Impact of the Connected Medicine collaborative in improving access to specialist care: a cross-sectional analysis
Bibliographic record
Abstract
<h3>Background:</h3> In 2017, the Canadian Foundation for Healthcare Improvement launched the Connected Medicine collaborative to support the implementation, spread and adaptation of 2 innovative remote consult solutions — the Champlain Building Access to Specialists through eConsultation (BASE) eConsult service and the Rapid Access to Consultative Expertise (RACE) service — across Canada. We evaluated the impact of the programs implemented through the collaborative. <h3>Methods:</h3> We conducted a cross-sectional analysis of data from provincial teams that participated in the Connected Medicine collaborative, which took place between June 2017 and December 2018 in 7 provinces across Canada (British Columbia, Alberta, Saskatchewan, Manitoba, Quebec, New Brunswick, Newfoundland and Labrador). Data included utilization data collected automatically by the BASE and RACE services and, where available, responses to surveys completed by primary care providers at the end of each case. We assessed programs on the following outcomes: usage (i.e., number of cases completed, average specialist response time), number of specialties available, impact on primary care provider’s decision to refer and impact on emergency department visits. We performed descriptive analyses. <h3>Results:</h3> Ten provincial teams participated in the collaborative and implemented or adapted either the RACE service (4 teams), the BASE service (5 teams) or a combination of the 2 services (1 team). Average monthly case volume per team ranged from 14.7 to 424.5. All programs offered multispecialty access, with specialists from 5 to 37 specialty groups available. Specialists responded to eConsults within 7 days in 80% (<i>n</i> = 294/368) to 93% (<i>n</i> = 164/176) of cases. Six programs provided survey data on avoidance of referrals, which occurred in 48% (<i>n</i> = 667/1389) to 76% (<i>n</i> = 302/398) of cases. Two programs reported on the avoidance of potential emergency department visits, noting that originally considered referrals were avoided in 28% (<i>n</i> = 138/492) and 74% (<i>n</i> = 127/171) of cases, respectively. <h3>Interpretation:</h3> The 2 innovative virtual care solutions implemented through the Connected Medicine collaborative received widespread usage and affected primary care providers’ decisions to refer patients to specialists. The impact of these models of care in multiple settings shows that they are an effective means to move beyond the pilot stage and achieve spread and scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".