Enabling patient-centred policy for electronic consultations: A qualitative analysis of discussions from a stakeholder meeting
Bibliographic record
Abstract
INTRODUCTION: To support the expansion of a successful regional electronic consultation (eConsult) service, we hosted a full-day national eConsult Policy Think Tank, connecting health-services researchers, clinicians, patients and policymakers to discuss policy considerations related to eConsult. In this paper, we assess the discussion arising from the Think Tank to identify and understand the policy enablers and barriers to the national spread and scale of eConsult services across Canada. METHODS: We conducted a constant comparative thematic analysis of stakeholder discussions captured during the Think Tank held in Ottawa, Canada, on 5 December 2016. Forty-seven participants attended and debated the following topic areas: (a) delivery of services and standards; (b) payment considerations; and (c) equitable access. The meeting was recorded, and verbatim transcripts were analysed using qualitative approaches. RESULTS: We identified four themes affecting spread and scale of eConsult innovation from a policy perspective: (a) patient-centredness; (b) value; (c) regulation; and (d) considerations for spread and scale. Patient-centredness was viewed as a foundational principle upon which policy shifts should be guided. Active participation of patient partners transitioned the discussions and resulting recommendations from provider-centred to patient-centred thinking around the relevant policy issues, explicitly demonstrating the importance of patient involvement in healthcare policy decision making. DISCUSSION: eConsult was viewed as a high-value, disruptive innovation with great potential to transform access to specialists in Canada. A patient-centred approach to policy change (and not just healthcare delivery) was identified as a novel yet critical enabler to the scale and spread of eConsult across Canada.
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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 | 0.058 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".