Cost-Effectiveness of Virtual Emergency Care Models: A Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual care technologies have rapidly expanded in emergency medicine, particularly following the COVID-19 pandemic. However, comprehensive economic evaluations of their cost-effectiveness remain fragmented across different clinical applications and health care settings, creating uncertainty for policymakers and health care administrators considering implementation. OBJECTIVE This study aimed to systematically review and synthesize evidence on the cost-effectiveness of virtual emergency care models compared to traditional in-person emergency care across diverse clinical conditions, populations, and health care settings. METHODS We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching 8 electronic databases (PubMed, Embase, Scopus, Web of Science, CINAHL, Cochrane Library, MEDLINE, and PsycINFO) from inception to February 2025. We included full economic evaluations comparing virtual emergency care interventions with usual care. Two reviewers independently screened studies, extracted data, and assessed quality using the Drummond checklist and Consensus Health Economic Criteria (CHEC) list. Evidence certainty was evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Given heterogeneity in interventions and methods, we conducted a narrative synthesis by virtual care modality and clinical application. RESULTS From 5817 identified references, 13 studies met inclusion criteria, representing diverse virtual care modalities across 6 countries (United States, Australia, Italy, Canada, Haiti, and Belgium). All included studies reported favorable economic outcomes for virtual emergency care. Video consultation was the most common modality (11/13 studies), achieving 31% to 73% reduction in patient transfers and cost savings of US $73 (AUD $105) to US $5118 per encounter. A total of 6 (46%) studies found virtual care to be dominant (both less costly and more effective). Incremental cost-effectiveness ratios ranged from US $1273 (€990) to US $108,363 per quality-adjusted life year, with most below accepted willingness-to-pay thresholds. Transfer avoidance was the primary economic driver, particularly in rural settings. Quality assessment revealed high methodological rigor (mean Drummond score 92.3%, SD 6.0%; mean CHEC score 95%, SD 4.2%). Using GRADE, evidence certainty was rated high for cost-effectiveness, moderate for transfer reduction and quality of life improvements, and low for emergency department length of stay and mortality benefits. CONCLUSIONS Virtual emergency care demonstrates strong and consistent cost-effectiveness across diverse clinical conditions, populations, and health care settings. The evidence particularly supports implementation for stroke care, pediatric emergencies, and rural/remote populations where transfer avoidance drives substantial economic benefits. All evaluated modalities achieved favorable economic outcomes, suggesting technology should match context rather than maximize sophistication. These findings provide robust economic justification for expanding virtual emergency care access and removing regulatory barriers. As health care systems face mounting pressures from aging populations, workforce shortages, and budget constraints, virtual emergency care offers a proven strategy for improving access and quality while reducing costs. CLINICALTRIAL PROSPERO CRD42025648218; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025648218
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".