Improving access to urologists through an electronic consultation service
Bibliographic record
Abstract
INTRODUCTION: Access to specialist services is limited by wait times and geographic availability. Champlain Building Access to Specialist Advice (BASE) has been implemented in our service region to facilitate access to specialists by primary care providers (PCPs). Through a secure web-based system, PCPs are able to send eConsults instead of requesting a formal in-office consultation. METHODS: Urology eConsults completed through the Champlain BASE service from March 2013 to January 2015 were analyzed. Each consult was characterized in regard to the type of question asked by the referring physician and the clinical content of the referral. Using the mandatory close-out surveys, we analyzed rates of referral avoidance, physician satisfaction, and overall impact on patient care. RESULTS: Of 190 eConsultations, 70% were completed in less than 10 minutes. The most common clinical questions related to the interpretation of imaging reports (16%) and tests to choose for investigating a condition (15%). The most common diagnoses were hematuria (13%) and renal mass (8%). In 35% of cases, referral to a urologist had originally been contemplated and was avoided. In 8% of cases, a PCP did not believe a consultation was initially needed, but a referral was ultimately initiated after the eConsultation. CONCLUSIONS: Our study shows that although certain clinical presentations still require a formal in-person urological consultation, eConsultations can potentially reduce unnecessary clinic visits while identifying patients who may benefit from early urological consultation. Through both these mechanisms, we may improve timely access to urologists.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".