What determines diagnostic resource consumption in emergency medicine: patients, physicians or context?
Bibliographic record
Abstract
OBJECTIVES: A major cause for concern about increasing ED visits is that ED care is expensive. Recent research suggests that ED resource consumption is affected by patients' health status, varies between physicians and is context dependent. The aim of this study is to determine the relative proportion of characteristics of the patient, the physician and the context that contribute to ED resource consumption. METHODS: Data on patients, physicians and the context were obtained in a prospective observational cohort study of patients hospitalised to an internal medicine ward through the ED of the University Hospital Bern, Switzerland, between August and December 2015. Diagnostic resource consumption in the ED was modelled through a multilevel mixed effects linear regression. RESULTS: In total, 473 eligible patients seen by one of 38 physicians were included in the study. Diagnostic resource consumption heavily depends on physicians' ratings of case difficulty (p<0.001, z-standardised regression coefficient: 147.5, 95% CI 87.3 to 207.7) and-less surprising-on patients' acuity (p<0.001, 126.0, 95% CI 65.5 to 186.6). Neither the physician per se, nor their experience, the patients' chronic health status or the context seems to have a measurable impact (all p>0.05). CONCLUSIONS: Diagnostic resource consumption in the ED is heavily affected by physicians' situational confidence. Whether we should aim at altering physician confidence ultimately depends on its calibration with accuracy.
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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".