I-Corps: Translation Potential of Artificial Intelligence (AI)-Driven, Real-Time Billing for Smoking Cessation

The broader impact of this I-Corps project is the development of a real-time billing tool to support accurate coding for smoking cessation services within healthcare systems. The solution addresses missed coding and billing opportunities that compromise data integrity and public health insights, impacting quality metrics and population health assessments.

For example, gaps in data can make it unclear if smoking cessation care occurred but was not billed or if the care was overlooked entirely. By automating the identification of eligible encounters directly within clinical workflows, this solution enhances both the financial viability and transparency of preventive services like smoking cessation. Broad adoption of this solution could bolster health systems’ capabilities to provide, track, and bill for essential preventive care, aligning clinical actions with public health priorities.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on a machine learning model that analyzes clinical notes in real-time to identify billing-eligible smoking cessation counseling. By directly integrating with electronic health records, the solution ensures immediate, accurate billing and correct data capture from clinical notes during patient encounters, addressing the dual challenge of missed billing and incomplete data.

Initial testing demonstrates high predictive accuracy, establishing a foundation for scaling this solution across healthcare systems. This project aims to bridge gaps in automated healthcare billing, advancing clinical informatics, and enhancing data-driven public health outcomes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.