Course Description:

In recent years, generative artificial intelligence (GenAI) has passed medical exams, diagnosed complex cases. In fact, large language models (LLMs) such as GPT-4, Google’s BARD, and BioGPT now perform complex tasks that can completely transform clinical decision-making and enhance administrative efficiency. The financial backbone of healthcare, RCM, entails everything from payments and pre-authorization before treatment to reimbursement, compliance, and interoperability with other systems. This flow of revenue from patient encounters to post discharge management has grown even more complicated with evolving regulations, data overload, shifting payment models, and increasing patient financial responsibility. This is where Advanced AI can be a game-changer by managing and processing the intricate web of unstructured data across clinical notes, insurance claims, diagnostic images, medical charts, and more.

The presenter will showcase advanced AI use cases for coding to demonstrate the promising advancements that this technology can bring to coding operations to reduce the resource burden, decrease denials, improve workflow and increase accuracy and efficiency. The speaker will unpack the journey from the differences between Computer Assisted Coding (CAC)/ Natural Language Processing (NLP) of today versus advanced AI platforms using large language models (LLMs) infused with deep machine learning. She will showcase the requirements, outcomes, selection process and the AI governance guardrails needed to guide your organization to success.

Learning Objectives:

• To provide a road map to infuse current documentation capture and computer assisted coding systems with advanced AI to reduce resource burden and increase productivity without adversely impacting quality, financial and denial metrics
• To identify use cases ripe for AI using large language models to reduce coder, physician and CDI burden
• To facilitate the analysis of the current AI technology landscape to enable the creation of a business case and project charter
• How to select, oversee and manage an AI technology partner from a proof of concept (if applicable) to implementation
• To assist with the identification of an AI governance strategy with specific guardrails to mitigate potential risks, and track metrics and revenue realization

Areas Covered in the Session:

• Current Coding Scenarios
• Learning Outcomes
• Business Case to Leverage Advancements in AI to facilitate Coding and Clinical Documentation Improvement (CDI)
• Journey from CAC (NLP) to AI
• AI Use Cases for CDI
• AI Use Cases for Coding
• How to Prepare a Business Case, Choose a Partner and Define Metrics
• AI Governance – Rules and Regulations
• Summary/ Next Steps
• Live Q&A Session

Suggested Attendees:

• Chief Revenue Officers
• RCM Directors
• Technology Officers
• Coding Directors
• Coding Professionals
• Compliance Officers
• Billing Professionals
• Auditors
• Healthcare Providers
• Practice Administrators
• Revenue Cycle Staff
• CDI Specialists
• Health Information Management (HIM) Directors and Officers