HIT 217 - Artificial Intelligence for Healthcare Systems

Credits: 3
2 Lecture Hours 2 Lab Hours

Prerequisites: HIT 103, MAT 165

Co-requisites: HIT 215 , HIT 216

Description
This course provides students a comprehensive overview of machine learning concepts in healthcare systems including programming and training of artificial intelligence to detect fraud, waste and abuse in healthcare systems. Students also focus on data elements in the revenue cycle process, natural language processing and the future of automation in medical coding and revenue cycle.

Learning Outcomes
Upon successful completion of the course, students will:

Monitor training data to identify risks or adverse events.
Test artificial intelligence systems for accuracy.
Build algorithims from payer policies to detect fraud.
Identify trends in artificial intelligence for health informatics.
Describe machine learning techniques.
Develop automation in revenue cycle.
Create effective prompts.

Listed Topics

Data elements in reimbursement and claims
Automation techniques
Machine learning
Data validation
Java Script Object Notation (JSON)
Natural Language Processing (NLP)

Reference Materials
Current software packages

Students who successfully complete this course acquire general knowledge, skills and abilities that align with CCAC’s definition of an educated person. Specifically, this course fulfills these General Education Goals:

Quantitative & Scientific Reasoning
Technological Competence

Approved By: Dr. Quintin B. Bullock Date Approved: 04/13/2025

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