Using eConsult to access specialist advice for persons living with dementia – A cross-sectional analysis
Bibliographic record
Abstract
Context: Dementia affects nearly half a million Canadians. Though dementia can be managed in primary care, the complexity of the condition and high prevalence of multimorbidity often require advice from a variety of specialists. Travel to see consultants can be disorienting and difficult for persons living with dementia (PLWD). eConsult is a secure web-based platform that may make communication with specialists more accessible for primary care providers (PCPs). Objective: To examine how eConsult is being used in the care of PLWD. Study Design and Analysis: Cross-sectional analysis. Setting or Dataset: eConsult cases closed in 2021 from the Champlain region in Eastern Ontario for PLWD living in the community and in long-term care (LTC). Population Studied: Our sample included 97 cases from PLWD in the community and 53 cases from LTC. Intervention/Instrument: We collected basic eConsult service utilization data, including the specialty group consulted and specialist response time, as well as the PCP’s responses to a close-out survey to describe their experience with eConsult. Outcome Measures: Our team of clinicians coded the questions and responses using validated taxonomies adapted to this study, using iterative discussions to achieve consensus. We provide descriptive statistics of the service utilization and taxonomy results. Results: PCPs’ questions were directly related to the patient’s dementia in 30% of community cases (n=29), compared to 15% in LTC (n=8). Specialists responded to all cases in a median of less than 1.2 days, and often considered the patient’s dementia in their responses (community: 46% [n=45], LTC: 38% [n=20]). PCPs indicated that an in-person referral was avoided in 39% of community cases (n=38) and 41% of LTC cases (n=22). Geriatrics was the most frequently consulted specialty from the community (18%, n=17), and dermatology from LTC (30%, n=15). Resources, services or assistance for caregivers of PLWD were discussed by PCPs/specialists in 32% of community cases (n=31) and 26% of LTC cases (n=14). Conclusions: PCPs are using eConsult to access different specialists for different care issues depending on whether the PLWD is living in the community or LTC. eConsult facilitates prompt response and supports PCPs in managing complex conditions, thereby reducing the potential wait times for, and travel burden on, this vulnerable population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".