Is Your Health System Ready for Enterprise AI?

While AI point solutions have been successful, they often haven’t accounted for one of the most crucial aspects of lasting AI success: scalability.

In order to thrive at the enterprise-wide level, you must account for four key components of AI strategy:

• Choose: The “why” of AI. Why should you implement AI and what strategic opportunities can it unlock?
• Integrate: The “how” of AI. How will you integrate AI to overcome infrastructure challenges and ensure it evolves with patient care needs?
• Adopt: The “who” of AI.? Who will drive AI adoption? Who needs to collaborate across your organization for large-scale technology shifts?
• Govern: The “what” of AI oversight. What governance structures will you put in place to manage AI effectively and ensure compliance, security and ethical use?

Assessing organizational readiness within each is critical before moving forward with implementation.

This Clinical AI Readiness Assessment is a resource for health system introspection, posing questions that help determine if your organization is prepared for the challenges and opportunities of enterprise AI.

Stop The Healthcare Scavenger Hunt

AI is the ultimate tool to help streamline healthcare’s complex, fragmented processes. Aidoc CEO Elad Walach highlights how AI can integrate disparate data sources, enhance clinical workflows and improve outcomes by providing real-time, actionable insights. This transformation turns the traditional, inefficient data scavenger hunt into a cohesive, efficient care delivery system.

Time Is Brain: Ochsner Health Door-To-Puncture Highlights

Ochsner Health achieved a 34% reduction in door-to-puncture time and a 24% reduction in door-to-CT time by leveraging AI-driven workflow optimization. These improvements are equivalent to saving 72.2 million neurons per patient.

A “TsunamAI” of Change

A recent Forbes article highlights AI’s transformative impact on healthcare across administration, R&D and care delivery. Aidoc’s cardiovascular solutions are mentioned, exemplifying how AI can streamline data consolidation and enhance diagnostics.