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Roy Rosin, the former Chief Innovation Officer at Penn Medicine and current Board Partner at VC firm First Round Capital, joined CIO News to discuss outdated industry habits blocking AI adoption in healthcare.
Rosin said most challenges reside in workflow, implementation, and change management, not the capabilities of AI technology.
Achieving high accuracy in healthcare AI requires solving numerous edge cases and rigorous orchestration and governance.
The market is shifting from buying technology features to purchasing outcomes, emphasizing financial results over raw tech.
The healthcare industry at large is banking on intelligent automation to tame spiraling costs and inefficiencies of administrative work. Successful implementation will free up providers and streamline the labyrinth of scheduling, billing, and compliance processing. But a troubling challenge has emerged. Recent benchmarks suggest that large language models often perform worst on these very administrative and workflow tasks.
This disconnect raises a critical question: Is the technology subpar, or is the industry’s framework for implementation simply outdated?
For an answer, we spoke with the former Chief Innovation Officer at Penn Medicine's Center for Health Care Transformation and Innovation, and current Board Partner of First Round Capital Roy Rosin. He has seen this pattern before, and is confident the issue isn't a failure of AI, but a lapse in imagination rooted in long-standing industry habits and a lack of vendor alignment.
A history of suboptimal thinking: "Healthcare is littered with a history of suboptimal thinking about technology where people think they're done when they've implemented and deployed," said Rosin. True technological maturity, he argued, has evolved from being "done when implemented," to being "done when you achieve an outcome." But in the AI era, even that isn't enough. The new reality requires a state of continuous monitoring, learning, and refinement at enormous scale.
The key is proper orchestration within tightly tailored and governed systems. According to Rosin, the highest rates of accuracy are made possible by an "interplay of systems," including agentic state machines and RAG models designed to eliminate hallucinations and ensure outputs are correctly sourced. But trust is maintained through relentless governance. This means running hundreds of thousands of test cases and even deploying agents to stress-test the system to ensure the technology performs exactly as promised.
Infinite edge cases: That process of refinement becomes exponentially harder when dealing with the sheer complexity of real-world healthcare. Rosin pointed to First Round portfolio company Assort Health, which had to solve for an astonishing 900,000 edge cases to achieve high accuracy in its patient scheduling and communication automations. That colossal number wasn't pulled from a public dataset; it was painstakingly compiled by going practice to practice, specialty by specialty—from dermatology to neurology to gastroenterology—each with its own maze of unique workflows.
Building moats through rigor: The key, Rosin said, is old-fashioned rigor. "You uncover the edge cases by sitting shoulder to shoulder with the experts. You have to work through not only the way things are supposed to go, but also what happened when things didn't work the way they were supposed to." The externalities include massive proprietary data assets and a powerful first-mover advantage, turning technical challenges into competitive moats.
The force that drives rigor isn't just better engineering. It requires a fundamental shift in the market. The historical problem, Rosin diagnosed, was "information asymmetry," where non-technical healthcare leaders were burned by over-promising vendors. That era is ending.
Features ≠ outcomes: "I'm not buying features, and I'm not buying functionality. I'm buying outcomes," Rosin said. Clients are no longer buying raw technology; they're buying the tangible financial outcome it produces, such as the guaranteed "clawback protection" offered by Brellium due to their ability to automatically review 100% of patient charts for all payor and quality requirements.
Whereas traditional SaaS pricing structures with non-consumption based pay-per-seat models marked the previous era, perhaps the coming of AI will produce a new wave of truly incentive-aligned technology that guarantees ROI. "More and more technology companies are now dealing with years of skepticism by saying, 'We're here for the outcome, not for the cool tech.' So this is a phenomenal evolution," said Rosin. "When you simply cannot achieve an outcome without accuracy and refining workflows to delight the care team and administrative users, everyone becomes aligned, and it becomes about more than just making the sale."
