Transforming the Healthcare Revenue Cycle with Artificial Intelligence: A Guide to Building Impactful...

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Korin Reid

Transforming the Healthcare Revenue Cycle with Artificial Intelligence

A Guide to Building Impactful AI Using Electronic Claims and Electronic Heath Record Data

Korin Reid

$103

Paperback

Forthcoming
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English

Productivity Press
08 August 2025

Management accounting & bookkeeping; Business strategy; Management: leadership & motivation; Epidemiology & medical statistics; Artificial intelligence

Revenue cycle management (RCM) refers to an institution’s financial management process that helps track, identify, collect, and manage incoming payments. This process helps businesses foster financial transparency within the company and charge patients the correct amount for the services 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 health information management (HIM) professionals of potential denials in advance of billing. This affords an 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 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 AI journey by using the most commonly available administrative datasets, electronic claims, and electronic health records. Peppered throughout the book are hilarious anecdotes and cautionary tales from the author’s experience in building AI solutions in the healthcare space.

The book begins with an overview of key concepts such as data science, machine learning, AI, language models (e.g., ChatGPT), and more. The author expands on 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 point of the development process, including third-party vendor AI solutions.

By: Korin Reid
Imprint: Productivity Press
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 453g
ISBN: 9781032639482
ISBN 10: 1032639482
Pages: 180
Publication Date: 08 August 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Forthcoming

Chapter 1: What is AI and Machine Learning. Chapter 2: Common Algorithms for Revenue Cycle Use Cases. Chapter 3: Other Modeling Categories. Chapter 4: Model Development Process. Chapter 5: Revenue Cycle Process Overview. Chapter 6: The Healthcare AI Process. Chapter 7: The MVP Process for Healthcare AI. Chapter 8: Post MVP Process for Healthcare AI. Chapter 9: AI in Healthcare Teams. Chapter 10: Big Data for EHR and Claim Data. Chapter 11: Production Deployment, Privacy, Security, and Key Issues

Korin Reid earned a PhD in chemical engineering at Georgia Institute of Technology, where she leveraged AI and operations research (OR) 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 vice president of data science and innovation at a mid-market healthcare revenue cycle information technology firm. Dr. Reid is also known for developing the first AI 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. She frequently speaks on the topic of AI 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 is Chief Executive Officer of Ellison Laboratories, a healthcare information technology company that leverages AI and OR to improve the quality and efficiency of healthcare delivery. She also teaches in the master’s in data science program at UC Berkeley. Dr. Reid has contributed as a writer, podcaster, and on-air talent for multiple media platforms and has been featured in the Wall Street Journal and Forbes magazine.