. For this reason, healthcare organizations should consider adopting prospective adverse event surveillance to support their local quality improvement methods.
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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 | 0.050 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".