What do primary care providers want to know when caring for patients living with frailty? An analysis of eConsult communications between primary care providers and specialists
Bibliographic record
Abstract
BACKGROUND: Frailty is a complex condition that primary care providers (PCPs) are managing in increasing numbers, yet there is no clear guidance or training for frailty care. OBJECTIVES: The present study examined eConsult questions PCPs asked specialists about patients with frailty, the specialists' responses, and the impact of eConsult on the care of these patients. DESIGN: Cross-sectional observational study. SETTING: ChamplainBASE™ eConsult located in Eastern Ontario, Canada. PARTICIPANTS: Sixty one eConsult cases closed by PCPs in 2019 that use the terms "frail" or "frailty" to describe patients 65 years of age or older. MEASUREMENTS: The Taxonomy of Generic Clinical Questions (TGCQ) was used to classify PCP questions and the International Classification for Primary Care 3 (ICPC-3) was used to classify the clinical content of each eConsult. The impact of eConsult on patient care was measured by PCP responses to a mandatory survey. RESULTS: PCPs most frequently directed their questions to cardiology (n = 7; 11%), gastroenterology (n = 7; 11%), and endocrinology (n = 6; 10%). Specialist answers most often pertained to medications (n = 63, 46%), recommendations for clinical investigation (n = 24, 17%), and diagnoses (n = 22, 16%). Specialist responses resulted in PCPs avoiding referral in 57% (n = 35) of cases whereas referrals were still required in 15% (n = 9) of cases. Specialists responded to eConsults in a median 1.11 days (IQR = 0.3-4.7), and 95% (n = 58) of cases received a response within 7 days. Specialists recorded a median of 15 min to respond (IQR = 10-20), with a median cost of $50.00 CAD (IQR = 33.33 - 66.66) per eConsult. CONCLUSIONS: Through the analysis of questions and responses submitted to eConsult, this study provides novel information on PCP knowledge gaps and approaches to care for patients living with frailty. Furthermore, these analyses provide evidence that eConsult is a feasible and valuable tool for improving care for patients with frailty in primary care settings.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".