Variations in Medical Clearance Testing for Mental Health Emergency Department Visits
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There are recommendations against routine medical clearance testing for children evaluated in the emergency department (ED) for mental health concerns. Our objective was to determine variation, factors, and costs associated with medical clearance testing during ED encounters for mental health concerns. METHODS: We conducted a cross-sectional study of ED encounters among children aged 5 to 18 years who presented to 35 US children's hospitals for mental health concerns (2016-2023). We determined the prevalence of medical clearance testing defined as any electrocardiogram, complete blood count, serum chemistry, urine or serum drug screening, urine pregnancy, urinalysis, COVID-19, or thyroid function testing. We used generalized estimating equations to identify patient and hospital factors associated with medical clearance testing. Costs for medical clearance evaluation were estimated from billed charges. RESULTS: Among 604 869 ED encounters, 56.9% had at least 1 medical clearance test conducted. Medical clearance testing varied substantially by hospital (range: 33.5% to 85.3% of ED encounters). Compared with encounters resulting in ED discharge, admission to a psychiatric unit at the same facility (aOR, 30.73; 95% CI, 21.73-43.47) and transfer to a psychiatric facility (aOR, 5.64; 95% CI, 4.01-7.92) were associated with greater odds of medical clearance testing. Medical clearance testing cost a total of $25 187 999 per year across the included hospitals. CONCLUSIONS: More than half of ED encounters for children with mental health concerns involved medical clearance testing and such practices varied across hospitals. Medical clearance testing in ED encounters for mental health-related concerns resulted in substantial and potentially unnecessary costs.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".