An Innovative Strategy for Collecting Family Health History: An Effectiveness-Implementation Trial in Primary Care Clinics
Bibliographic record
Abstract
PURPOSE We aimed to evaluate an innovative strategy to collect family history (FH) and explore patients’ views of this strategy. METHODS We conducted a matched-pair effectiveness-implementation trial in family practices affiliated with the University of Toronto Practice-Based Research Network (UTOPIAN). The intervention group included family physicians (FPs) from randomly selected practices using electronic health records (EHRs) and an e-mailing platform, and randomly selected patients aged 30-69 years (4/FP/week) seen in clinic over a 6-month period. The matched control group included FPs (1:1) and patients (up to 5:1) from the UTOPIAN database. The intervention included patient and FP education, an e-mailed patient invitation to complete an FH questionnaire, automatic FH EHR upload, FP notification of completed FH questionnaire, and links to clinical support tools. Intervention patients were e-mailed a postvisit follow-up questionnaire. The assessed outcome was new documentation of FH in the EHR using mixed effects logistic regression and descriptive statistics for patient feedback. RESULTS Fifteen FPs and 576 patients were recruited from 3 multidisciplinary team practices to the intervention group, matched to 15 FPs and 2,203 patients in the control group. Within 30 days of visit, a new FH was documented in the EHR for 93/576 (16.1%) of intervention patients compared with 5/2,203 (0.2%) control patients (adjusted OR = 94.2; 95% CI, 36.8-240.8). New cancer FH documentation was greater in the intervention group compared with the control group (7.8% vs 0.1%; P < .01). Of patients who reported discussing FH (n = 296), 24.5% reported screening test recommended, 7.5% referral to a nongenetics specialist, and 2.4% referral to a genetics specialist. Most patients (60.5%) found this FH strategy helpful. CONCLUSIONS This study showed improved collection/documentation of FH. Contributors to success of the intervention included being patient completed and seamless EHR integration with a reminder. This FH strategy needs tailoring to different contexts.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".