Abstract
OBJECTIVES: The objective of the Prediction Augmented Screening Initiative (PASI) pilot application was to design and implement a clinical tool to optimize the lung cancer screening (LCS) workflow for providers. The Boston Informatics Group (BIG) at the Department of Veterans Affairs (VA) developed the Enabling Technologies for Rapid Learning Health Systems Platform (ENTHRALL) to support delivery of knowledge in a Learning Health System (LHS) framework. The BIG leveraged ENTHRALL to implement the PASI pilot application on a very short timeline. The application uses VA data to estimate patients' benefit from LCS based on National Cancer Institute (NCI) models, allowing proactive outreach to patients with high predicted benefit from LCS. METHODS: The application was designed utilizing ENTHRALL infrastructure, including optimized nightly data pulls to gather patient information, Natural Language Processing to extract smoking history, and a user interface (UI). Cross-functional collaboration allowed the use of the NCI's peer-reviewed prediction algorithm to provide daily patient benefit scores. RESULTS: The UI displays patients in descending order of benefit, delivering a prioritized list to providers. Clinicians can fill in information and track patient status to assist with their outreach activities. For the pilot, only patients meeting USPSTF LCS criteria (the current field standard) were displayed. Five VA stations were included. CONCLUSIONS: Utilizing the VA BIG's ENTHRALL framework for an LHS, the group demonstrated their ability to design and deliver a new application within 3 months of inception, which was successfully utilized at 5 VA hospitals. The VA's capability to rapidly build clinically relevant applications will help it become an LHS tailored to current problems impacting the Veteran. Due to the success of the pilot, the clinical research team got approval to expand their study. The BIG is working on a non-pilot build.