Cognitive bias: how understanding its impact on antibiotic prescribing decisions can help advance antimicrobial stewardship
Bibliographic record
Abstract
The way clinicians think about decision-making is evolving. Human decision-making shifts between two modes of thinking, either fast/intuitive (Type 1) or slow/deliberate (Type 2). In the healthcare setting where thousands of decisions are made daily, Type 1 thinking can reduce cognitive load and help ensure decision making is efficient and timely, but it can come at the expense of accuracy, leading to systematic errors, also called cognitive biases. This review provides an introduction to cognitive bias and provides explanation through patient vignettes of how cognitive biases contribute to suboptimal antibiotic prescribing. We describe common cognitive biases in antibiotic prescribing both from the clinician and the patient perspective, including hyperbolic discounting (the tendency to favour small immediate benefits over larger more distant benefits) and commission bias (the tendency towards action over inaction). Management of cognitive bias includes encouraging more mindful decision making (e.g., time-outs, checklists), improving awareness of one's own biases (i.e., meta-cognition), and designing an environment that facilitates safe and accurate decision making (e.g., decision support tools, nudges). A basic understanding of cognitive biases can help explain why certain stewardship interventions are more effective than others and may inspire more creative strategies to ensure antibiotics are used more safely and more effectively in our patients.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".