#237 Tele-emergency medicine: a systematic review of the impact of telemedicine on emergency medicine on quality of care, time to treatment, and accessibility versus traditional care
Bibliographic record
Abstract
Background Telemedicine has surged in popularity since the COVID-19 pandemic with widespread implementation across healthcare. In Emergency Medicine (EM), telemedicine—referred to as ‘tele-EM’—enhances patient flow and potentially reduces overcrowding by enabling timely remote consultations. This study systematically reviews the impact of tele-EM, focusing on quality of care, time to treatment, and accessibility compared to traditional in-person care. Methods Following PRISMA guidelines, a systematic review was conducted using four electronic databases: PubMed, Scopus, CENTRAL, and Embase. Search terms included ‘telemedicine’, ‘telehealth’, ‘tele-emergency’, ‘emergency departments’, ‘quality of care’, ‘implementation’, and ‘impact’. Studies were selected based on their relevance to telemedicine interventions in emergency department settings. Exclusion criteria included non-EM populations, comparisons of telemedicine with other interventions, and failure to meet primary endpoints. Cohort studies were assessed for bias using the Newcastle-Ottawa Scale. Results Of the 1,195 studies identified, 17 met the inclusion criteria. Tele-EM showed significant benefits, including a 20% reduction in transfer rates in rural emergency departments and a 30% decrease in paediatric interfacility transfers. Additionally, tele-EM shortened time to treatment, with a 20-minute reduction in time-to-ECG for myocardial infarction patients and a 50% reduction in time-to-head CT interpretation for neurological emergencies. Tele-EM also improved adherence to neonatal resuscitation guidelines by 25% and enhanced clinical decision-making by 20% for chest pain patients. Real-time video conferencing was the most utilized method of delivery. Conclusion Tele-EM demonstrates significant potential in EM, particularly in cardiology, stroke care, paediatrics, and psychiatry. It improves accessibility, reduces wait times, and enhances patient outcomes while potentially alleviating emergency department (ED) overcrowding through admission avoidance. However, limitations such as moderate-quality studies and regional biases affect the generalizability of these findings, potentially excluding certain patient populations. Future research should prioritize high-quality randomized trials to support broader implementation of tele-EM across diverse healthcare settings.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".