Why Cedars-Sinai named its 1st chief AI officer

Los Angeles-based Cedars-Sinai appointed its inaugural chief data and artificial intelligence officer in December to boost its systemwide AI strategy.

Mouneer Odeh, the former vice president of analytics at Fairfax, Va.-based Inova Health System, told Becker's he accepted the position to use technology to strengthen healthcare overall. Here is the interview, edited for clarity and brevity.

Q: Why did you decide to take on the role of chief data and AI officer at Cedars-Sinai?

Mouneer Odeh: We are at an inflection point in healthcare. The growing aging population creates increased demand for healthcare services, while cost pressures make it harder to achieve operational efficiencies. At the same time, we're seeing fewer individuals entering the workforce as clinicians. This creates tremendous pressure to think differently about how we address these forces in the future, and innovation is a key part of that.

The advancements in AI and analytics are unprecedented. The cost of computing has decreased, data availability is at an all-time high, and AI has transformed how we interact with that data. Over the next decade, I believe we'll see significant changes in healthcare. While progress in the short term may seem slow, the long-term impact will likely exceed what we can imagine today.

On a personal note, I've spent the last 10 years in healthcare on the provider side with a goal of using data-driven intelligence to drive faster, better decisions and improve outcomes. Over time, I've seen data have a tremendous impact, and with advancements in AI, I believe we'll have 10 times the impact over the next 10 years.

Cedars-Sinai is a very mission driven culture, and it embraces progress. Our ability to bring the future forward faster, here at Cedars-Sinai, is somewhat unprecedented. I haven't come across another health system that is as well-equipped and as primed, from a cultural perspective, to take advantage of the transformational changes that are going on with AI and advanced analytics.

Q: What is Cedars-Sinai doing well in AI already?

MO: Cedars-Sinai is a leader in AI with a strong computational biomedicine program spanning education and research. The organization fosters collaboration across multidisciplinary teams, including operational staff, informaticists, researchers, and advanced analytics professionals. The collaborative culture enables innovation and a sustained effort to refine and implement solutions.

What's particularly impressive is how Cedars-Sinai focuses on validation and effective implementation. Technology is no longer the biggest hurdle in AI — that's getting easier and cheaper — it's ensuring that solutions are implemented in ways that work effectively for people as intended.

Q: What data or AI project from your career are you most proud of?

MO: It's hard to point to just one project because I've been involved in hundreds — maybe over a thousand — over my career. What I'm most proud of is fostering a data-driven culture. Creating impact at scale isn't about one or two projects; it's about building an environment where people have easy, ready access to data and feel confident using it. This involves not only providing the tools but also promoting data literacy and fostering a culture of decision-making driven by data.

One notable accomplishment was leading a team at Jefferson Health in the CMS AI Health Outcomes Challenge. Competing against heavyweights in the field, our team successfully advanced to the finals, showcasing our ability to build innovative, impactful solutions.

Also, using data to drive decisions during the COVID-19 crisis and during the IV fluid shortage caused by hurricanes. Empowering teams with the tools to respond quickly and effectively in times of crisis was incredibly rewarding.

Q: What is the biggest challenge for healthcare AI?

MO: There are two key challenges:

Implementation: The hardest part of AI isn't developing models — it's implementing them effectively. It requires understanding who needs specific information, when they need it, what actions they should take, and how to monitor the impact of those actions. Success depends on careful planning and validation to ensure models are effective, fair and equitable.

AI literacy: Many people need more education and awareness to understand and trust AI tools. Transparency and building trust are critical for adoption. Over time, democratizing access to these tools will allow individuals to develop and adapt AI capabilities, but we must make sure it's done safely and reliably.

Q: What do you hope to accomplish in the new role? Any AI projects planned already?

MO: One area I'm particularly excited about is leveraging large language models to mine unstructured data, such as clinical notes. Historically, extracting meaningful insights from unstructured data has been challenging and labor-intensive. LLMs offer a scalable and efficient way to extract medical concepts, which could significantly improve clinical quality and patient safety.

For example, when there are infections in hospital settings, case reviews are triggered. These reviews often involve mining unstructured data, which can be time-consuming.

By using LLMs to streamline this process — and other clinical quality reporting requirements — we can save thousands — potentially tens of thousands — of hours and reinvest that time in improving patient care.

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