Electronic Consultation Services Worldwide: Environmental Scan
Bibliographic record
Abstract
BACKGROUND: Excessive wait times for specialist care pose a serious concern for many patients, leading to duplication of tests, patient anxiety, and poorer health outcomes. In response to this issue, many health care systems have begun implementing technological innovations designed to improve the referral-consultation process. Among these services is electronic consultation (eConsult), which connects primary care providers and specialists through a secure platform to facilitate discussion of patients' care. OBJECTIVE: This study aims to examine different eConsult services available worldwide and compare the strategies, barriers, and successes of their implementation in different health care contexts. METHODS: We conducted an environmental scan comprising 3 stages as follows: literature review; gray literature search; and targeted, semistructured key informant interviews. We searched MEDLINE and EMBASE (literature review) and Google (gray literature search). Upon completing the search, we generated a list of potential interview candidates from among the stakeholders identified. Potential participants included researchers, physicians, and decision makers. The maximum variation sampling was used to ensure sufficient breadth of participant experience. In addition, we conducted semistructured interviews by telephone using an interview guide based on the RE-AIM framework. Analyses of transcripts were conducted using a thematic synthesis approach. RESULTS: A total of 53 services emerged from the published and gray literature. Respondents from 10 services participated in telephonic interviews. The following 4 major themes emerged from the analysis: service structure; benefits of eConsult; implementation challenges; and implementation enablers. CONCLUSIONS: eConsult services have emerged in a variety of countries and health system contexts worldwide. Despite differences in structure, platform, and delivery of their services, respondents described similar barriers and enablers to the implementation and growth and reported improved access and high levels of satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.057 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".