Contribution of Online Referral Systems to Health Services: Systematic Review
Bibliographic record
Abstract
The online referral system (eReferral) is designed to improve waiting times and efficiency by standardizing information and online communication in the referral process. Online referral is the automation of the referral process in which appointments and other information regarding the outcome of a consultation are transferred between two or more healthcare providers. This study was designed to identify the implementation of online referrals at various levels of health care. This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Of the 10 articles included in this review, 6 studies were conducted in the USA, 2 studies in Canada, and one each in the UK, and Saudi Arabia. The studies used a variety of designs, including Quasi-experimental (n=5), longitudinal (n=2), qualitative (n=1), and Research and Development (n=2). This study provides a clear picture of the use of an online referral system, starting from the work process, effectiveness, barriers, and development possibilities for a wider reach. This system is very helpful in easing the workload of specialist doctors and for patients who are referred users get the benefit of shorter waiting times/queues, making it possible to reduce medical costs further.
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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 | 0.041 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".