Rehumanizing global health care with agentic AI

The global healthcare sector faces immense strain due to chronic underinvestment, recruitment challenges, and rising demand from aging populations. This has led to fragmented care access and high rates of staff burnout. The World Health Organization predicts a shortfall of 11 million healthcare workers by 2030. Many providers are now turning to agentic AI as a solution, with over two-thirds already integrating it into their workforce.

Agentic AI automates complex back-office processes, collaborates with medical teams, and triages patients. This reduces the cognitive load on clinicians and improves care quality, especially as the supply of human healthcare workers dwindles. Unlike earlier digital health technologies, which often added to administrative burdens or failed to replicate in-person care quality, agentic AI can handle nuanced, complex scenarios autonomously. It retrieves information from expert sources and iterates over time, allowing clinicians to focus on higher-level patient care.

HSS, an academic medical center, has successfully deployed AI agents to manage insurance claims, reducing processing times and improving success rates. Building on this, HSS is now using AI for patient-facing services like scheduling and triage. This 24/7 service uses conversational AI to gather patient information and book appointments with the most suitable clinician, demonstrating the technology's ability to "complete the whole loop" of patient interaction.

Despite the significant autonomy of agentic AI, safeguards are crucial. The HSS triage service escalates sensitive cases to human specialists, and all AI decisions are auditable. Patient data security and adherence to HSS protocols are paramount, ensuring a balance between automation, patient safety, and human oversight.

To maximize the value of agentic AI, healthcare providers must integrate it as a general-purpose technology across the entire enterprise, much like electricity. This requires a unified data strategy to consolidate fragmented data sources and establish a comprehensive "source of truth." This organizational approach is essential for successful implementation and widespread adoption of agentic AI.