Providence's plan to make tech meaningful for clinicians

Providence is doubling down on its commitment to digital transformation and AI, with Chief Transformation Officer Sara Vaezy at the helm.

In a conversation with Becker's Hospital Review, Ms. Vaezy detailed how her role has evolved and how Renton, Wash.-based Providence is strategically leveraging AI to improve clinical workflows while maintaining a responsible approach to technology adoption.

Transitioning into a new role

Ms. Vaezy, formerly Providence's chief strategy and digital officer, stepped into her new role as chief transformation officer to spearhead the organization's efforts in digital, virtual care, marketing, consumer experience, and innovation. While her focus on these areas remains, the shift marks a more integrated approach to transformation.

"Implementing technology isn't just about implementing technology if you're really hoping to transform a system," Ms. Vaezy said. "It's about incorporating the right stakeholders, ensuring buy-in, and doing the necessary change management."

Under her leadership, Providence will target a few key areas each year for deep transformation efforts. The goal is to not only implement cutting-edge technology but also ensure it is fully integrated into the healthcare system, providing meaningful improvements for clinicians and patients alike.

AI for clinician experience

One of Providence's immediate priorities is improving the clinician experience, an area Ms. Vaezy acknowledges has been hindered by past technology implementations.

"Technology hasn't really worked to the benefit of clinicians since Meaningful Use came out," she said. "We think we can actually make technology work for our physicians, make their lives better, and reverse the trend."

While specific AI-driven projects were not disclosed, Ms. Vaezy emphasized that the focus for the remainder of the year—and likely into next year—will be on making AI a practical tool for healthcare providers rather than an added burden.

Responsible AI adoption

Providence has been engaged in AI work for several years, initially leveraging more traditional machine learning and predictive analytics. However, with the rise of generative AI and large language models, the organization has taken a structured approach to implementation, ensuring safety and effectiveness.

"When generative AI became widely available, we focused on a few key steps: establishing governance and guardrails, creating technical infrastructure to protect patient data, and prioritizing strategic use cases," Ms. Vaezy said.

Providence built a leadership group dedicated to generative AI, established a data ethics council, and implemented monitoring systems to track AI model performance over time. This multi-pronged approach aims to mitigate risks such as bias and model drift while maximizing the technology's potential benefits.

Cutting through the AI hype

With AI-enabled solutions flooding the healthcare market, health systems face the challenge of distinguishing meaningful innovations from overhyped technologies. For Providence, a rigorous, measured approach is key.

"You need to get into the details," Ms. Vaezy said. "What kind of AI is it? What is it specifically doing? What kind of APIs does it have? Evaluating these with technical acumen is critical."

Beyond technical assessment, Providence takes a strategic approach by concentrating resources on a few high-impact use cases rather than spreading efforts too thin.

"If you've got 100 different use cases that you're trying to work on all at once, you're never going to be able to get deep enough," Ms. Vaezy said. "We've been very intentional and measured in our approach."

The future of AI at Providence

Looking ahead, Providence aims to further refine its AI strategy and integrate it into broader operational transformations. Ms. Vaezy said she remains focused on ensuring that AI serves as an enabler of healthcare improvement rather than a disruptive force lacking direction.

"The models themselves will likely become commoditized," she said. "What's important is the data and how you operationalize AI in real-life healthcare settings."

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