Piloting electronic screening forms in primary care: findings from a mixed methods study to identify patients eligible for low dose CT lung cancer screening
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that screening with low dose computed tomography (LDCT) scans significantly reduces mortality from lung cancer. However, optimal methods to identify potentially eligible patients in primary care are not known. Using brief electronic screening forms administered prior to a primary care visit is a strategy to identify high risk, asymptomatic patients eligible for LDCT screening. The objective of this study was to compare the acceptability and feasibility of using brief electronic versus paper screening forms to identify eligible patients at high risk of developing lung cancer in primary care. METHODS: A mixed method pilot comparative study was conducted in primary care. Practices were allocated to an electronic form (e-form) group or a paper-based form (p-form) group. Allocation was randomly assigned for the first practice then by alternation. Patients in the e-form practices completed forms at home via the web or in the waiting room on a tablet. Patients in p-form practices completed forms in waiting rooms. Interviews were conducted with patients, administrators, and primary care physicians (PCPs) about their experiences. RESULTS: Six of 30 (20%) eligible practices agreed to participate. Over the 16-week study period, a total of 831 of an expected 1442 patients (58%) aged 55-74 years were enrolled; 573/690 (83%) patients in the e-form group and 258/752 (34%) in the p-form group. Of the 573 participants in the e-form group, 335 (58%) completed forms via the web; 238 (29%) did so via tablet. Twenty-four interviews were conducted with 15 patients, 5 administrative staff and 4 PCPs. Patients were willing to discuss lung cancer screening eligibility with their PCP. Staff members expressed low administrative burden except for an extra step to link appointment information to patient demographics to identify eligible patients. PCPs indicated that forms were reminders to discuss smoking cessation. PCPs in the e-form group reported that patients asked questions about screening. CONCLUSION: There was fairly low uptake by primary care practices. For e-forms to be feasible in practice workflow, electronic medical record software needs to link appointment information with patient eligibility requirements. The use of brief pre-consultation electronic screening forms for LDCT eligibility encouraged PCPs to discuss smoking cessation with patients.
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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 | 0.055 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".