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