Transforming the Healthcare Revenue Cycle with Artificial Intelligence A Guide to Building Impactful AI Using Electronic Claims and Electronic Heath Record Data.

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 rec...

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Bibliographic Details
Main Author Reid, Korin
Format Electronic eBook
LanguageEnglish
Published Oxford : Productivity Press, 2025.
Subjects
Artificial intelligence > Medical applications.
Information storage and retrieval systems > Medical care, Cost of.
Medical informatics > Data processing.
Revenue management.
elektronické knihy
electronic books
Online AccessFull text
ISBN9781040382455
1040382452
9781032639512
1032639512
9781040382462
1040382460
Physical Description1 online resource (199 p.)

Cover

Table of Contents:
  • Cover
  • Half Title
  • Title
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • Author
  • Introduction
  • 1 What Is AI and Machine Learning
  • Supervised Learning
  • Regression
  • Classification
  • Types of Classification Models
  • Unsupervised Learning
  • Clustering
  • Frequent Pattern Mining
  • Dimensionality Reduction
  • Semi-Supervised Learning
  • Self-Supervised Learning
  • Reinforcement Learning
  • Recommendation Engines
  • 2 Common Algorithms for Revenue Cycle Use Cases
  • Logistic Regression and Linear Regression
  • Neural Networks
  • Support Vector Machines
  • Decision Trees
  • 3 Other Modeling Categories
  • Image Processing
  • Graph Neural Networks
  • Large Language Models and Natural Language Processing
  • Machine Learning versus Automation
  • Machine Learning versus Optimization
  • Descriptive, Predictive, and Prescriptive Analytics
  • 4 Model Development Process
  • Data Selection and Acquisition
  • Feature Engineering
  • Model Selection and Tuning
  • 5 Revenue Cycle Process Overview
  • What Is Revenue Cycle Operations
  • Front-End Revenue Cycle
  • Mid-Revenue Cycle
  • Back-End Revenue Cycle
  • 6 The Healthcare AI Process
  • The Discovery Process for Healthcare AI
  • Ask and Observe
  • The Art of What's Possible
  • Validate
  • 7 The MVP Process for Healthcare AI
  • Business Overview and Requirements
  • Data Overview and Requirements
  • Evaluation Agreement
  • Development
  • Evaluation
  • 8 Post MVP Process for Healthcare AI
  • Data at Scale Overview and Requirements
  • Model at Scale Overview and Requirements
  • Product at Scale Overview and Requirements
  • Hardening
  • The Post-Deployment Process for Healthcare AI and the Importance of Feedback Loops
  • 9 AI in Healthcare Teams
  • Data Scientists in Healthcare
  • The Business Analyst Persona
  • The Statistician Persona
  • The Software Developer Persona
  • Data Engineers in Healthcare
  • Product Management for Healthcare AI
  • General Software Engineering and Quality Assurance
  • Subject Matter Experts for Healthcare AI
  • Team Organization
  • Full Stack Data Scientist versus Individualized Roles
  • Hub and Spoke versus Centralized
  • Notes
  • 10 Big Data for EHR and Claim Data
  • Distributed Computing Overview
  • Big Data Processing Frameworks
  • Batch versus Streaming for Claim and EHR Data
  • Publish/Subscribe Queues
  • Apache Spark for Claim and EHR Data
  • Apache Flink for Claim and EHR Data
  • Data Store Overview
  • Wide Column versus Document for Claim and EHR Data
  • Search for Claim and EHR Data
  • Graph Databases for Claim and EHR Data
  • Large Language Model Data Stores
  • Cloud-Based Options
  • Data Science Tools for Healthcare AI
  • R versus Python, the War for Data Science
  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Large Language Models
  • Graph Machine Learning
  • 11 Production Deployment, Privacy, Security, and Key Issues
  • Data Privacy for Healthcare AI Use Cases
  • HIPAA
  • Safe Harbor Method

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