Introduction
Artificial intelligence is transforming healthcare, but its effectiveness hinges entirely on the quality of underlying data systems. Healthcare leaders across the country are collaborating with their teams to establish comprehensive infrastructure, robust governance protocols, and organizational cultures that enable AI deployment at enterprise scale.
During the AI Summit at the Becker’s CEO+CFO Roundtable in early November, healthcare executives convened to address critical challenges facing AI adoption. The discussions centered on essential topics including high-quality data preparation, strategic alignment between technical and clinical teams, and implementing cautious approaches to security and interoperability concerns.
The Foundation: Clean and Structured Data
Data Readiness Drives AI Success
Healthcare organizations cannot expect meaningful outcomes from AI initiatives without first establishing fundamental data readiness. Rajiv Kolagani, chief data and AI officer at Ann & Robert H. Lurie Children’s Hospital of Chicago, emphasizes that data preparation represents the cornerstone of any successful AI strategy.
“We can’t really get any utility or value out of AI without data,” Kolagani explained. “When you think about AI, you have to really think about preparing data for AI differently.”
Modernization Through Cloud Infrastructure
Lurie Children’s Hospital invested three years in a comprehensive data modernization initiative, transitioning to a modern cloud-based data platform incorporating technologies like Snowflake and Fabric. This infrastructure transformation became essential groundwork for all subsequent AI implementations.
The hospital now operates using a medallion architecture featuring bronze, silver, and gold data layers, ensuring trustworthy and reliable data sources throughout the organization. Building upon this foundation, the technical team develops knowledge graphs that effectively teach AI systems the meaning and context behind healthcare data, enabling semantic understanding of complex medical concepts including infections, diagnoses, and treatment protocols.
“You have to teach AI all of the context,” Kolagani noted, highlighting the complexity of preparing healthcare data for AI consumption.
Implementing Data Quality Standards
Intentional Quality Management
Data quality cannot emerge organically—it demands deliberate structure, dedicated resources, and clear accountability mechanisms throughout every stage of a health system’s data ecosystem. Kolagani stressed that quality management must be integrated from the beginning, not treated as an afterthought in the development process.
“In order to make that happen, you have to have different tooling in your infrastructure,” he explained.
Tracking Data Lineage and Errors
Lurie Children’s Hospital implemented comprehensive data catalogs and lineage tools that trace data from its origin point, through processing stages, and identify errors that occur throughout the pipeline. This systematic approach ensures transparency and enables rapid identification of quality issues.
“There’s a ton of work that actually goes into making sure that you have high-quality data output,” Kolagani said. “Quality doesn’t happen by accident. You have to be super intentional about it, and you also have to have the right guy or gal running the data shop.”
Addressing Healthcare’s Usability Challenge
Data Rich but Information Poor
Margaret Lozovatsky, MD, CMIO and vice president of digital health innovation at the American Medical Association, identified a fundamental paradox in healthcare technology: organizations possess vast amounts of data yet struggle to extract actionable information.
“Anybody that has ever used an EHR to deliver care to their patients knows that there’s a lot of stuff in there that is not accurate and shouldn’t be in there,” Dr. Lozovatsky observed.
Overcoming Interoperability Barriers
The root cause of healthcare’s information problem lies in structure and interoperability challenges. Much of the critical information stored in electronic health records exists in narrative formats that resist standardization and automated analysis.
“We have struggled for years with interoperability, because every organization has this data in their own format,” she explained.
Large language models offer promising solutions to these longstanding barriers, providing capabilities to process and translate disparate data formats into usable clinical insights. “These tools can unlock the data and translate it into useful information that clinicians can use to make decisions,” Dr. Lozovatsky said.
The AMA approaches AI through the lens of augmented intelligence, recognizing that while computers excel at processing vast amounts of information, human expertise remains essential for understanding context and making informed decisions.
Governance and Security Frameworks
Balancing Innovation with Protection
Healthcare organizations must protect sensitive patient data while simultaneously fostering innovation through AI adoption. This balance requires establishing strong governance frameworks that enable progress without compromising security.
“We need to go slow to go fast,” Dr. Lozovatsky emphasized. “Setting up these processes today to understand how your organizational data is being used in testing these tools and implementing these tools to ensure that at the end of the day, the patient information is not identifiable is really important.”
Implementing Best Practices
The AMA developed an AI Governance Toolkit featuring an eight-step process designed to help healthcare organizations balance privacy requirements with innovation goals. “It’s an eight-step process that is some of the best practices for organizations to think about as you’re setting up your governance within your institution,” Dr. Lozovatsky explained.
The Future: Administrative Efficiency and Connected Care
Starting with Administrative Automation
Healthcare organizations will likely achieve their earliest AI successes through administrative automation rather than direct clinical interventions. Dr. Lozovatsky predicted that initial implementations will focus on reducing administrative burdens, particularly in areas like patient access, scheduling, and documentation.
“We’re going to start to tackle all of the administrative burdens first,” she said. “That’s the next iteration of these tools.”
Building Connected Healthcare Ecosystems
Anil Saldanha, chief innovation officer at Rush University System for Health in Chicago, views AI as the catalyst for systemic transformation that healthcare desperately needs. He envisions a future where connected, data-driven care operates seamlessly across the entire healthcare continuum.
“We can’t just look at health systems in isolation. We live in a connected world,” Saldanha said. “I’m really bullish on diagnostics, machine learning, AI, cancer detection, stroke triage. We’re building the plane as we fly, but I’m really bullish on the future of medicine.”
Conclusion
Healthcare’s AI transformation depends on establishing strong data foundations, implementing quality standards, and developing governance frameworks that protect patients while enabling innovation. Organizations that invest in these fundamentals today will be positioned to deliver better care tomorrow.
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