Paging the eCardiologist: insights into referral behaviour of primary care physicians from qualitative analysis of a cardiology eConsult service
Bibliographic record
Abstract
Objective The purpose of this study is to investigate determinants of primary care physician cardiology referrals by performing qualitative analysis of questions asked by primary care physicians in cardiology electronic consultation services (eConsults). Setting A health region in eastern Ontario, Canada, where primary care providers have had access to an eConsult service since 2010. Participants We included all consecutive cardiology eConsults initiated by registered primary care provider users of our eConsult service and who initiated one or more eConsult between July 2014 and January 2015. We excluded eConsults in which the primary care provider attached a document without asking a question. A convenience sample of 100 consecutive eConsults initiated by 61 primary care providers was analysed after excluding 14 eConsults. Primary and secondary outcome measures: Primary care provider eConsult questions are categorised into thematic categories based on the constant comparison method of qualitative analysis with external validation by content experts. Secondary outcomes include sample primary care provider eConsult questions to illustrate each theme and any emergent subthemes. Results Thematic saturation occurred after analysis of 30 eConsults. An additional 70 eConsults were coded with no new emergent themes. Themes include exceptions to clinical guidelines ( n=13), non-cardiac treatment in a cardiac patient ( n=13), specific investigation/management ( n=18), interpretation of diagnostic testing ( n=46), clinical concerns despite normal testing ( n=4) and screening for positive family history ( n=6). Subthemes include multiple comorbidities and mild abnormalities on cardiac tests. Conclusions We report categories of clinical questions that drive primary care provider cardiology eConsults. Multimorbidity leads to cardiology eConsults as primary care providers try to apply treatment guidelines in medically complex patients. Mild test abnormalities unrelated to clinical problems commonly lead to cardiology eConsult requests. Further research is needed to determine how guidelines can better account for multimorbidity, and how cardiologists can better communicate with primary care providers to put cardiac test results in clinical context.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".