Dan Liljenquist: How He Is Transforming Healthcare with AI

Artificial intelligence is rapidly changing healthcare, and Dan Liljenquist is among the healthcare executives helping organizations turn AI from an emerging technology into practical tools. As chief strategy officer at Intermountain Health, Liljenquist has been closely involved in the organization’s broader strategy, innovation, and transformation efforts.

Intermountain has been developing hundreds of AI initiatives aimed at reducing administrative work, improving efficiency, supporting caregivers, and making healthcare more accessible. Liljenquist has described AI as an important opportunity to simplify healthcare work while helping organizations respond to workforce shortages and rising demand.

Who Is Dan Liljenquist?

Dan Liljenquist is the chief strategy officer at Intermountain Health. He joined the organization in 2012 and has worked across strategy, healthcare transformation, and organizational initiatives.

Before moving into healthcare leadership, Liljenquist served in the Utah State Senate, where he worked on public-policy issues including Medicaid and pension reform. He later became involved in healthcare strategy and transformation at Intermountain.

In 2018, Intermountain named Liljenquist senior vice president and chief strategy officer, adding him to the organization’s executive leadership team.

His role gives him a broad view of how technology, business strategy, healthcare delivery, and operational improvements can work together.

Dan Liljenquist’s Vision for AI in Healthcare

Liljenquist’s approach to AI is largely focused on solving practical problems rather than adopting technology simply because it is new.

Intermountain has been working on roughly 300 AI projects, according to Liljenquist. These initiatives are designed to help simplify healthcare operations, address workforce pressures, and give clinicians and employees more time to focus on higher-value activities.

This strategy reflects a broader shift in healthcare. AI is increasingly being used for administrative and operational tasks where large amounts of information must be processed quickly.

Intermountain AI Appeal Agents

One of the clearest examples of Intermountain’s AI strategy involves insurance appeal letters.

Preparing an appeal after an insurance denial can require employees to search through medical records and identify information supporting the claim. Liljenquist has explained that Intermountain is using AI to help search through medical-record information and gather the relevant details needed to prepare an appeal.

The reported benefit is significant: the AI-assisted process can save approximately 30 minutes per appeal letter.

The example demonstrates how AI can be useful even when it is not making clinical decisions.

Instead, the technology handles repetitive information-retrieval and drafting work while people remain involved in reviewing and managing the process.

Why AI Appeal Agents Matter

Insurance appeals are just one example of a much larger healthcare challenge.

Healthcare organizations process enormous amounts of information every day. Employees may spend considerable time searching records, organizing information, completing documentation, and handling repetitive administrative tasks.

AI agents can potentially reduce some of this workload.

A simplified process looks like this:

Insurance denial → AI searches relevant records → Information is organized → Appeal is prepared → Human review

This approach allows employees to spend less time on repetitive information gathering and more time on work that requires human judgment.

Importantly, publicly available information does not establish that Intermountain’s AI system independently makes medical-necessity decisions. The reported use is focused on helping prepare appeal documentation.

AI and Healthcare Workforce Challenges

Workforce shortages and clinician burnout are major concerns across healthcare.

Liljenquist has argued that AI can provide new tools for simplifying the work required to care for patients. Intermountain’s strategy therefore connects AI adoption with a broader effort to make healthcare operations more efficient.

This is particularly important as healthcare organizations face increasing demand while trying to control costs.

Instead of viewing AI only as a replacement technology, Intermountain’s approach emphasizes using AI to support employees and remove unnecessary administrative burdens.

Intermountain’s Broader AI Strategy

The appeal-letter project is only one part of Intermountain’s AI strategy.

The organization has been pursuing AI applications across different areas of healthcare operations. In 2026, Intermountain leaders have continued discussing AI agents, interoperability, data standardization, and clinical AI as important components of healthcare transformation.

Intermountain’s AI efforts also build on an existing history of using data and technology in healthcare.

The organization established a Data Science and Artificial Intelligence Center of Excellence to promote responsible AI development, including attention to privacy, fairness, validation, data integrity, and governance.

AI Is Not Just About Replacing Workers

One of the most important aspects of Liljenquist’s approach is the emphasis on augmentation rather than simple replacement.

Healthcare requires human interaction, professional judgment, empathy, and accountability. AI can process information quickly, but healthcare workers remain responsible for many decisions and patient-facing activities.

By automating repetitive tasks, AI can potentially give employees more time for activities that require human expertise.

This distinction is particularly important in healthcare, where poorly designed automation could create new risks instead of solving existing problems.

The Importance of Data

AI systems are only as useful as the information available to them.

At the 2026 HIMSS Global Health Conference & Exhibition, Liljenquist emphasized the strategic importance of data and interoperability as healthcare organizations expand their use of AI.

Standardized and accessible data can make it easier for AI systems to retrieve relevant information and produce useful results.

For organizations such as Intermountain, this means AI strategy cannot be separated from data strategy.

Responsible AI in Healthcare

Healthcare AI also requires strong safeguards.

Intermountain has previously outlined principles for responsible AI, including transparency, equity, privacy, validation, documentation, bias detection, and data integrity.

These principles become increasingly important as AI moves from experimentation into everyday workflows.

The goal is not simply to deploy more AI. Healthcare organizations need to ensure that these systems are accurate, secure, useful, and appropriately supervised.

What Dan Liljenquist’s AI Strategy Means for the Future

Dan Liljenquist’s work at Intermountain illustrates a broader trend in healthcare: AI is increasingly being applied to everyday operational problems.

The insurance-appeal example is particularly notable because the benefit can be measured in time saved per transaction. When a healthcare system handles thousands of similar administrative processes, even modest efficiency improvements can become significant at scale.

Intermountain’s hundreds of AI initiatives also suggest that healthcare AI is moving beyond isolated experiments toward broader organizational adoption.

Final Thoughts

Dan Liljenquist’s approach to AI highlights a practical direction for the future of healthcare.

Rather than focusing exclusively on futuristic clinical applications, Intermountain Health is using AI to address immediate operational challenges. From insurance appeal preparation to broader administrative and data-driven workflows, these applications demonstrate how AI can help reduce repetitive work.

The reported 30-minute time saving on individual appeal letters shows why these seemingly simple applications can matter. At scale, small improvements can translate into substantial operational benefits.

As Intermountain continues expanding its AI initiatives, Liljenquist’s strategy offers an example of how healthcare organizations can combine technology, data, and human expertise to create a more efficient and patient-centered system.