For Alexander Khalessi, MD, chief innovation officer at UC San Diego Health, scaling AI in healthcare requires more than enthusiasm.
“When you have the ambition to build something without precedent, it’s critical to lean into the combined capabilities, expertise, and infrastructure that makes your institution special,” he said.
Dr. Khalessi was appointed chief innovation officer at UC San Diego Health in December. In this role, he oversees innovation strategy across the seven-campus health system and the academic health sciences enterprise.
In a conversation with Becker’s, he discussed the unique opportunity of a combined academic and health system leadership role to mobilize the ecosystem resources of a public research university and destination care delivery system.
Editor’s note: Responses have been lightly edited for clarity.
Question: You stepped into this enterprise innovation role in December. What are your top three priorities in this role?
Dr. Alexander Khalessi: On the health system side, I’m responsible for shaping innovation strategy across seven campuses, working with our spectacular faculty physician leadership, information services experts, and technology development teams to build intentional partnerships with commercial partners.
The goal is to pilot emerging technologies, rigorously assess performance and scale what delivers value. Our unique collaborative culture, academic health system environment, and domain subject matter experts position us to drive progress few can match.
Broadly, we are focused on three platform technology use cases.
First, clinical decision support technology that leverage the electronic health record and biomedical literature to inform our expert clinicians at the point of care.
Second, patient-facing technologies that improve access, service coordination, reduce system friction, and enhance the overall care experience.
Third, operational technologies, including revenue cycle and authorization workflows, to optimize patient access, acute care inpatient flow, and transfer system efficiency.
On the university side, there is a special opportunity to operationalize discovery and translational science within the health system. We’re building a learning health system that functions as a living laboratory. The core priority is integration, aligning discovery, validation, governance and deployment into a unified system.
Q: You’ve been asked to integrate new technologies, including AI, across the health system and academic enterprise. What are the first 2 to 3 AI use cases you’re prioritizing and why those first?
AK: UC San Diego has been home to several early advancements in this space.
Clinically, we have academic subject matter experts who have published extensively on early detection of sepsis to prevent downstream complications and support early clinical intervention. We’ve also developed decision models to evaluate imaging patterns in certain forms of cancer and to assess solitary pulmonary nodules on chest x-rays.
On the patient-facing side, we’ve partnered with third-party vendors to develop AI-enabled agents that help patients prepare for elective colonoscopy – ensuring readiness for preventative procedures and reducing avoidable delays.
Operationally, we’re leveraging AI to enhance revenue cycle performance and ensure patients have regular access to our providers without friction around preauthorization and approval.
Our technology adoption approach typically involves three distinct phases:
First, we structure defined pilots to frame the problem, develop clear and transparent outcome metrics, and assess the scale of patient benefit. We plan for success.
Second, we develop formal governance pathways to transition validated pilot tools into enterprise operations with an assessment of executing teams and structural budget.
Third, longitudinal performance monitoring is key to detect drift and ensure sustained value.
Many AI models are trained on defined datasets. Responsible implementation means not simply deploying a tool, but continuously reassessing its performance, equity and impact over time.
There’s a lot of enthusiasm around AI right now. But few industries carry stakes as high as healthcare. Getting it right requires rigor, governance and accountability from the outset.
Q: What excites you most about stepping into this enterprise role?
AK: I’ve been at UC San Diego since 2011 and continue to serve as chair of neurosurgery. As an academic surgeon, I was grateful for this enterprise role because I can’t imagine a better place to help define the future of healthcare.
Defining that future means contemplating five distinct care environments.
The first domain focuses on health span and prevention — leveraging wearables, nutrition, exercise, and sleep science.
Second, establishing personalized baseline data through genetics and wearables will facilitate early disease detection, prevention, and timely intervention.
The core of health care delivery will remain complex acute care for serious illness.
In the post-acute care environment, we will have powerful tools to ensure treatment success, assess functional outcomes, inform patient selection, and refine complication avoidance.
Finally, we will coordinate, personalize, and enhance chronic disease management.
We’re thinking intentionally about how to bring those dimensions together in a patient-centered way, mobilizing our exceptional population health teams, our data-enabled mission control, and co-developing technology solutions with industry partners.
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.