Thursday, September 10, 2026
12:00-1:30 PM ET 
ACHE has approved this event for 1.5 Virtual Interactive Credits!

Registration to open shortly!

Panel Purpose

The panel should help attendees identify the practical AI habits healthcare employees can begin building now, the manager behaviors that make adoption safer and more equitable, and the leadership choices that strengthen workforce resilience, adaptability, and readiness for an AI-enabled future while protecting quality, safety, privacy, and trust.

Core Discussion Themes

• AI literacy in healthcare: Understanding where AI is already appearing in clinical, operational, administrative, and patient-facing workflows—and what employees need to know to use it responsibly.
• Practical AI habits: Building repeatable behaviors such as asking better questions, giving clear context, reviewing outputs carefully, and knowing when to verify or escalate.
• Clinical and operational judgment: Treating AI outputs as support—not final answers—and knowing when human review, escalation, or subject matter expertise is required.
• Responsible use: Protecting patient privacy, avoiding inappropriate data sharing, checking for bias, and following organizational policies for approved tools.
• Manager support: Creating space for safe experimentation, clarifying guardrails, sharing use cases, coaching teams, and helping staff translate AI curiosity into resilient work practices.
• Workforce resilience and readiness: Strengthening durable skills—adaptability, data comfort, critical thinking, communication, and change leadership—that help healthcare employees remain effective as AI tools evolve.

Takeaways for Attendees

• AI fluency is becoming a core competency for healthcare employees and leaders.
• Resilient healthcare employees will combine AI curiosity with repeatable habits, critical thinking, privacy awareness, clinical or operational expertise, and communication skills.
• Managers can accelerate safe adoption by setting guardrails, identifying appropriate use cases, coaching teams, and normalizing careful review of AI outputs.
• Healthcare organizations should prioritize AI use cases that improve quality, safety, access, workforce experience, and patient-centered value.