Electronic consultation systems: worldwide prevalence and their impact on patient care—a systematic review
Bibliographic record
Abstract
BACKGROUND: Many health organizations are exploring the potential of electronic consultation (eConsult) services to address excessive wait times for specialist care. OBJECTIVE: To understand the effectiveness, population impact and costs associated with implementation of eConsult services. METHODS: We conducted a systematic review using a narrative synthesis approach. We searched Medline and Embase from inception to August 2014 (English/French). Included studies focused on communication between primary care providers and specialist physicians through an asynchronous, directed communication over a secure electronic medium. We assessed study quality with a modified version of the Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies. We synthesized the results using the Triple Aim framework. RESULTS: A total of 36 studies were included. Most were set in the USA and focused on single-specialty services (most commonly dermatology). Population health outcomes included patient populations, adoption/utilization and provider attitudes. Providers cited timely advice from specialists, good medical care, confirmation of diagnoses and educational benefits. No clinical outcomes were reported. Patient experience of care was generally positive, with quick specialist response times (4.6 hours to 3.9 days), avoided referrals (12-84%) and satisfaction ranging from 78% to 93%. System costs were reported in only seven studies using different outcome measures and settings, limiting comparability. CONCLUSION: Though eConsult systems are highly acceptable for patients and providers and deliver improved access to specialist advice, gaps remain regarding eConsult's impact on population health and system costs. To achieve optimized health system performance, eConsult services must include specialty services as determined by community needs and further explore cost-effectiveness.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".