Agentive AI Transforms Global Healthcare

Growing Pressure on the Healthcare Sector

The global healthcare sector is under increasing pressure due to decades of chronic underinvestment and recruitment constraints. This situation coincides with a heightened demand for services, partly due to aging populations. The consequences are already visible: fragmented access to care, as well as high levels of stress and burnout among medical staff. The World Health Organization has warned that current workforce shortages could reach 11 million by 2030.

In this urgent quest for solutions, many healthcare providers are turning to agentive AI. According to KPMG, more than two-thirds (68%) of providers have already integrated AI agents into their workforce. This technology is deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients. The goal is to reduce the cognitive load on clinicians and improve the quality of care as the number of healthcare workers declines.

Another Form of Digitization

So far, the benefits of digitization in the healthcare sector have been limited. Many employees have blamed the slow or outdated technologies for adding to the administrative burden rather than alleviating it. For example, although American patient data was transferred to electronic health records (EHR) in the early 2000s, this data remains fragmented and relies on manual entries.

New telehealth services and digital care tools, such as remote monitors, have also shown gaps. Ashis Barad, MD, Chief Digital and Technology Officer at the Hospital for Special Surgery (HSS), an academic medical center in New York focused on musculoskeletal health, emphasizes that while these technologies have helped improve access to care by removing geographical barriers, they have failed to replicate the quality of in-person care or gain patients' trust.

Agentive AI stands out from these existing technologies, he insists. Rather than relying on manual entries or referring to human workers for any case slightly outside 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, freeing clinicians to focus on higher-level patient care. As Dr. Barad puts it: “Agentive AI takes your workflow and compresses it, augments it, supercharges it, and makes it more efficient.”

The Impact of Agentive AI at HSS

At HSS, AI agents have already been deployed in several areas. They manage complex background processes, such as insurance requests that previously took several weeks to process and involved both HSS staff and a third-party contractor to handle the volume. Now, AI agents complete all requests internally, processing 1,100 requests per month. They have reduced the call phase from 45 minutes to 5 minutes and improved the success rate of these calls from 65% to 100% in the nine months following their implementation.

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 agentive AI developer Ema Unlimited. The service is accessible 24/7 via the web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then schedules appointments with the most appropriate clinician, taking into account location, insurance coverage, and physician availability. “This completes the entire process,” says Dr. Barad. The AI agent is trained on “all our context, all our rules, and all our knowledge base,” he adds, providing patients with streamlined access to highly specialized knowledge from world-renowned surgeons.

Given the critical decisions delegated to AI agents, the triage service has built-in safeguards: sensitive, complex, or uncertain scenarios are escalated to human specialists. Every decision made by the AI agent is auditable, and human staff can intervene at any time. Patient data is secured, and the system is trained on all HSS protocols, policies, and care pathways. By keeping humans in the loop, Ema claims that its technology strikes a balance between efficient automation, patient-centered safety, and human-informed decision-making.

Towards Widespread Adoption of Agentive AI

As the technology becomes more widespread, it is up to providers to ensure they integrate this type of safeguard into their systems, says Dr. Barad. At HSS, all decisions regarding technology are filtered through an AI subcommittee that Dr. Barad co-chairs with a senior nursing executive. AI agents likely to impact patient care will be scrutinized much more rigorously than, for example, background processes, he explains.

Dr. Barad plans to create a dedicated AI lab on HSS's main campus in New York, a move aimed at democratizing access to technology within the organization. It will be open to all staff wishing to understand or build AI agents, with informative courses and one-on-one training. “We are putting agentive AI in everyone's hands,” he says. This echoes research from Deloitte, which revealed that leading adopters of agentive AI in the healthcare sector were much more likely to have opted for multi-agent solutions, redefining end-to-end workflows rather than limiting themselves to narrow solutions or individual use cases.

It seems that the key is to integrate AI agents throughout the enterprise, viewing them as a versatile technology. As Dr. Barad states: “It is a mistake to think of agentive AI in terms of use cases... It is a versatile technology, akin to electricity.”

In practice, this means that healthcare providers need to lay the right foundations to derive value from agentive AI. This includes creating a unified data strategy that integrates fragmented data sources within an organization to create a single, comprehensive source of truth. In the healthcare sector, data is often spread across multiple departments and providers, each with its own legacy IT system.

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

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

Building Better Outcomes

For Dr. Barad, the potential of AI agents to transform healthcare and alleviate current pressures on resources, access, and patient care is enormous. He envisions a future where 90% of non-clinical tasks in the healthcare sector could be managed by AI agents, thereby freeing clinicians for what he calls “value-added” work, namely the most complex, specialized, and sensitive cases.

Most healthcare providers also seem optimistic. According to research from KPMG, 84% of providers are already comfortable delegating decision-making regarding specific processes to AI agents.

“We spend so much time on keyboards and computers right now that we are not actually doing what we should be doing,” says Dr. Barad. “This is going to rehumanize healthcare.”