Lung cancer is the leading cause of cancer deaths in the United States. Recent clinical trials provide evidence that screening with low dose CT scans will decrease lung cancer and all-cause mortality among older heavy smokers. Clinical guidelines have been issued with the USPSTF recommending annual screening from age 55 to 80 for those with 30 pack years or more of smoking or who quit less than 15 years ago. Evidence clearly delineates both the benefits (mortality reduction) and harms (false positives, follow-up testing, risk of invasive testing, and risk of overdiagnosis) of lung cancer screening. Preliminary data from an HSR\&D pilot grant finds that some Veterans are highly reluctant to enter the care pathway associated with lung cancer screening due to its potential harms. Additional preliminary data using Best Worst Scaling in older smokers demonstrate groups of patients who place greater importance on harms than benefit when considering lung cancer screening. Preference assessment methods can help Veterans to weigh benefits and harms, consider the clinical pathway they are entering, anticipate future health states, and communicate these values to their health care providers. Although basic educational tools to inform lung cancer decision-making have been developed, there is a lack of validated preference assessment tools that can be integrated into the clinical setting. Building upon preference assessment methods developed and validated in an HSR\&D pilot grant (PI-Schapira) and using a trans-disciplinary approach, this team is positioned to advance the science and practice of decision support for lung cancer screening in the Veteran population. The objectives of this study are to 1) elicit patient and provider stakeholder input to inform the development of a lung cancer screening decision tool, 2) develop a web based Lung Cancer Screening Decision Tool (LCSDecTool) that incorporates patient and provider input, and 3) evaluate the impact of the LCSDecTool compared to usual care on the decision process, clinical outcomes, and quality of life. The study will be conducted in 3 phases. In phase 1, mixed methods will be used to assess usability of preference assessment methods and perceived usefulness of a web based lung cancer screening decision support tool among patient and provider stakeholders. In phase 2, an interactive web based decision support program will be developed that incorporates preference assessment methods. In phase 3, a pilot RCT will be conduced to evaluate the efficacy of the web based decision support program. Outcomes evaluated will include decision quality as indicated by knowledge, decisional conflict, and decision regret; screening behavior, clinical outcomes as indicated by anxiety, and quality of life. The study was conducted across three VA sites; West Haven-VA in Connecticut, Corporal Michael J. Crescenz VA in Philadelphia, Pennsylvania, and the Zablocki VA in Milwaukee, WI. Results of this study will provide tools that can be used to integrate lung cancer screening into clinical practice at VA Medical Centers in a patient centered approach. Lung cancer screening is fundamentally different from existing screening paradigms in several respects; eligibility is defined by a behavior (smoking), a high rate of false positive findings is expected, and the target population is older with higher comorbidity than the target population for cervical, breast, or colorectal screening. Given these unique aspects of lung cancer screening, there is a critical need to develop and test tools for preference assessment and informed decision making that are applicable for the VA setting. The current proposal provides a mechanism to accomplish these goals. The Principal Investigator is working closely with the US Department of Veterans Affairs National Center for Health Promotion and Disease Prevention to integrate the tools and paradigm developed to primary care in the VA Medical Care System. The work builds directly upon a recently completed HSR\&D pilot support in the area of lung cancer and shared decision making.