Paramedic Determinations of Medical Necessity: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Reducing unnecessary ambulance transports may have operational and economic benefits for emergency medical services (EMS) agencies and receiving emergency departments. However, no consensus exists on the ability of paramedics to accurately and safely identify patients who do not require ambulance transport. Objective. This systematic review and meta-analysis evaluated studies reporting U.S. paramedics' ability to determine medical necessity of ambulance transport. METHODS: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library databases were searched using Cochrane Prehospital and Emergency Care Field search terms combined with the Medical Subject Headings (MeSH) terms "triage"; "utilization review"; "health services misuse"; "severity of illness index," and "trauma severity indices." Two reviewers independently evaluated each title to identify relevant studies; each abstract then underwent independent review to identify studies requiring full appraisal. Inclusion criteria were original research; emergency responses; determinations of medical necessity by U.S. paramedics; and a reference standard comparison. The primary outcome measure of interest was the negative predictive value (NPV) of paramedic determinations. For studies reporting sufficient data, agreement between paramedic and reference standard determinations was measured using kappa; sensitivity, specificity, and positive predictive value (PPV) were also calculated. RESULTS: From 9,752 identified titles, 214 abstracts were evaluated, with 61 studies selected for full review. Five studies met the inclusion criteria (interrater reliability, kappa = 0.75). Reference standards included physician opinion (n = 3), hospital admission (n = 1), and a composite of physician opinion and patient clinical circumstances (n = 1). The NPV ranged from 0.610 to 0.997. Results lacked homogeneity across studies; meta-analysis using a random-effects model produced an aggregate NPV of 0.912 (95% confidence interval: 0.707-0.978). Only two studies reported complete 2 x 2 data: kappa was 0.105 and 0.427; sensitivity was 0.992 and 0.841; specificity was 0.356 and 0.581; and PPV was 0.158 and 0.823. CONCLUSION: The results of the few studies evaluating U.S. paramedic determinations of medical necessity for ambulance transport vary considerably, and only two studies report complete data. The aggregate NPV of the paramedic determinations is 0.91, with a lower confidence limit of 0.71. These data do not support the practice of paramedics' determining whether patients require ambulance transport. These findings have implications for EMS systems, emergency departments, and third-party payers.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".