E-referral Solutions: Successful Experiences, Key Features and Challenges- a Systematic Review
Bibliographic record
Abstract
BACKGROUND: around the world health systems constantly face increasing pressures which arise from many factors, such as an ageing population, patients and providers demands for equipment's and services. In order to respond these challenges and reduction of health system's transactional costs, referral solutions are considered as a key factor. This study was carried out to identify referral solutions that have had successes. METHODS: relevant studies identified using keywords of referrals, consultation, referral system, referral model, referral project, electronic referral, electronic booking, health system, healthcare, health service and medical care. These searches were conducted using PubMed, ProQuest, Google Scholar, Scopus, Emerald, Web of Knowledge, Springer, Science direct, Mosby's index, SID, Medlib and Iran Doc data bases. 4306 initial articles were obtained and refined step by step. Finally, 27 articles met the inclusion criteria. RESULTS: we identified seventeen e-referral systems developed in UK, Norway, Finland, Netherlands, Denmark, Scotland, New Zealand, Canada, Australia, and U.S. Implemented solutions had variant degrees of successes such as improved access to specialist care, reduced wait times, timeliness and quality of referral communication, accurate health information transfer and integration of health centers and services. CONCLUSION: each one of referral solutions has both positive and changeable aspects that should be addressed according to sociotechnical conditions. These solutions are mainly formed in a small and localized manner.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".