The Feasibility of Using Electronic Consultation in Long-Term Care Homes
Bibliographic record
Abstract
Patients in long-term care (LTC) homes face barriers to accessing specialist advice. Electronic consultation (eConsult) has the potential to improve access for these patients. We used a multi-method approach to evaluate adoption of the Champlain BASE eConsult service in LTC homes across Eastern Ontario, Canada. We conducted a cross-sectional study of all eConsults submitted by primary care providers (PCPs) working at LTC homes between January 1, 2018 and December 31, 2018. Service use data were collected and descriptive statistics were calculated. We completed a thematic analysis of 4 focus groups with PCPs, senior leadership, and a nurse champion working in LTC homes where eConsult is used. Sixty-four cases were submitted to 23 specialty and subspecialty groups by LTC PCPs, most frequently dermatology (19%), geriatric medicine (11%), and infectious disease (9%). Specialists responded in a median of 0.6 days, and 70% of cases were resolved without the resident needing a face-to-face specialist visit. In 60% of cases, PCPs received advice for a new or additional course of action. Participants described complexities in the LTC context, the value of eConsult in LTC, and considerations for implementation. PCPs with experience using the service described increased access to specialist advice, ease of use, and benefits to themselves, residents, and families. eConsult is feasible in LTC and should continue to be used in this region and beyond to improve equity of access to specialist advice. Resolving the identified limitations in LTC, which hinder access to specialists and adoption of eConsult and similar innovations, should be of high priority to researchers and policy makers.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".