Using artificial intelligence to improve revenue cycle operations

In God we trust; all others must bring data

                                — W. Edwards Deming

The ability to use the data aggregated throughout the revenue cycle is critical to proactive issue resolution, project prioritization, estimation of financial impact of revenue cycle initiatives and forecasting. However, data analysis at each stage of the revenue cycle is dependent on disparate data silos from numerous systems. This analysis requires manual work to create one comprehensive report for healthcare executives — a fragmented, manual process that creates risk for costly delays, confusion and discrepancies.

In Using AI to Improve Revenue Cycle Innovation at HFMA’s 2019 Annual Conference, Jakeline Chalarca, director of revenue integrity for Moffitt Cancer Center, and Korin Reid, PhD, principal data scientist at Craneware, will define the steps used to design, develop, implement and evaluate an impactful AI solution to answer the need for timely, accurate and actionable insights for revenue cycle executives.

This interactive session will provide participants with opportunities to think beyond the use case formally presented in the lecture portion. Attendees will learn how stakeholders from a variety of backgrounds can and should participate in the process of applying AI to improve revenue cycle outcomes. Specifically, the interactive portion will allow attendees to learn from their peers by brainstorming innovative revenue cycle AI solutions in small groups and putting the innovation process defined in the lecture portion to work.

Learning objectives for this session include

• Analyze the current AI technology landscape and define the various steps required to design, develop, implement and evaluate impactful AI solutions in the healthcare revenue cycle space through a real-world charge-capture case study.
• Identify how stakeholders from a variety of backgrounds (both technical and nontechnical) can and should participate in the revenue cycle data-science innovation process to improve the quality and efficiency of healthcare delivery while avoiding common pitfalls associated with AI innovation efforts.
• Discuss and brainstorm opportunities — in a small-group discussion format — to apply AI to improve revenue cycle outcomes.
• Simulate the data science innovation process in small groups by following a series of defined steps that serve as a framework for designing innovation processes, defining requisite resources and stakeholders and providing an evaluation framework for opportunities identified in the brainstorming session.

Reid and Chalarca will present Using AI to Improve Revenue Cycle Innovation (session D03) on Tuesday, June 25 at 10 a.m. in conference room W307 A-D. Attendees will receive 1.5 CPE credits for their participation.