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

• Published in: JAMA network open 2024-07, Vol.7 (7), p.e2419624
• Main Authors: Caverly, Tanner J, Wiener, Renda S, Kumbier, Kyle, Lowery, Julie, Fagerlin, Angela
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Decision making; Decision Making, Shared; Early Detection of Cancer - methods; Early Detection of Cancer - statistics & numerical data; Female; Humans; Interrupted Time Series Analysis; Lung cancer; Lung Neoplasms - diagnosis; Male; Medical screening; Middle Aged; Quality Improvement; Tomography, X-Ray Computed - methods; Tomography, X-Ray Computed - statistics & numerical data; United States
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2024.19624

Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit