Real-Time Automated Billing for Tobacco Treatment: A CDS Hook Approach for Simulating Clinician Facing Coding Prompts Within EHRs

Poster Number: P188
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Natural Language Processing, Interoperability and Health Information Exchange, Rule-based artificial intelligence, Workflow
Primary Track: Applications
Programmatic Theme: Clinical Informatics

A machine learning prediction model, CigStopper, demonstrated automation of smoking cessation billing using a CDS Hook in a simulated clinical workflow. The approach showcases the real-time interoperability of an externally hosted API interfacing with EHRs using a public sandbox proxy with SMART on FHIR standards. This proof-of-concept emphasizes the functional potential for scalable, EHR-agnostic CDS tools to improve clinical care by automating billing processes with real-time decision support.

Speaker(s):
Derek Baughman, MD
Vanderbilt University

Author(s):
Layth Qassem, PharmD - VUMC; Eric Morgan, MS - Plasma FHIR; Lina Sulieman, PhD - Vanderbilt University Medical Center; Hilary Tindle, MD, MPH - Vanderbilt University Medical Center; Michael Matheny, MD, MS, MPH, FACMI, FAMIA - Vanderbilt University Medical Center; Daniel Fabbri, PhD - Vanderbilt; Adam Wright, PhD - Vanderbilt University Medical Center;