Impact of and Satisfaction with a New eConsult Service: A Mixed Methods Study of Primary Care Providers
Bibliographic record
Abstract
OBJECTIVES: We have improved access to specialist care and decreased wait times in our region through the development and implementation of the Champlain BASE (Building Access to Specialists through eConsultation) service. This secure, web-based tool allows primary care providers (PCPs) quick access to specialist advice for their patients and often helps to avoid the need for a face-to-face referral. Our successful implementation of eConsult in our region provides a unique opportunity to examine PCPs' satisfaction and overall perspective on using the service. METHODS: Following the closure of each case, PCPs completed a short survey with multiple-choice and open-ended questions regarding the eConsult. All eConsults submitted between April 15, 2011, and December 31, 2013, were analyzed. We calculated satisfaction scores from the survey and conducted a constant-comparison thematic analysis on those cases where the PCP elected to leave a text response. RESULTS: We analyzed 2,052 eConsults completed during the study period. In 91% and 93% of eConsults, PCPs reported a high value for their patients and themselves, respectively. In 554 eConsults, PCPs elected to leave a written response. Three major themes emerged: PCP appreciation of the eConsult service, perceived benefits for the quality of patient care, and attitudes towards using a new health technology. High satisfaction was expressed with quick response times, helpfulness of responses, and reassurance reported. Most PCPs felt eConsult had a positive impact on patient care by also providing reassurance to patients, reducing burden of time and travel, and offering educational opportunities to PCPs applicable to future cases. CONCLUSION: PCPs showed a high level of satisfaction with eConsult's quick turnaround time and quality of specialist advice. Our results illustrate the advantages of using asynchronous virtual platforms to increase access to specialty care from a PCP perspective.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".