Insights into Specialists' Participation and Self-Reported Billing Times in a Multispecialty eConsult Service: Correlating Response Length with Outcomes and Satisfaction
Bibliographic record
Abstract
Background: Electronic consultation (eConsult) services have been shown to reduce the need for face-to-face consultations. The largest expense is remunerating the specialist. Introduction: The Champlain BASE™ eConsult service remunerates specialists based on their self-reported billing time. It is important for funders of eConsult systems to understand and plan for specialist remuneration. This study examined specialists' time commitments pertaining to eConsult, identified factors that affect their self-reported billing time, and determined if self-reported billing time is associated with changes in primary care provider (PCP) behavior. Methods: A cross-sectional study of eConsults was completed between January 1 and December 31, 2017. Data were collected automatically by the service and through mandatory closeout surveys. Logistic regressions identified associations between specialists' self-reported billing time and volume of cases completed, PCP characteristics, specialty group, impact on PCP behaviors, and PCP satisfaction. Results: A total of 11,985 cases met inclusion criteria. Self-reported billing time was <5 min in 18.3% of cases, 5–10 min in 35.6%, 10–15 min in 27.3%, 15–20 min in 11.3%, and >20 min in 7.5%. Self-reported billing time demonstrated significant variation between specialty groups. Cases with higher self-reported billing time were more likely to lead to new/additional course of action for PCPs (p ≤ 0.0001), resulted in fewer referrals (p ≤ 0.0001), and received higher rankings for helpfulness and educational value (p ≤ 0.0001). Discussion: A thorough understanding of when and how specialists respond to eConsult cases is critical to ensuring the service's long-term sustainability. Examining these factors and their impact on PCP behaviors helps us to better understand the service's overall value and serve to inform the structure of its remuneration process. Conclusions: Specialists' self-reported billing time varies by specialty group and is associated with changes in PCP behavior and satisfaction. Further research is needed to identify what factors influence self-reported billing time and how eConsult can be best incorporated into clinicians' workflows.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".