The future of healthcare with agentic AI 

The healthcare industry is undergoing a rapid transformation. Costs are high and talent is scarce, yet artificial intelligence (AI) is helping providers do more and better with less — and through agentic AI, digital agents will expand the workforce’s capacity to meet demand. This shift requires a radical rethink of work to ensure care delivery remains efficient, impactful and sustainable.

Agentic AI is a complex, autonomous system that works toward goals without needing constant human input. Agents can learn, perceive, reason, plan and even make decisions on their own by combining at least some of the following:

• The efficiency of robotic process automation (RPA)
• The natural language processing (NLP) of chatbots
• The trial-and-error of machine learning (ML)
• The processing power of large language models (LLMs)
• The data-driven logic of predictive AI
• The novel outputs of generative AI (Gen AI)

AI and automation can drive exponential gains in performance, from boosting productivity and efficiency to bridging knowledge and skills gaps. In healthcare, however, the primary motivation is freeing up capacity so talent and skills can be redeployed to where they’re needed most.

Achieving this goal requires an approach that’s “work backward” instead of “tech forward.” Although 67% of employers  adopt new tools without changing how they work, leading organizations are embracing the AI-augmented operating system (AOS) and embedding AI into their cultures, strategies and operations to unlock human–machine synergy.

Numerous health systems are redesigning work to enable the optimal combinations of humans and machines. Through work design, these systems can deconstruct jobs and processes into tasks, identify which tasks are best suited for humans or technology (or both), and then reconstruct new ways of working.

Each task is time- and labor-intensive. To diagnose cancer and recommend treatments, for instance, oncologists need to review huge volumes of data — including patients’ medical histories and biomarkers. But through the integration of RPA and ML, AI systems can analyze medical imaging and other data to identify risks, optimize radiation and chemotherapy, develop new treatments, and predict outcomes.

These emerging technologies could radically transform the entire oncology workflow to free up time and increase the impact of every human interaction, from intake to diagnosis. Reducing the time needed for these tasks not only streamlines the decision-making process but also enables oncologists to focus on more complex cases that require human expertise.

Beyond oncology, this work redesign enables a range of healthcare professionals to practice “at the top of their license,” handling complex tasks that require the full scope of their training. Providers are now exploring the use of automation and AI in several areas:

Given the more recent advances in generative AI and agentic AI, this is just the tip of the iceberg for healthcare work design. In our next article, we explore how these technologies have exponentially transformed healthcare workforce management.