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Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake.

Tanner J Caverly^1,2, Renda S Wiener^3,4, Kyle Kumbier¹

¹Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan.

JAMA Network Open

|July 1, 2024

Summary

A new tool improved lung cancer screening (LCS) uptake by helping doctors identify high-benefit individuals. This personalized approach encourages screening more effectively, especially for those most likely to benefit from early detection.

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Area of Science:

Pulmonary Medicine
Health Services Research
Decision Science

Background:

Low-dose computed tomography lung cancer screening (LCS) uptake remains suboptimal, particularly among high-risk individuals with significant life expectancy.
Effective strategies are needed to improve LCS uptake and ensure equitable access for those who stand to benefit most.

Purpose of the Study:

To evaluate the impact of a prediction-augmented shared decision-making (SDM) tool on LCS uptake.
To assess if the tool enhances clinician ability to identify high-benefit candidates and tailor screening encouragement.

Main Methods:

A quality improvement interrupted time series study was conducted across six Veterans Affairs sites.
The study involved LCS-eligible individuals (aged 55-80, ≥30 pack-year smoking history) using clinical reminders to prompt SDM.
A decision support tool, enhanced by a prediction model, personalized SDM by tailoring screening encouragement based on predicted benefit.

Main Results:

In a cohort of 9904 individuals, 11% received screening. The tool implementation was associated with higher overall LCS uptake.
Benefit-based LCS uptake increased, with higher screening rates observed among individuals predicted to be at high benefit compared to intermediate benefit.
The mean predicted probability of screening was significantly higher for high-benefit individuals (24.8%) versus intermediate-benefit individuals (15.8%).

Conclusions:

Implementing a personalized LCS approach integrating SDM and a prediction-augmented decision support tool improves screening uptake.
This strategy is particularly effective for individuals most likely to benefit from lung cancer screening.
Findings are crucial given the persistent low rates of LCS uptake and highlight a pathway for optimization.