Previsit Preparation for Shared Decision-Making in Lung Cancer Screening in Primary Care Using a Paper Decision Aid and an Automated Text Messaging Program: Quasi-Experimental Pilot Study
Bibliographic record
Abstract
Background: Patient-provider discussions and shared decision-making (SDM) are essential for tailoring lung cancer screening (LCS) decisions to individual patients. However, the implementation of SDM in primary care settings is challenging. Innovative approaches are needed to reach and prepare patients eligible for LCS for SDM in primary care settings and increase LCS uptake. Objective: We piloted previsit preparation comparing 2 strategies: a paper decision aid (DA; DA group) and an enhanced comparator strategy consisting of the paper DA plus an automated text message program (DA+TM group) designed to promote patient-provider LCS discussions. We explored feasibility and gathered preliminary data on its potential effects on LCS discussions, decision-making, and LCS uptake in primary care settings. Methods: In a sequential quasi-experimental pilot study, we recruited patients who were eligible for LCS in a single academic health care system. Prior to an upcoming visit, participants in both groups received a paper-based DA by mail. In the DA+TM group, participants also received a series of automated text messages to help them prepare for their LCS discussions. We monitored participant recruitment and retention, as well as patient engagement in DA and text messages. In exploratory analyses, we assessed patient-provider discussion of LCS, SDM, patient knowledge, decision conflict at baseline and in follow-up telephone surveys, and LCS completion measured by electronic health records. Results: We enrolled and included 48 participants (DA group=19 and DA+TM group=29) in the final analysis. Participants were predominantly White, with a median age of 61.0 (IQR, 57.0-65.0), and 58% (28/48) of them were female. Engagement was high in both groups. LCS knowledge significantly improved in the DA+TM group (4.5 baseline vs 6.0 follow-up; P=.003), while there was no change in the DA group (5.0 baseline vs 5.0 follow-up, P=.23). Median LCS knowledge change from baseline to follow-up was 0.5 (IQR -1.0 to 2.5) in the DA group and 1.5 (IQR 0-3.0) in the DA+TM group (P=.24). Decision conflict in both groups significantly decreased (DA group: 37.5 baseline vs 0 follow-up, P<.001; DA+TM group: 50.0 baseline vs 20.0 follow-up, P=.003). The median SDM process score (a measure of SDM) was 3.0 (IQR 1.5-4.0) in the DA group and 2.0 (IQR 1.0-3.0) in the DA+TM group (P=.11). The LCS completion rates were 5% (1/19) in the DA group and 31% (9/29) in the DA+TM group at 3 months (P=.07), and 26% (5/19) in the DA group and 34% (10/29) in the DA+TM group at 6 months (P=.75). Conclusions: Previsit preparation was feasible in primary care settings. An enhanced, text message-based strategy has the potential to reach and engage broader LCS-eligible populations and prepare patients for LCS discussions with their primary care providers, which may ultimately improve informed decision-making and LCS uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".