Family physicians’ experiences with an electronic medical record-integrated family history collection strategy: a qualitative study
Bibliographic record
Abstract
BACKGROUND: A complete, up-to-date family history (FH) is imperative in primary care to identify those at increased risk of heritable conditions who may benefit from personalised screening and management. Complete FH is rarely documented in the electronic medical record (EMR). AIM: To understand family physicians' (FPs') experiences of an EMR-integrated FH strategy. DESIGN & SETTING: A descriptive qualitative study was conducted using one-to-one interviews to assess a FH strategy. Primary care teams, affiliated with University of Toronto Practice-Based Research Network in Ontario, Canada, were randomly selected. The participants were FPs from three sites that implemented the strategy. METHOD: Telephone interviews were undertaken with FPs. Thematic analysis was used for identifying, analysing, and reporting patterns in the data. An iterative process was used, with modification of interview and coding guides as new themes emerged. RESULTS: A total of 14 out of 15 FPs were interviewed. The following six major themes were identified: 1) FH informs hereditary risk and enables tailored patient care; 2) routine, intentional FH collection by patients and FPs is essential; 3) FH collection supports meaningful patient-FP discussions and quality care; 4) point-of-care tools enhance FP awareness and knowledge; 5) success is supported by patient engagement and EMR integration; and 6) tailored approaches are needed to improve acceptability. CONCLUSION: Physicians expressed the importance of routine FH collection and its implications for clinical management. Factors contributing to the strategy's success included being patient-initiated and medical record integration.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".