Cognitive Debiasing Strategies for the Emergency Department
Bibliographic record
Abstract
The emergency department (ED) is a high-risk environment where diagnostic error is not uncommon. Most errors (70%) are due to faulty reasoning.1 Decision making occurs through two primary pathways: 1) Pattern recognition is fast, intuitive, and heuristically driven and occurs largely unconsciously; 2) analytic thinking is slow and deliberate and takes place under conscious control. When functioning optimally, expert clinicians toggle back and forth between these two systems depending on the complexity of the case and the demands of the environment. Systematic errors (known as biases) can interfere with reasoning via either pathway, but predominately affect the abbreviated decision making associated with pattern recognition. Thus, a critical feature of cognitive bias mitigation involves deliberate “switching” from intuitive to analytical processing and the deliberate use of debiasing strategies.2, 3 Prominent cognitive psychologist Daniel Kahneman (Thinking Fast and Thinking Slow) holds the largely pessimistic view that physicians are incapable of employing bias mitigation strategies to overcome their flawed intuition.4 Recent research, however, offers strong converging evidence that doctors do have the means to overcome bias through education.5 This Med Ed download focuses on some of the most common biases amongst ED providers so that you can more effectively recognize and mitigate bias in yourself and in your learners. The aim is to help teachers and learners develop a common language around bias to make you STOP, THINK about the thinking that underlies these errors, and ACT by proposing debiasing strategies to address them. See the patient yourself and form your own impressions before reading the triage summary or nurses' notes or hearing a learner's case presentation. Two heads (or many) are better than one. You will invariably each pick up important data that the other person did not. Collectively this information forms a more complete picture of the case. “Group think” should be used for difficult cases. Ask a colleague for an independent assessment or a second opinion. Do not “frame” the patient to a colleague; give objective data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".