An Everyday Patient-Centered Discussion Model for Primary Care: Protocol for a Feasibility and Acceptability Study of the Zeroing in on Individualized, Patient-Centered Decisions (ZIP) Approach
Bibliographic record
Abstract
BACKGROUND: The Zeroing in on Individualized, Patient-Centered Decisions (ZIP) approach was developed to be a feasible, everyday shared decision-making (SDM) approach to personalizing decisions in primary care. Current SDM models, which require 5 to 10 minutes just to present initial information, are impractical in primary care, highlighting the need for more concise, patient-centered approaches. The ZIP approach preserves core aspects of SDM while offering a more pragmatic framework suited to real-world clinical constraints. This approach includes three key elements: (1) making a personalized recommendation, (2) qualitatively presenting trade-offs, and (3) supporting patient decisional autonomy. Previous work has found this approach to be acceptable. However, little is known about how feasible and acceptable the ZIP approach is during an actual primary care visit. OBJECTIVE: This paper aims to describe the protocol for a pilot test of the feasibility and acceptability to both patients and primary care physicians (PCPs) of using a paper-based deployment of the ZIP approach in a primary care clinic. METHODS: Two case studies were examined: lung cancer screening (LCS) and blood pressure (BP) treatment decisions. This study was a multicomponent pilot implementation study involving training PCPs in the ZIP approach and providing them with an encounter-based decision aid supporting the ZIP approach during clinic visits. Eligible patients were either candidates for an initial LCS conversation or a conversation about intensifying BP medication. The patient-PCP medical encounters were audio recorded. Following the appointment, the patient completed a short survey and underwent a semistructured interview. After PCPs completed 2 to 3 study appointments, they underwent a semistructured interview reflecting on their experience with the ZIP approach. Surveys and interviews sought to understand the overall ZIP components presented during the appointment (ie, feasibility) and the extent to which patients and physicians found the approach appropriate (ie, acceptability). Survey data were analyzed to provide an overview of patient and physician demographics. Interviews were transcribed and analyzed through qualitative coding and thematic analysis to identify high-level takeaways of the feasibility and acceptability of this approach. RESULTS: This study was funded in October 2022 by the Department of Veterans Affairs. We recruited 10 PCPs and 23 patients (n=4, 17% patients undergoing LCS and n=19, 83% patients involved in BP treatment decision-making). Data collection took place from October 2023 to April 2024. Data analysis concluded in December 2024. Planned paper submission will occur in June 2025. CONCLUSIONS: The results from this pilot implementation study will contribute to the ongoing efforts toward integrating a practical approach to SDM into primary care. This pilot will lay the groundwork for an effective and efficient larger-scale trial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64998.
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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 | 0.080 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".