Understanding the impact of a multispecialty electronic consultation service on family physician referral rates to specialists: a randomized controlled trial using health administrative data
Bibliographic record
Abstract
BACKGROUND: Electronic consultation (eConsult) services are secure online applications facilitating provider-to-provider communication. They have been found to improve access to specialist care. However, little is known about eConsult's impact on family physicians' referral rates to specialty care. The objective of this study was to assess the impact of a multispecialty eConsult service on referral rates from primary care. METHODS: In this parallel-arm, randomized controlled trial, we recruited primary care providers across Ontario not previously enrolled with eConsult. We randomly assigned participants to intervention and control arms. Participants in the intervention arm received access to eConsult for a period of 1 year while those in the control arm received no access to eConsult. The main outcome was specialist referral rate, expressed as the total number of referrals to (1) specialties available through eConsult, and (2) all medical specialties, per 100 patients seen. Multivariable negative binomial regression analysis was used to evaluate the effect of the intervention before and after adjusting for provider characteristics, using health administrative data. RESULTS: One hundred and thirteen participants were randomized (56 to control and 57 to intervention). For the primary outcome (referrals to eConsult specialties), the results show a statistically significant reduction in the number of referrals in both arms (control-arm Rate Ratio (RR), 0.85, 95% CI 0.79 to 0.91; intervention-arm RR, 0.80, 95% CI 0.74 to 0.85; unadjusted and adjusted RR values almost identical), as compared to the baseline data collected during the 12-month period before randomization, with a non-statistically significant 6% greater reduction in referrals in the intervention arm, compared to the control arm (unadjusted RR 0.94, 95% CI 0.85 to 1.03; adjusted RR 0.93, 95% CI 0.85 to 1.03). CONCLUSIONS: Our randomized controlled trial of a multispecialty eConsult service demonstrated inconclusive results in terms of the impact of eConsult on physician referral rates. Findings are discussed in light of important limitations associated with conducting randomized controlled trials (RCTs) of complex interventions in the primary care context with intent to inform the design and analysis of future trials. TRIAL REGISTRATION: Clinicaltrials.gov, ID: NCT02053467 . Registered prospectively on 3 February 2014.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".