Background: Conventional recommendations in national prevention guidelines often fail to address differences in outcome risk and life expectancy across the target population. By ignoring such differences, conventional recommendations can inadvertently lead to substantial underuse (by excluding Veterans with potentially high net benefit who do not meet a conventional cutoff) and overuse (by including Veterans for whom net benefit would be limited). Further, minoritized patients who could most benefit from prevention are often disproportionately excluded by conventional recommendations, exacerbating racial disparities. The long- term goal of the proposed research is to support optimal preventive care decisions for all Veterans. The overall objective of this proposal is to study an alternative guideline approach (“tailored” recommendations based on multivariable prediction) that can better support individualized prevention efforts. A second goal is to study the extent to which optimizing decisions for individuals can broaden the public health impact of preventive care programs within VA. We use lung cancer screening, statin use, and blood pressure treatment as case studies as they are ideal models for studying the key issues. Significance: This work is significant because current knowledge gaps in guideline development, which we address in this proposal, inhibit progress toward more nuanced preventive care recommendations that often better identify high-benefit patients and enable more Veteran-centered care. Innovation and Impact: The proposed research is innovative because it will advance a different paradigm for developing preventive care guidelines both within and outside VA, which will open new horizons for optimizing the delivery of cancer screening, cardiovascular prevention, and other prevention. The rationale underlying the proposed work is that its successful completion would enable guideline-level assessment of when tailored recommendations would be highly advantageous, to promote more effective and personalized care and reduce racial disparities. Specific Aims: Aim 1: Estimate “individualized” net benefit for lung cancer screening, statin use, and blood pressure treatment. Aim 2: Estimate the comparative effects of conventional recommendations vs. tailored recommendations. Aim 3: Identify best practices for examining tailored recommendations in future guidelines. Methods: Under Aim 1, we will adapt existing microsimulation models for each preventive service to estimate the distribution of predicted (“individualized”) net benefit across the target Veteran population. This will inform the development of tailored recommendations under the guidance of the Aim 3 Expert Panel. Then, in Aim 2, we will use Aim 1 microsimulation evidence to examine the pros and cons of conventional vs. tailored approaches, again with close input from the Expert Panel. For Aim 3a, an Expert Advisory Panel of guideline experts will engage in a longitudinal process to identify best practices for conducting and presenting these microsimulation analyses in the development of future preventive care guidelines. In Aim 3b, interviews with guideline stakeholders will assess the potential for this microsimulation evidence to influence how future recommendations are established. Next steps/Implementation: The final product will be new guideline-level methods to support the adoption of tailored recommendations in national guidelines, when doing so would improve care (particularly among minority groups) and enable more Veteran-centered shared decision making. We will disseminate our work through professional channels, including through multiple research publications and presentations. Our strong engagement with VA guideline partners and experts from influential national guideline groups will facilitate wide dissemination of these methods to guideline groups. Our separate line of research to study and implement clinical decision support tools will support the downstream implementation of the proposed work.
External Links for this Project
Grant Number: I01HX003505-01A2
None at this time.
Cardiovascular Disease, Lung Disorders, Health Systems
TRL - Applied/Translational, Data Science
Best Practices, Healthcare Algorithms, Practice Patterns/Trends, Quality of Care
None at this time.