Real-time automated billing for tobacco treatment: developing and validating a scalable machine learning approach
CigStopper validates foundational methods for automating the discernment of appropriate billing codes for eligible smoking cessation counseling care.
Authors
- Scott D. Nelson (ORCID: https://orcid.org/0000-0002-8435-5065)
- Adam T. Wright (ORCID: https://orcid.org/0000-0001-6844-145X)
- Michael E. Matheny (ORCID: https://orcid.org/0000-0003-3217-4147)
- Daniel Fabbri (ORCID: https://orcid.org/0000-0003-0530-2510)
- Derek J Baughman (ORCID: https://orcid.org/0000-0001-5549-6697)
- Hilary A. Tindle (ORCID: https://orcid.org/0000-0002-7581-7483)
- Lina Sulieman (ORCID: https://orcid.org/0000-0002-2680-8230)
- A. Goodman
- Layth Qassem
Institutions
- Louisiana State University in Shreveport (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf039
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Vanderbilt University
- Vanderbilt-Ingram Cancer Center
- Vanderbilt University Medical Center