Machine Learning in Healthcare: Regulatory Requirements, Reimbursement Challenges, Privacy and Security Risks
Recording of a 90-minute CLE video webinar with Q&A
This CLE course will guide healthcare counsel on machine learning in the healthcare context. The panel will discuss how healthcare companies and providers are using machine learning to provide healthcare, patient care, and administrative processes. The panel will examine the regulatory requirements and the implications for reimbursement. The panel will also address privacy and security issues and offer best practices for compliance when using machine learning.
Outline
- Machine learning in healthcare
- Patient care
- Administrative processes
- Key considerations
- Regulatory requirements
- Reimbursement
- Standard of care
- Privacy and security
- Ethical issues
- Other
- Contractual issues
- Indemnifications
- Reps and warranties
- Insurance
- Best practices for compliance when using machine learning in healthcare
Benefits
The panel will review these and other key issues:
- How can healthcare providers minimize liability risks when using machine learning for patient care--or when deciding not to use it?
- Who may be liable when a healthcare provider's care is based on machine learning?
Faculty
Deborah Godes
Vice President
McDermott+Consulting
Ms. Godes advises clients on reimbursement and policy strategy for medical devices, diagnostics, biologics and health... | Read More
Ms. Godes advises clients on reimbursement and policy strategy for medical devices, diagnostics, biologics and health services by public and private payers. With more than 25 years of health industry experience, she leverages her deep knowledge and strong industry relationships to deliver strategic, operational and policy consultative services to companies of all sizes, from start-ups to Fortune 100 companies. For the past decade, Ms. Godes has worked alongside healthcare innovators to advance their market access goals, including: navigating the complex Centers for Medicare and Medicaid Services landscape; addressing the challenges and opportunities of bringing e-products to market; and providing insight on the U.S. healthcare system and the health industry. She also collaborates with clients pursuing Medicare coding, coverage and payment of their groundbreaking, digitally-enabled healthcare solutions and services, including diagnostic tests and medical devices, artificial intelligence-powered technologies, remote monitoring tools, and other innovative health services.
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Carolyn V. Metnick
Partner
McDermott Will & Emery
Ms. Metnick concentrates her practice on transactional and business issues affecting healthcare providers. She... | Read More
Ms. Metnick concentrates her practice on transactional and business issues affecting healthcare providers. She routinely guides her healthcare clients through various transactions including joint ventures, M&As and reorganizations. Ms. Metnick counsels her clients on governance matters, regulatory issues, and federal fraud and abuse laws. She also counsels her clients on HIPAA compliance, healthcare technology (mData), and disputes and litigation with an emphasis in contract law, covenants not to compete, and business torts.
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Bradley M. Thompson
Member
Epstein Becker & Green
Mr. Thompson counsels medical device, drug, and combination product companies on a wide range of FDA and FTC... | Read More
Mr. Thompson counsels medical device, drug, and combination product companies on a wide range of FDA and FTC regulatory, reimbursement, and clinical trial issues. He is a quantitative thinker by nature, who enjoys tinkering with algorithms. To develop a deeper understanding of machine learning algorithms, Mr. Thompson earned a Master of Applied Data Science in February 2022 from the University of Michigan’s School of Information. As a part of that curriculum, he studied the math, statistics, and computer science (Python), which serve as the basis for artificial intelligence (AI). Specific coursework included SQL & Databases, SQL Architectures & Technologies, Efficient Data Processing, Scalable Data Processing, Math Methods for Data Science, Data Mining, Supervised Learning, Unsupervised Learning, Deep Learning, Machine Learning Pipelines, Natural Language Processing, and Network Analysis. At Epstein Becker Green, Mr. Thompson leads an initiative to serve the legal needs of those clients that either develop or use AI tools. That initiative cuts across the firm’s practice areas to include both health and labor.
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