AI-Assisted Medical Coding and Billing: Applied Practice
The AI-Assisted Medical Coding and Billing: Applied Practice course equips healthcare coding and billing professionals with the knowledge and skills to responsibly integrate artificial intelligence (AI) into their workflows. As AI tools become increasingly common in revenue cycle management, this course helps learners understand how to use these technologies effectively while maintaining accuracy, compliance, and ethical standards.
Participants explore the fundamentals of AI, including the differences between generative and non-generative models, and examine leading AI platforms used in medical coding, billing, and clinical documentation improvement. Through real-world case studies and hands-on prompting exercises, learners gain practical experience applying AI to tasks such as diagnosis and procedure prediction, audit support, and documentation review.
Education and experience requirements
Learners should have foundational knowledge of medical coding and billing processes, including familiarity with CPT, ICD-10-CM, and HCPCS coding systems
While not mandatory, prior experience working in healthcare coding, billing, CDI, or revenue cycle roles enhances the learning experience and practical application of AI tools
Participants should be comfortable using web-based applications, navigating online platforms, and interacting with digital tools (e.g., EHR systems, coding software)
A working understanding of healthcare documentation, physician notes, and clinical reports is beneficial for applying AI-assisted tools effectively
Continuing education units (CEUs)
Approval for 10 CEUs upon successful course completion.
Required books
AI-Assisted Medical Coding and Billing: Applied Practice - Training Manual; AAPC publisher (included in course)
Course format
This is an online self-paced instruction course. You get the flexibility to complete the course on your own time, at your own pace with on-demand training. Get the help you need from the certified coaching team at any point in your training via email.
Course length
This course is designed to be taken at your own pace within a 4-month period or less. Your enrollment date begins on the date of your purchase.
Certificate of completion
Certificate issued upon completion with a 70% passing score.
Who should take this course
Medical billers and coders – Professionals seeking to integrate AI into their daily coding and billing tasks to improve accuracy, speed, and compliance
Revenue cycle managers – Leaders looking to adopt AI tools to optimize productivity, reduce denials, and ensure regulatory alignment across their teams
CDI (clinical documentation improvement) specialists – Those aiming to enhance clinical documentation analysis and query generation using AI-assisted insights
Healthcare compliance officers and auditors – Professionals needing a firm understanding of AI’s risks and ethical implications in healthcare coding environments
Healthcare IT and innovation teams – Stakeholders exploring responsible implementation of AI solutions within coding systems and EHR workflows
Students and entry-level healthcare admin professionals – Individuals entering the healthcare revenue cycle workforce who want a forward-looking skillset that includes AI fluency
Course objectives:
Once you complete this course, you'll be equipped to:
Integrate AI tools into medical coding, billing, and documentation workflows responsibly
and effectivelyEvaluate and apply AI platforms (e.g., ChatGPT, 3M) to support tasks such as procedure prediction, documentation assistance, and audit preparation
Craft clear and compliant AI prompts and ensure HIPAA compliance and ethical safeguards are in place when using AI-assisted tools
Collaborate with AI systems to enhance coding accuracy, documentation quality, and overall efficiency within revenue cycle operations
Assess risks and limitations of AI in healthcare and implement quality assurance processes to validate AI-generated outputs
Course content:
Chapter 1: Foundations of AI in Healthcare and Coding
What is AI? How does it work?
Types of AI and machine learning (generative vs. non-generative)
Common AI models and coding tools used in healthcare
AI use cases: procedure/diagnosis prediction, audit support, CDI review, AI
scribingPlatform comparison: ChatGPT, Anthropic, Grok, 3M, Semantic, etc.
Chapter 2: Compliance, Ethics, and AI Safeguards in Medical Coding
AI as a support tool, not a decision-maker
Regulatory considerations (e.g., FDA involvement)
HIPAA and AI: data privacy, security, and breach risks
Ethical boundaries and liability concerns
Safeguards and quality assurance processes
Chapter 3: Prompting, Practice, and Real-World AI Applications
Prompt engineering: key components of a good prompt
Communicating tasks and expectations to AI
Practice exercises in prompt refinement
Case studies: AI in RCM, billing accuracy, audit support
Human-AI collaboration for optimal results
Course terms and conditions
By purchasing any online course and other items, I certify that I have read and agree to the online course terms and conditions and the refund policy.
Date of purchase is considered your course start date, additional time will be given for processing of your order. After your order is processed, you will receive further email instruction for course access.
This online course must be completed within a 1-year period or less from date of purchase. Additional time extensions may be purchased for a monthly fee. Date of purchase is considered your course start date. After your order is processed you will receive further email instruction on course access.