Assessing the Utilization of Electronic Consultations in Genetics: Seven-Year Retrospective Study
Bibliographic record
Abstract
Background: Patient and health care provider access to genetic subspecialists is challenging owing to limited number of genetics experts across the United States. The University of California San Francisco (UCSF) Genetics electronic consultation (e-Consult) service was implemented along with the usual referral pathway to improve access to timely genetic expertise through robust asynchronous provider-to-provider communication. objectives: This study examined the impact of the UCSF Genetics e-Consult service on patient access to genetics expertise. Methods: A retrospective chart review of 622 e-Consult requests was conducted. Data pertinent to e-Consult completion rates, provider response times, consultation content, and adherence to geneticist recommendations were abstracted. Results: From October 2016 to March 2024, the UCSF Genetics e-Consult service received a total of 622 consultation orders, with yearly volumes increasing from 34 in 2017 to 144 in 2023. A total of 360/622 (57.8%) consultations were completed, of which 197/360 (54.6%) were resolved without requiring a specialty care visit. Of the 262/622 (42.1%) e-Consult orders declined by the geneticist reviewer, 184/262 (70.2%) were scheduled for a synchronous genetics visit due to case complexity precluding an appropriate e-Consult response and 29.8% (78/262) were recommended to be referred to a different and more appropriate specialty. Geneticists responded to 83.9% (522/622) of e-Consults within 3 days, with most spending between 5 and 20 minutes on their e-Consult response. Nearly half of the genetics e-Consult requests (69/144; 47.9%) came from primary care providers and pediatricians. Among the 144 e-Consult requests in 2023, 50.6% (73/144) were about diagnostic queries, 17% (25/144) were on symptom management, and 11% (16/144) were about test interpretation. Provider adherence to geneticists' recommendations was observed in 84% (116/144) of cases. Conclusions: The UCSF Genetics e-Consult service has demonstrated efficiency in providing timely genetic consultations, with a high rate of provider adherence to recommendations. These findings support the potential of e-Consult frameworks as a viable strategy for enhancing access to genetic health care services.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".