Healthcare AI discussions often focus on futuristic diagnostics, robotic surgery, or breakthrough drug discovery. Kam Thindal, Managing Director of Core Capital Partners, believes the more immediate disruption will happen somewhere far less glamorous: administration.
Thindal argues that healthcare still depends on deeply inefficient systems. Clinicians spend hours on documentation, prior authorization requests, billing codes, and fragmented patient records. For him, the biggest near-term AI opportunity is reducing that operational friction rather than replacing physicians.
“Healthcare is a regulated service business, delivered by humans, constrained by capacity, and shaped by incentives that often reward volume over outcomes,” Thindal says.
He points to three forces driving the current wave of AI adoption: labor shortages, the growth of digital health data, and advances in language-based AI systems capable of handling unstructured information like clinical notes and forms.
According to Thindal, healthcare now produces enormous volumes of data through electronic health records, imaging systems, lab reports, claims databases, and wearable devices. Yet clinicians often struggle to access useful information quickly during patient care.
“The paradox is that healthcare has more data than ever, yet clinicians often have less usable information in the moments that count,” he explains.
Rather than focusing first on headline-grabbing breakthroughs, Thindal expects AI adoption to emerge through workflow improvements that save time and reduce repetitive tasks. He sees potential in systems that summarize charts, automate documentation, pre-fill coding fields, or draft appeals for denied insurance claims.
“These are not glamorous problems, but they determine whether the system flows or jams,” he says.
For investors, he believes the challenge is distinguishing between impressive demonstrations and products that can survive inside real healthcare environments. Hospitals and clinics cannot adopt technology casually because errors carry legal, regulatory, and patient safety consequences.
“Healthcare does not adopt technology the way consumer markets do,” Thindal says. “AI will be judged on reliability, not novelty.”
That means integration matters as much as the AI itself. Products that require clinicians to leave their existing systems or manually transfer outputs are unlikely to gain traction. Instead, Thindal expects successful tools to function quietly inside existing workflows such as electronic health record systems and claims management pipelines.
He also warns that healthcare incentives remain fragmented. Providers, insurers, regulators, employers, and patients often benefit differently from the same technology, slowing adoption even when the software works effectively.
For now, Thindal remains focused on companies solving operational bottlenecks with measurable financial returns.
“Right now, I’m more interested in the companies solving the boring problems,” he says. “These aren’t sexy, but they have clear ROI, quantifiable time savings, and alignment across stakeholders.”
