Rehumanizing global healthcare with agentic AI

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The global healthcare sector is under increasing pressure.

Decades of chronic underinvestment and recruitment constraints have coincided with an increase in demand for services for aging populations. Gaps in provision are already having detrimental consequences, with fragmented access to care and high rates of stress and burnout among staff. And it's getting worse and worse. The World Health Organization has warned that the current shortfall will reach 11 million workers by 2030.

In their urgent search for a solution, many healthcare providers are now pinning their hopes on agentic AI, with more than two-thirds (68%) having already adopted AI agents on staff, according to KPMG.

The technology is being deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients, all with the goal of reducing the cognitive load on clinicians and improving the quality of patient care as the number of healthcare workers declines.

Another type of scanning

So far, the benefits of digitalization in healthcare have been limited.

Many staff blamed slow or outdated technology for adding to administrative burden rather than easing it. For example, U.S. patient data was migrated to electronic health records (EHRs) in the early 2000s, but this data remains fragmented and dependent on manual entry.

New telehealth services and digital care tools, like remote monitors, have similar shortcomings, says Ashis Barad, MD, director of digital and technology at Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health. Both technologies have helped improve access to health care by removing geographic barriers, he says, but they have failed to replicate the quality of in-person care or gain patient trust.

Agentic AI is different from these existing technologies, he insists.

Rather than relying on manual input or defaulting to human workers for any case that falls slightly outside of a rigid framework, AI agents can handle nuanced and complex scenarios. They can make autonomous decisions, retrieve information from expert clinical sources, and iterate over time, allowing clinicians to focus on higher-level patient care. As Dr. Barad says: “Agentic AI takes your workflow and shrinks it, augments it, supercharges it, and makes it more efficient. »

At HSS, AI agents have already been deployed in several areas. They manage complex back-end processes, such as insurance claims that previously took several weeks and involved both HSS staff and a third-party contractor to manage the volume. Today, says Dr. Barad, AI agents process 1,100 claims per month. They reduced the call stage from 45 minutes to five minutes and improved the success rate of these calls from 65% to 100% in the nine months after implementation. HSS now handles all claims internally.

Building on this success, HSS is now deploying AI agents in non-clinical patient-facing environments with an AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, SMS or telephone. It uses conversational AI to ask patients clarifying questions about their condition, then schedules an appointment with the most appropriate clinician, taking into account the doctor's location, insurance coverage and availability. “It closes the whole circle,” says Dr. Barad. The AI ​​agent is trained on “all of our context, all of our rules and our entire knowledge base,” he adds, providing patients with simplified access to highly specialized knowledge from world-renowned surgeons.

Given the high-stakes decisions delegated to AI agents, the triage service has built-in protections: sensitive, complex or uncertain scenarios are passed to human specialists. Every decision made by the AI ​​agent is verifiable and human staff can intervene at any time. Patient data is kept secure and the system is trained on all HSS protocols, policies and care pathways. By keeping humans informed, Ema says its technology strikes a balance between effective automation, patient safety first, and human-informed decision-making.

As the technology becomes more prolific, it will be up to providers to ensure these types of guardrails are built into their systems, says Dr. Barad. At HSS, all technology decisions are filtered through an AI subcommittee that Dr. Barad co-chairs alongside a senior nursing executive. AI agents that could impact patient care will be scrutinized much more rigorously than, say, back-end processes, he explains.

AI agents drive system-level change

For example, Dr. Barad plans to create a dedicated AI lab on HSS's main campus in New York, an initiative that aims to democratize access to technology across the organization. It will be open to all staff looking to understand or create AI agents, he explains, with informative courses and one-on-one training. “We’re putting agentic AI in everyone’s hands,” he says. This echoes research from Deloitte, which found that leading users of agentic AI in healthcare were much more likely to have opted for multi-agent solutions, rethinking workflows end-to-end rather than sticking to narrow solutions or individual use cases.

The key seems to be to integrate AI agents throughout the enterprise, treating them as a general-purpose technology. As Dr. Barad says: “It is wrong to think of agentic AI in use cases… It is a general-purpose technology, analogous to electricity. »

In practice, this means that healthcare providers need to lay the right foundation to drive value through agentic AI. This includes creating a unified data strategy, which integrates fragmented data sources across an organization to create a single, comprehensive source of truth. In healthcare, data is often distributed across multiple departments and providers, each with their own existing IT system.

In systems that rely on fragmented data sources, metrics often also lack standardized definitions. For example, Dr. Barad says each hospital he worked at had a slightly different definition of “surgery start time,” a commonly used metric to gauge operating room efficiency. This level of fragmentation prevents AI agents from retrieving information from different sources or applications and assimilating the tacit knowledge that differentiates them from other technologies.

By creating greater data interoperability at HSS, patient-facing AI agents can draw on a patient's clinical history and existing recommendations from their clinician, combine that information with current symptoms, and decide whether a situation requires escalation before notifying the correct specialist and informing the patient.

Building better results

For Dr. Barad, the potential for AI agents to reshape healthcare and alleviate current pressures on resources, access and patient care is enormous.

He envisions a future in which 90% of non-clinical healthcare tasks could be administered by AI agents, freeing up clinicians for what he calls white glove work—that is, the most complex, specialized and sensitive cases.

Most health care providers seem equally optimistic. According to a KPMG study, 84% of providers are already comfortable entrusting decision-making regarding specific processes to AI agents.

“We spend so much time on keyboards and computers right now that we're not doing what we should be doing,” says Dr. Barad. “This will rehumanize health care. »

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the MIT Technology Review editorial team. It was researched, designed and written by human writers, editors, analysts and illustrators. This includes writing surveys and collecting data for surveys. The AI ​​tools that could have been used were limited to secondary production processes that had undergone extensive human review.

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