A comparison of faxed referrals and eConsult questions for rheumatology referrals: a descriptive study
Bibliographic record
Abstract
BACKGROUND: In Canada, wait times for access to specialized rheumatology services have increased, leading to new strategies to improve timely care; electronic consultations (eConsults) enable providers to ask specialists a clinical question using a secure platform, often reducing the need for a face-to-face visit. In this study, we sought to compare the types of referrals received through fax versus eConsult and to determine whether faxed referrals could be addressed using eConsult. METHODS: We conducted a descriptive study of consecutive faxed referrals sent to a tertiary care centre between Feb. 1 and Mar. 6, 2017, and a convenience sample of eConsults directed to rheumatology between Feb. 1, 2015, and Sept. 30, 2016, through the Champlain BASE eConsult Service, an Ontario-based service. We reviewed all referrals and categorized them by clinical content and question type. A rheumatologist with experience completing eConsult referrals assessed faxed referrals for their suitability to be answered through eConsults. Descriptive statistics were generated. RESULTS: We analyzed 300 consecutive faxed referrals and 300 (of 470) eConsult referrals. Faxed questions more often pertained to rheumatoid arthritis (32/300 [10.7%] v. 17/300 [5.7%]), systemic lupus erythematosus (24/300 [8.0%] v. 10/300 [3.3%]), and polyarthritis (30/300 [10.0%] v. 18/300 [6.0%]). eConsults more often addressed abnormal serology without joint symptoms (27/300 [9.0%] v. 8/300 [2.7%]) and gout (15/300 [5.0%] v. 4/300 [1.3%]). Faxed referrals were more likely to have no specific question (116/300 [38.7%]), and eConsults were more likely to have more than 1 question posed (99/300 [33.0%]) and a drug-related question (67/300 [22.3%]). The rheumatologist identified potential benefit from eConsult in 216/300 (72.0%) faxed referrals and 55/59 (93.2%) declined faxed referrals. INTERPRETATION: Despite differences in diagnosis between eConsults and faxed referrals, most faxed referrals showed the potential to be addressed through eConsult. Using eConsult may allow primary care providers to obtain answers to questions without requesting a face-to-face specialist referral, or provide support for patients awaiting face-to-face consultation.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".