AI and the Healthcare Coverage Conundrum
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January 16, 2026
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Jessica Landau
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5 min read
Expertise
Artificial intelligence (AI) is transforming healthcare at a notable pace and presents the potential to significantly influence care delivery across the healthcare industry spectrum. For healthcare providers, adopting AI is no longer optional. With innovation and investment reshaping the landscape, organizations that fail to embed technology into their operations risk falling behind in delivering effective, high-quality care.
AI presents unique opportunities to enhance care delivery and alleviate persistent staffing challenges facing the healthcare industry. Staffing shortages have led to burnout and high turnover, ultimately affecting the quality of care provided to patients. AI also has the potential to improve the quality of treatment by advancing the focus on personalized medicine and patient centric outcomes. Enhancing efficiency and accuracy in both diagnostics and treatment is another application of AI that can improve care delivery.
There are numerous use cases for AI adoption within the healthcare industry; however, its adoption requires careful consideration. Understanding technology’s limitations is critical to achieving successful implementation.
AI adoption presents new risk and uncertainty. As providers rely more heavily on AI, it becomes increasingly difficult to separate human error from technological failure or systemic exposure. For insurers and brokers, this blurring of responsibility is already testing traditional coverage frameworks and exposing coverage gaps that could widen as AI becomes integrated into clinical workflows and operational systems.
The insurance industry has long grappled with questions arising from the integration of technology into models of care. Similar questions surfaced when telehealth technology and services were introduced, but AI’s widespread adoption has magnified the issue and further complicated the industry’s ability to assess liability and coverage implications. As AI tools become more deeply embedded in the healthcare ecosystem, the potential for gray areas expands dramatically.
The Data Dilemma
AI systems are only as reliable as the data they use. Questions centered around data quality, bias and accuracy all have direct implications for patient care.
If an AI model is built on incomplete or biased data, it can unintentionally introduce inequities in healthcare delivery. For insurers, this creates downstream exposure. Incorrect diagnoses, inappropriate treatment recommendations and documentation errors can all lead to claims.
Equally concerning is the risk of overreliance on AI. As healthcare providers increasingly rely on AI-assisted tools, there is a concern that clinical judgment could be diminished. It is vital that healthcare organizations establish best practices, training protocols and oversight processes to ensure AI supports and enhances clinical expertise rather than replace it.
Intensified Privacy Concerns
As healthcare organizations adopt AI-assisted tools, data privacy concerns become more pronounced. Protected health information (PHI) is among the most sensitive data collected in any industry, with strict regulations promulgated within the Health Insurance Portability and Accountability Act (HIPAA) to protect it.
AI systems often require access to large volumes of PHI to function effectively, whether for diagnostics, workflow optimization, or predictive analytics. This increases the risk of unauthorized access, data breaches, and inadvertent disclosures. Unlike other industries, healthcare entities experience heightened exposure: HIPAA violations can result in substantial fines, reputational damage and regulatory scrutiny.
AI also introduces new complexities. For example, machine learning models may inadvertently retain or expose PHI during training or deployment. The integration of third-party AI vendors further complicates compliance, as covered entities must ensure that all partners adhere to HIPAA standards.
Staffing and Scope Creep
AI can help ease staffing pressures through automation and efficiency; however, it also risks blurring professional boundaries. The industry is already experiencing “scope creep,” in which responsibilities traditionally held by physicians are shifting to physician assistants (PAs) or restorative nurse assistants (RNAs) with the help of technology.
AI can improve access to care in meaningful ways and bridge the gap in resource-limited communities. However, AI can increase risk if staff members rely too heavily on AI for critical decisions or are not adequately trained to adopt new technology. Insufficient training, supervision and staffing can lead to overdependence on AI, potentially diminishing care quality and introducing new risks that demand careful mitigation.
Brokers and Underwriter Impact
The healthcare industry is becoming increasingly complex, driven by rapid technological advancements and the adoption of AI. In this evolving landscape, collaboration among brokers, wholesalers, and insurance companies is essential to address client needs and safeguard organizations against an expanding array of risks. That’s why it is more important than ever for wholesalers to partner with insurance companies that stay ahead of industry trends and emerging exposures. Backed by deep expertise, specialized capabilities and strong financial stability, Cardinal E&S is prepared to deliver the innovative solutions the healthcare market demands.
About Cardinal E&S
Cardinal E&SSM is a service mark owned by CNA Financial Corporation. CNA is one of the largest U.S. commercial property and casualty insurance companies. Backed by more than 125 years of experience, CNA provides a broad range of standard and specialized insurance products and services for businesses and professionals in the U.S., Canada and Europe. For more information, please visit CNA at www.cna.com.
About the Author
Jessica Landau is VP, Healthcare Wholesale at Cardinal E&S. To learn more, visit cardinalcna.com.