The Impact of the Chevron Decision on Healthcare:

Navigating the Complex Landscape with the Right Standards

by: Sherri Douville, CEO at Medigram | IEEE UL 2933 Trust SG Co-Chair | Chair, TTIC

Illustration on the Chevron decision and it’s legal implications by Linas Garsys/The Washington Times more >

The U.S. Supreme Court’s Chevron decision, formally known as Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc., has long been a cornerstone of administrative law. It established the principle that courts should defer to interpretations of statutes made by government agencies unless those interpretations are unreasonable. However, the recent shift in how Chevron deference is applied or reconsidered could have profound implications across various sectors, including healthcare.

Understanding the Chevron Decision and Its Healthcare Impact

The Chevron decision has traditionally allowed agencies like the FDA, CMS, and others to interpret vague or ambiguous laws related to healthcare. This deference gave these agencies the flexibility to adapt and evolve regulations to meet the changing landscape of healthcare. However, the potential rollback of Chevron deference could lead to stricter judicial scrutiny of agency decisions, limiting the ability of these agencies to interpret regulations flexibly.

In healthcare, this shift means that regulations could become more rigid, and compliance with federal laws could become more challenging. Healthcare organizations may face increased litigation risks as courts take a more active role in interpreting statutes, potentially leading to inconsistencies and uncertainties in the application of the law.

The Role of Standards as a Smoother Path Forward

In this evolving regulatory landscape, adopting and adhering to well-established standards can provide a smoother path forward for healthcare organizations. Standards like IEEE UL 2933, which focuses on the secure and ethical integration of AI and IoT systems in clinical settings, offer a structured approach to compliance. These standards are developed through a consensus-driven process, involving experts from various fields (250 in 22 countries), is ANSI-accredited, which adds an additional layer of legal protection and credibility.

Adhering to such standards helps healthcare organizations ensure that their technologies and practices align with best practices, reducing the risk of legal challenges in a post-Chevron world. These standards provide clear guidelines that can help navigate the complex regulatory environment, helping support a form of “safe harbor” that can protect against the uncertainties of shifting legal interpretations.

Why Many Data, AI Experts, and Doctors Resist Standards

Despite the clear benefits, many professionals in the fields of data, AI, and medicine often resist or misunderstand the importance of standards.

Several factors contribute to this resistance:

Focus on Innovation Over Compliance:

• Data and AI experts are typically driven by innovation, pushing the boundaries of what technology can achieve. Standards, which often codify established practices, may be perceived as stifling creativity and slowing down progress. This focus on innovation can sometimes overshadow the need for compliance and the role standards play in ensuring safety and interoperability.

Lack of Exposure and Education:

• Many experts, especially those who specialize in cutting-edge technologies, may not have been adequately exposed to the importance of standards in their education or professional training. The intricate details of regulatory compliance and the role of standards in safeguarding patients and systems are often underemphasized in technical and medical education.

Complexity and Accessibility:

• Standards can be complex and difficult to interpret, especially for those who are not familiar with the process of standardization. This complexity can deter professionals from engaging with standards, leading to a lack of understanding and appreciation for their importance.

Cultural Resistance:

• In the medical community, there is often a cultural resistance to anything perceived as bureaucratic or outside the traditional scope of medical practice. Doctors and healthcare professionals may view standards as unnecessary red tape, rather than as essential tools for ensuring patient safety and system interoperability.

Embracing Standards for a Stable Future in Healthcare

The shifting landscape of healthcare regulation, particularly in a post-Chevron world, underscores the importance of ready, well managed and governed organizations deriving strategic advantage from adopting robust standards like IEEE UL 2933. While the resistance from data, AI experts, and doctors is expected and understandable, it is essential for these professionals to recognize that standards are not just bureaucratic hurdles, but critical frameworks that ensure safety, compliance, and trust in healthcare systems.

For healthcare organizations, particularly those navigating the complex integration of AI and IoT, embracing these standards is not just a matter of regulatory compliance, but a strategic move to safeguard operations and reputation in an increasingly litigious environment. By fostering a culture of understanding and appreciation for standards, healthcare leaders can better prepare their organizations for the challenges ahead, ensuring a stable and compliant path forward in the ever-evolving landscape of healthcare.

Many data and AI experts often express discomfort or frustration with standards. This sentiment can arise from several factors:

Perceived Constraints on Innovation: Standards are sometimes seen as limiting creativity and innovation. Data and AI experts, who often work at the cutting edge of technology, might feel that rigid standards can stifle the development of novel approaches or solutions.

Complexity and Bureaucracy: The process of developing and adhering to standards can be seen as overly complex and bureaucratic. For experts who are used to rapid iteration and agile methodologies, the slower, more formalized nature of standards can be frustrating.

Lack of Familiarity: Many data and AI professionals come from backgrounds that emphasize technical expertise and innovation rather than compliance and regulation. This can lead to a lack of understanding or appreciation for the role that standards play in ensuring safety, interoperability, and regulatory compliance.

Evolving Nature of AI: AI and data science are rapidly evolving fields, and some experts may feel that standards struggle to keep pace with the latest advancements. They might worry that by the time a standard is established, it could already be outdated.

Varied Global Standards: The existence of different standards across countries and industries can create confusion and frustration. For AI and data experts working in a global context, navigating these varied standards can be challenging.

Despite these perspectives on concerns, standards are crucial for ensuring that AI and data systems are safe, ethical, and interoperable, especially in regulated industries like healthcare. The challenge lies in balancing innovation with the need for consistency and reliability that standards provide. The most strategically adept leaders are designing their implementations of these standards now.

References at bottom of page.

Sherri Douville BIO

Sherri Douville leads at the intersection of healthcare technology, mobility, cybersecurity, and artificial intelligence. As the CEO of Medigram and the Chair of the Trustworthy Technology and Innovation Consortium (TTIC), Sherri is instrumental in advancing secure, AI-enabled mobile solutions to transform healthcare communication and decision-making. She co-chairs the IEEE/UL P2933 standard SG for trust in clinical IoT, where she helps lead global efforts to establish ethical and technical standards for the trustworthy and secure integration of AI and IoT in healthcare.

With a deep understanding of the regulatory landscape, Sherri is actively engaged in addressing the challenges posed by evolving regulations, including the recent Chevron decision. Her work focuses on ensuring that our exclusive ecosystem of healthcare organizations and select technology vendors navigate this complex environment by adhering to robust standards that enhance patient safety, data security, and operational excellence.

In addition to her leadership roles, Sherri is a sought-after speaker and moderator, frequently contributing insights on keynotes, talks, and panels addressing the most pressing challenges in healthcare and technology. Her work is dedicated to fostering collaboration across industries, driving the development of trustworthy AI, and ensuring that technological advancements align with global best practices.

References

• Garsys, Linas. “Illustration on the Chevron Decision and Its Legal Implications.” The Washington Times. Accessed [Date]. https://www.washingtontimes.com/.
• “Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc.” Legal Information Institute. Accessed [Date]. https://www.law.cornell.edu/supremecourt/text/467/837.
• IEEE Standards Association. “IEEE UL 2933: Standard for Clinical Internet of Things (IoT).” IEEE Standards Association. Accessed [Date]. https://standards.ieee.org/standard/UL_2933-2020.html.
• “HIPAA (Health Insurance Portability and Accountability Act).” U.S. Department of Health & Human Services. Accessed [Date]. https://www.hhs.gov/hipaa/index.html.
• “General Data Protection Regulation (GDPR).” European Commission. Accessed [Date]. https://ec.europa.eu/info/law/law-topic/data-protection_en.
• International Organization for Standardization (ISO). “ISO/IEC 27701:2019 Privacy Information Management System.” ISO. Accessed [Date]. https://www.iso.org/standard/71670.html.
• “NIST AI Risk Management Framework.” National Institute of Standards and Technology (NIST). Accessed [Date]. https://www.nist.gov/itl/ai-risk-management-framework.
• Organisation for Economic Co-operation and Development (OECD). “OECD Principles on Artificial Intelligence.” OECD. Accessed [Date]. https://oecd.ai/en/dashboards.
• World Health Organization (WHO). “WHO Guidance on Ethics and Governance of AI in Health.” WHO. Accessed [Date]. https://www.who.int/publications/i/item/9789240029200.
• U.S. Food and Drug Administration (FDA). “Digital Health Innovation Action Plan.” FDA. Accessed [Date]. https://www.fda.gov/media/106331/download.
• European Commission. “Ethical Guidelines for Trustworthy AI.” European Commission. Accessed [Date]. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.