Most healthcare budget conversations are organized around the fiscal year. However, Ebrahim Barkoudah, MD, is prioritizing investments at UT Southwestern Medical Center more organized around the next decade.
Dr. Barkoudah, the inaugural chief of the division of hospital medicine at the Dallas-based academic health system, named two essential priorities, knowing they won’t pay off on a standard budget timeline:
1. Building a pipeline for advanced practice providers; and
2. Deepening the organization’s clinical informatics capacity through fellowships and partnerships.
“Both have payback window longer than typical budget cycle,” he said during an interview with the “Becker’s Healthcare Podcast.” “So that would have way more investment because we need these dashboards to know how we could do on our operation, how we can also predict, use that AI to tell us how we can budget better, how we can staff better in the winter.”
The rationale is rooted in the volume projections shaping hospital medicine nationally. Sg2 data shows adult inpatient discharges growing 10% through 2035, with projections reaching 176 million inpatient days nationally. Managing that surge well requires both the clinical personnel to staff it — hence the APP pipeline — and the data infrastructure to anticipate it. Without predictive modeling on staffing, seasonal demand and turnover, health systems react perpetually to volume they could have seen coming.
Labor pressure sits at the center of Dr. Barkoudah’s near-term concerns. Premium staffing costs — travel and contingency labor — remain an expense health systems have struggled to control since the pandemic. But the right answer isn’t to manage the expense in isolation, but to build the permanent workforce and analytical infrastructure to reduce dependence on it.
AI enters this framework as the layer that makes human investment more productive. Ambient documentation, predictive staffing models and agentic AI tools reduce the administrative surface area that has driven burnout and attrition. The goal is not to replace clinicians with algorithms but to free clinicians to do work algorithms cannot.
“I’m loving the AI,” Dr. Barkoudah said. “I still think you have to leverage lean process improvement type models. But once you do that, you can start looking at how AI can come in and help standardize and take away some of the monotonous, boring work that is just data-driven stuff. Now you can shift the work of the person to more people-centered things — discussions with people, communicating with people, talking to candidates.”
The clinical informatics fellowship, in his framing, is the bridge between the operational AI layer and the physician workforce. A clinician who understands informatics can do more than read a dashboard — they can identify where it is misleading, design the feedback loops that make predictive models reliable over time and translate system-generated insights into decisions at the bedside.
“We need to work harder and smarter,” he said. “In the long haul is investment in our labor to make sure the AI — in predictive, generative or now agentic, and all the tools we’re seeing — will help us to make sure we get better care delivery.”
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