Los Angeles Safety-Net Program eConsult System Was Rapidly Adopted And Decreased Wait Times To See Specialists
Bibliographic record
Abstract
Lack of timely access to specialty care is a significant problem among disadvantaged populations, such as those served by the Los Angeles County Department of Health Services. In 2012 the department implemented an electronic system for the provision of specialty care called the eConsult system, in which all requests from primary care providers for specialty assistance were reviewed by specialists. In many cases, the specialist can address the primary care provider's question via an electronic dialogue, thereby eliminating the need for the patient to see a specialist in person. We observed rapid growth in the use of eConsult: By 2015 the system was in use by over 3,000 primary care providers, and 12,082 consultations were taking place per month, compared to 86 in the third quarter of 2012. The median time to an electronic response from a specialist was one day, and 25 percent of eConsults were resolved without a specialist visit. Three to four years after implementation, the median time to a specialist appointment decreased significantly, while the volume of visits remained stable. eConsult systems are a promising and sustainable intervention that could improve access to specialist care for underserved patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".