Children’s National’s Chief Data & AI Officer Says Foundational Data Governance Work Key to Deriving Meaningful Insights

Tapping into advanced analytics and automation, the pediatric hospital focuses on outcome-first AI design

When Alda Mizaku assumed the role of Chief Data and AI Officer at Children’s National Hospital, the position did not yet exist. With a background in biomedical engineering and predictive modeling, she brought both technical and clinical perspectives to what would become a transformative role. Her first priority: establishing the data foundation necessary to drive analytics and AI across the organization.

Children’s National, ranked among the top five pediatric hospitals in the United States, serves the Washington, D.C., Maryland, and Virginia regions and attracts patients from around the world. In an environment defined by both complexity and scale, Mizaku’s mission has been to develop enterprise-wide capabilities while demonstrating immediate value. That required rethinking traditional sequential approaches.

“We really had to build the plane while flying it,” she said. “It’s not how we’re trained, but it doesn’t work in healthcare to wait two years to build the platform before showing results. We had to bring value quickly while establishing the infrastructure underneath.”

This approach included launching a cloud-based data platform and defining a centralized enterprise data model—a single source of truth to support analytics, automation, and AI initiatives.



AI as a Means, Not an End

While excitement around generative AI and autonomous systems grows, Mizaku emphasized the importance of beginning with problems—not technology. She described AI as a toolbox containing multiple instruments, from predictive models to generative agents to automation workflows. Selecting the right tool, however, requires clear alignment with operational goals.

“Technology for technology’s sake doesn’t accomplish much, particularly in healthcare,” she said. “We have to start with the problem, understand the clinical and operational context, and then determine the right tools to support it.”

This mindset also helps temper the appeal of the so-called “shiny new toy.” Mizaku noted that while breakthrough technologies such as GenAI demand exploration, feasibility and value assessments are essential. Even high-impact ideas must be weighed against resource constraints, with questions of scalability, safety, and implementation cost playing a central role.

Data itself becomes a bridge between theory and practice. Understanding how workflows truly function—through both direct observation and analysis of real-world data—enables leaders to identify variations, bottlenecks, and inefficiencies that might otherwise remain hidden. That intelligence, in turn, guides not only which technologies to adopt but also how to apply them effectively.

The Paradox of Change

Implementing AI at scale requires more than good technology. It requires trust, governance, and cultural buy-in. According to Mizaku, a successful strategy for change relies on two equally important components: engaging decision-makers directly in the design process and leveraging early adopters to build momentum.

“You want to do it together,” she said. “Bring people along from the start, get their fingerprints on the solution, and let them help shape it. That way, it becomes theirs.”

But for every enthusiastic partner, there may be skeptics. Mizaku’s approach to scaling innovation focuses first on finding champions who are eager to lead. Once a project proves successful, its results serve as a proof point that encourages adoption elsewhere in the organization—often in areas that initially resisted change.

Another nuance of the role involves balancing competing forces. On one hand, Mizaku’s team is tasked with enabling broad, safe use of AI technologies. On the other, they must manage a growing list of requests from advanced users eager to pilot new and often unvetted tools.