Clinical Questions Asked by Long-Term Care Providers Through eConsult: A Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: eConsult allows primary care providers (PCPs) to access timely specialist advice and informs patient care. To understand the use of eConsult in long-term care (LTC) settings, we examined the clinical content and types of questions asked by LTC PCPs. METHODS: A descriptive, retrospective study of eConsults submitted through the Champlain BASE™ eConsult Service between January 1, 2017, and December 31, 2018, by LTC PCPs was conducted. Cases were classified using validated taxonomies. Descriptive statistics were generated for content and question type classifications, service utilization data, and close-out survey responses. RESULTS: 22 LTC PCPs submitted 113 eConsults. They sought advice about drug treatment (58%), diagnosis (44%), and management (38%) in a breadth of clinical areas, often skin-related (39%). Long-term care PCPs frequently asked more than one question type (42%). They received advice within 1 week (91%) and rated eConsult as very helpful and educational. Three case examples are presented. CONCLUSION: This study demonstrates the type of advice LTC PCPs are seeking through eConsult and its usefulness in this setting. Long-term care stakeholders are encouraged to consider implementing eConsult in other regions, as a means to improve access to timely specialist advice, support clinical decision-making, and improve residents' quality of life.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".