Evaluation of BASE eConsult Manitoba: patient perspectives on the use of electronic consultation to improve access to specialty advice in Manitoba
Bibliographic record
Abstract
BACKGROUND: The burden of waiting to access specialist expertise may contribute to poorer health outcomes and causes distress for patients and providers. One solution to improve access to specialist care is to use innovative tools such as remote asynchronous electronic consultation (eConsult). Modeled after the Champlain BASE™ (Building Access to Specialist Advice) eConsult service, BASE™ eConsult Manitoba was launched in 2017 to help address long waits for patients to access specialist advice. OBJECTIVE: We aimed to evaluate patients' experiences after obtaining a BASE™ eConsult Manitoba service in their primary care setting. METHODS: Patients whose Primary Care Providers (PCPs) used BASE™ eConsult as part of their care were asked to participate and complete a telephone-based or online 29-question survey between January 2021 and October 2021. The survey questions were created in consultation with patient partners and based on questions asked in studies done in other jurisdictions. RESULTS: Of the 36 patients who chose to participate, 29 completed the entire survey (80%). Two-thirds (n = 22) agreed that eConsult has been helpful in their situation, and over 80% (n = 24) of participants agreed that eConsult was an acceptable way to access specialist care. During the visit when their PCP sent the eConsult, 7 patients were expecting to be referred to a specialist for a face-to-face consultation. Over half of all respondents (n = 15) reported that before the eConsult occurred, their PCP asked them what questions they wanted to be answered by the specialist. Almost all of these respondents' questions were fully answered by the eConsult. All of the respondents were satisfied with the experience of receiving an eConsult. CONCLUSION: Using eConsult is an acceptable way to improve access to specialist advice from patients' perspectives. Consideration should be given to expanding the use of eConsult services to improve access to specialist expertise for PCPs and their patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".