Improving the Referral Process, Timeliness, Effectiveness, and Equity of Access to Specialist Medical Services Through Electronic Consultation: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Access to specialty care remains a major challenge in the Canadian health care system. Electronic consultation (eConsult) services allow primary care providers to seek specialist advice often without needing the patient to go for a face-to-face consultation. It improves overall access to specialists and the referral process using an electronic care consultation service in urban and rural primary care clinics. This study describes the preliminary results of a pilot study with an eConsult service across 3 regions in the province of Quebec, Canada. OBJECTIVE: The main objective of this study was to provide a 1-year snapshot of the implementation of the eConsult Quebec Service in rural and urban primary care clinics to improve access to care and the specialty referral process for primary care providers (PCPs). METHODS: We established an eConsult service that covers urban and rural communities in 3 regions of Quebec. We conducted a quantitative analysis of all eConsult cases submitted from July 4, 2017, to December 8, 2018. RESULTS: For over a year, 1016 eConsults have been generated during the course of this study. A total of 97 PCPs submitted requests to 22 specialty groups and were answered by 40 different specialists. The most popular specialty was internal medicine (224/1016, 22%). Overall, 63% (640/1016) of completed cases did not require a face-to-face visit. PCPs rated the service as being of high or very high value for themselves in 98% (996/1016) of cases. CONCLUSIONS: The preliminary data highlight the success of the implementation of the eConsult Quebec Service across 6 primary care clinics. The eConsult platform proves to be effective, efficient, and well received by both patients and physicians. If used more widely, eConsult could help reducing wait times significantly. Recently, the Ministry of Health and Social Services of Quebec has identified developing a strategic plan to scale eConsults throughout other regions of the province as a top priority.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".