AI in Healthcare UX: From Burden to Breakthrough

As McKesson advances its three-year AI transformation, the focus across oncology and multispecialty care is on exploring how AI can genuinely improve clinician productivity and patient outcomes, while understanding where it must be constrained.
Anya Gerasimchuk shares how her team leverages AI to reduce administrative burden, protect trust through strong UX guardrails, and operate within the data and compliance constraints of high-risk healthcare environments.

Key Outcomes

• Identify high-value, low-risk entry points for AI in oncology and EHR workflows that truly reduce clinicians’ administrative burden.
• Design AI-assisted experiences that preserve context, transparency, and trust for care teams using UX guardrails and explainability patterns.
• Use AI to accelerate the product development lifecycle, from synthesizing user research into design specifications to driving actionable insights.
• Explore the constraints that healthcare environments impose on successful AI implementation, such as the availability of structured data, a unique challenge in its own right.