Revenue cycle management (RCM) refers to an institution's financial management process that helps track, identify, collect, and manage incoming payments. This helps businesses foster financial transparency within the company and charge patients the correct amount for the services and healthcare they receive. But because of the unique healthcare payment system in the United States, relatively few of these dollars change hands directly between providers and their patients. Instead, there is a complex reimbursement system, mostly driven by third-party payment transactions between government programs and insurance companies on the one hand, and healthcare providers on the other. Artificial Intelligence (AI) can help predict claim denials by analyzing past denial trends and alerting HIM professionals of the potential denial in advance of billing. This affords the opportunity to review and correct claims pre-bill. One major benefit of AI in RCM is increased efficiency. By automating routine tasks, healthcare organizations can free up their staff to focus on more important and value-added work. This can lead to improved productivity and faster turnaround times, ultimately resulting in improved patient care. This book provides an informative blueprint to help hospital and healthcare revenue cycle administration personnel along their Artificial Intelligence journey using the most commonly available administrative datasets, electronic claims, and electronic health records. Peppered throughout the book are hilarious personal anecdotes and cautionary tales from the author's wealth of experience in building AI solutions in the healthcare space.

The book begins with providing an overview of key concepts such as data science, machine learning, AI, language models (such as ChatGTP) and more. Then the author expands up the defined process in the context of common revenue cycle use cases that leverage electronic claims and electronic health records. Finally, the book provides guidance on how to evaluate AI solutions at each state of the development process including how to evaluate third-party vendor AI solutions.

About the Author

Korin Reid earned her Ph.D. in Chemical Engineering from Georgia Institute of Technology where she leveraged Artificial intelligence and Operations Research to determine the most cost-effective means of producing biodiesel from switchgrass in the southeastern United States. Since then, Dr. Reid has held numerous roles in the healthcare information technology space including serving as VP of Data Science and Innovation at a mid-market healthcare Revenue Cycle Information Technology Firm. Dr. Reid is also known for developing the first Artificial Intelligence model at a leading Fortune 5 Healthcare Company. The solution impacted more than 160 million patients. For this effort, she was named to Forbes 30 Under 30 in 2017. Dr. Reid frequently speaks on the topic of Artificial Intelligence in the healthcare space, sharing her unique framework for Data Scientists, Technologists, and Healthcare Providers alike to collaboratively develop transformative initiatives. She has a knack for conveying technical information in an easily understandable manner, often leveraging humor and entertaining personal anecdotes to do so.

Dr. Reid currently serves as CEO of Ellison Laboratories, a healthcare Information Technology company that leverages Artificial Intelligence and Operations Research to improve the quality and efficiency of healthcare delivery. Dr. Reid also teaches Data Science, Data Analytics, Software Development, and Data Engineering courses at Purdue Global and UC Berkeley. Dr. Reid has contributed as a writer, podcaster, and on-air talent for multiple media platforms including Forthdistrict and Model View Culture and has been featured in the Wall Street Journal and Forbes Magazine.

Book Information
ISBN 9781032639482
Author Korin Reid
Format Paperback
Page Count 204
Imprint Productivity Press
Publisher Taylor & Francis Ltd
Weight(grams) 453g