AI Implementation in Healthcare: Early Lessons from Rush University Health System

In the latest episode of the Leader to Leader podcast, Jeff Gautney , Senior Vice President & Chief Information Officer at Rush University System for Health, offered candid insights into the realities of implementing AI in healthcare. His perspective, shaped by three and a half years leading IT strategy at a $3.5 billion academic health system, reveals both the promise and practical challenges of healthcare AI adoption.

The Reality Check on AI ROI

While AI continues to dominate headlines and strategic planning sessions, Gautney's experience offers a reality check on immediate returns. "There's no doubt that ambient listening is saving our doctors time," he notes. "However, across a thousand doctors, it's saving three minutes a day, and so it doesn't really translate into another visit."

This modest time savings, while beneficial, has prompted Rush to reassess how they evaluate AI investments. The challenge isn't proving value – it's quantifying it in traditional ROI terms. As Gautney explained to episode host Mike Robin , "We can see the benefit, but I can't point to something in the budget that's going to go away because of it. And I can't point to something in our revenue growth plan that's directly attributable to the implementation."

Strategic Necessity vs. Immediate Returns

Despite these ROI challenges, Rush continues to invest in AI technologies, viewing them as strategic necessities rather than pure cost-saving measures. Gautney points to the competitive healthcare landscape in Chicago, where Rush competes with giants like Advocate Aurora and Northwestern. "Not having ambient listening will be a significant disadvantage to attracting and retaining clinicians going forward," he argues. "It's going to be an expectation."

A Four-Bucket Approach to AI Implementation

Rush has developed a structured approach to AI implementation, organized around four key areas:

1. Business Priority Incubator: Focuses on identifying, prioritizing, and promoting AI initiatives aligned with business goals
2. Governance and Safety: An "office of responsible AI" that includes compliance, privacy, cyber, legal, and HR considerations
3. Data Management: Ensures data quality and accessibility to support AI models
4. Tools and Development: Standardizes the technical environment and testing procedures across AI initiatives

This framework has proved valuable not just for generative AI, but across all AI applications, from medical imaging to robotic process automation.

Change Management: The New Core Competency

Perhaps the most significant shift in IT leadership has been the elevation of change management from an "appendage" to a core competency. "The decisions that have to be made around the software are fewer and fewer," Gautney observes, "and the work that has to be done to change people's jobs, change process, bring people along, identify when people are checking out – that work has grown exponentially."

This evolution has led Rush to hire senior-level change management expertise and develop it as a core competency within their IT team.

Digital Transformation: Three Pillars of Progress

Rush's digital strategy rests on three main pillars:

1. Consumer Experience: Focusing on access and care delivery models, including virtual care options and streamlined engagement
2. Care Transformation: Applying digital technology to transform care delivery through tools like virtual nursing and AI-assisted diagnostics
3. Operational Efficiency: Leveraging automation and optimization across existing platforms to maximize value

Looking Ahead: The Integration Imperative

Rather than creating separate digital or AI departments, Rush has chosen to integrate these capabilities across their entire IT organization. "Every single person in our organization owns part of this," Gautney emphasizes. This approach ensures that innovation isn't siloed and that career development opportunities are available to all team members.

Key Takeaways for Healthcare IT Executives

1. Realistic AI Expectations: While AI delivers value, immediate ROI may be more nuanced than traditional metrics suggest
2. Strategic Implementation: Success requires a structured approach that balances innovation with governance and practical considerations
3. Change Management Priority: Technical expertise must be matched with change management capabilities
4. Integration Over Isolation: Digital transformation and AI capabilities should be integrated across the organization rather than siloed
5. Competitive Necessity: Some AI investments may be justified by market expectations rather than immediate financial returns

As healthcare organizations continue to navigate the AI landscape, Rush's experience offers valuable insights into the practical challenges and strategic considerations of implementation. While the immediate returns might not always match the hype, the strategic imperative for AI adoption remains clear – particularly in competitive healthcare markets where technology capabilities increasingly influence both patient choice and clinical talent retention.

Catch up on past episodes of Leader to Leader here: https://www.healthcareitleaders.com/podcasts/