Machine Learning in Healthcare: Regulatory Requirements, Reimbursement Challenges, Privacy and Security Risks

A live 90-minute CLE video webinar with interactive Q&A

This program is included with the Strafford CLE Pass. Click for more information.
This program is included with the Strafford All-Access Pass. Click for more information.

Thursday, March 9, 2023

1:00pm-2:30pm EST, 10:00am-11:30am PST

Early Registration Discount Deadline, Friday, February 10, 2023

or call 1-800-926-7926

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.

Description

Machine learning has virtually unlimited uses in the healthcare industry. From pacemakers to smart scalpels, to smartwatches to radiology, and detecting cancers to mapping infectious diseases, healthcare providers can leverage machine learning to provide better healthcare. Machine learning can also be used to streamline administrative processes in hospitals.

There are legal issues that are raised with the use of machine learning in healthcare. Among the legal concerns are the regulatory requirements, reimbursement issues, privacy and security issues, and standard of care. For example, machine learning presents challenges to companies with obligations to safeguard protected health information and other sensitive information. Further, the use of machine learning may implicate HIPAA as well as state privacy and security laws. Machine learning presents risks of privacy breaches and cybersecurity threats.

It is critical for healthcare organizations, providers, and counsel to recognize how machine learning impacts the provision of care and address the legal implications.

Listen as our authoritative panel of healthcare attorneys examines 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 in healthcare.

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Outline

  1. Machine learning in healthcare
    1. Patient care
    2. Administrative processes
  2. Key considerations
    1. Regulatory requirements
    2. Reimbursement
    3. Standard of care
    4. Privacy and security
    5. Ethical issues
    6. Other
  3. Contractual issues
    1. Indemnifications
    2. Reps and warranties
    3. Insurance
  4. 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

Godes, Deborah
Deborah Godes

Vice President
McDermott+Consulting

Ms. Godes advises clients on reimbursement and policy strategy for medical devices, diagnostics, biologics and health...  |  Read More

Metnick, Carolyn
Carolyn V. Metnick

Partner
McDermott Will & Emery

Ms. Metnick concentrates her practice on transactional and business issues affecting healthcare providers. She...  |  Read More

Thompson, Bradley M.
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

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Early Discount (through 02/10/23)

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Early Discount (through 02/10/23)

You may pre-order a recording to listen at your convenience. Recordings are available 48 hours after the webinar. Strafford will process CLE credit for one person on each recording. All formats include course handouts.

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