Hospitals Turn to AI to Help Streamline Care and Cut Costs

Hospitals are facing unprecedented cost pressures and persistent staffing challenges, forcing a deeper look at where automation can make a measurable impact. The American Hospital Association’s “Cost of Caring” report shows that labor and related compensation now account for nearly 56% of total hospital expenses, underscoring the system’s heavy reliance on human capital. Executives are turning to artificial intelligence not just for new diagnostic breakthroughs but also to fix long-standing operational inefficiencies.

The latest partnerships and funding rounds indicate that AI is moving into the operational aspects of healthcare, according to recent Wall Street Journal coverage that detailed how hospitals are deploying AI agents for repetitive work that summarize records, manage patient intake, and coordinate follow-up care, all with the goal of freeing staff for direct clinical work.

One of the clearest examples of this shift is Suki, a healthcare AI company that has launched a national nursing consortium to build Suki for Nurses. The initiative includes partners such as McLeod Health, Fisher-Titus, and other regional providers, and is designed to automate documentation for nurses using voice input and ambient listening. The system connects directly with major electronic health record platforms, including Epic, MEDITECH, and Oracle Health. By capturing assessments and intake forms in real time, it aims to reduce the administrative load that often extends nurses’ shifts and contributes to burnout.

“Each partner brings a different perspective on nursing workflows, enabling Suki to both provide and power AI solutions in a scalable, expedited fashion that will ensure maximum adoption of this technology,” said Punit Soni, CEO and founder of Suki.

Other healthcare technology firms are addressing the same challenge from different points in the workflow. Notable has introduced Flow AI, a conversational assistant embedded in its low-code platform. Instead of engineers scripting automations, hospital teams can describe processes such as scheduling follow-ups or updating payer data in plain language. The system converts those instructions into automation logic and runs them internally, allowing rapid adjustments as requirements change. “Flow AI reimagines the way teams automate workflows by removing friction from the building process,” said Pranay Kapadia, co-founder and CEO of Notable.

On the administrative back end, Penguin AI raised $29.7 million to expand its AI platform for automating prior authorizations, claims, and appeals. Its design uses small language models (SLMs), leaner, domain-specific systems built for efficiency and tighter control compared to large general-purpose models. “The healthcare industry is plagued by inefficiencies and is spending billions on administrative tasks annually,” said Fawad Butt, founder and CEO of Penguin AI. “By harnessing the power of generative AI, we can significantly reduce these costs, improve accuracy, and unburden valuable resources so payers and providers can focus on delivering quality care to patients.”

The decision to adopt smaller, specialized models aligns with a broader trend. As PYMNTS recently reported, enterprises are turning to SLMs because they operate more efficiently, are faster to fine-tune, and are easier to govern, advantages that align with healthcare’s compliance requirements.

The same operational focus is evident at Oracle Health, which is embedding AI tools in billing, eligibility checks, and claims systems to help address an estimated $200 billion in annual administrative costs. In a related analysis, PYMNTS noted that 67 percent of healthcare executives cite manual payment processes as a major barrier to efficiency, while 74 percent say legacy systems increase compliance and penalty risk.

While adoption remains gradual, the direction is clear. Healthcare leaders now view AI as essential infrastructure for sustaining operations. As the American Hospital Association puts it, “operational innovation is no longer optional; it’s essential for maintaining access and stability.”

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