Use of Electronic Consultation System to Improve Access to Care in Pediatric Hematology/Oncology
Bibliographic record
Abstract
BACKGROUND: Electronic consultations (eConsult) allow for communication between primary care providers and specialists in an asynchronous manner. This study examined provider satisfaction, topics of interest, and efficiency of eConsult in pediatric hematology/oncology in Ottawa, Canada. METHODS: We conducted a cross-sectional assessment of all eConsult cases directed to pediatric hematology/oncology specialists using the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service from June 1, 2014 to May 31, 2016. RESULTS: There were 1064 eConsults to pediatrics during the study timeperiod and pediatric hematology/oncology consults accounted for 8% (85). During the same study timeperiod, 524 consults were seen in the pediatric hematology/oncology clinic. The majority of the eConsults were for hematology (90.5%) in contrast to oncology topics (9.5%). The most common topics were anemia, hemoglobinopathy, bleeding disorder, and thrombotic state. Primary care providers rated the eConsult service very highly, and their comments were very positive. The eConsult service resulted in deferral of 40% of consults originally contemplated to require a face-to-face specialist visit. CONCLUSIONS: This study showed successful implementation and use of the eConsult service for pediatric hematology/oncology and resulted in avoidance of a large number of face-to-face consultation. The common topics identified areas for continuing medical education.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".