Understanding communication between emergency and consulting physicians: a qualitative study that describes and defines the essential elements of the emergency department consultation-referral process for the junior learner
Bibliographic record
Abstract
Objectives: To define the important elements of an emergency department (ED) consultation request and to develop a simple model of the process. Methods: From March to September 2010, 61 physicians (21 emergency medicine [EM], 20 general surgery [GS], 20 internal medicine [IM]; 31 residents, 30 attending staff) were questioned about how junior learners should be taught about ED consultation. Two investigators independently reviewed focus group and interview transcripts using grounded theory to generate an index of themes until saturation was reached. Disagreements were resolved by consensus, yielding an inventory of themes and subthemes. All transcripts were coded using this index of themes; 30% of transcripts were coded in duplicate to determine the agreement. Results: A total of 245 themes and subthemes were identified. The agreement between reviewers was 77%. Important themes in the process were as follows: initial preparation and review of investigations by EM physician (overall endorsement 87% [range 70-100% in different groups]); identification of involved parties (patient and involved physicians) (100%); hypothesis of patient's diagnosis (75% [range 62-83%]) or question for the consulting physician (70% [range 55-95%]); urgency (100%) and stability (74% [range 62-80%]); questions from the consultant (100%); discussion/communication (98% [range 95-100%]); and feedback (98% [range 95-100%]). These components were reorganized into a simple framework (PIQUED). Each clinical specialty significantly contributed to the model (χ2 = 7.9; p value = 0.019). Each group contributed uniquely to the final list of important elements (percent contributions: EM, 57%; GS, 41%; IM, 64%). Conclusions: We define important elements of an ED consultation with input from emergency and consulting physicians. We propose a model that organizes these elements into a simple framework (PIQUED) that may be valuable for junior learners.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".