Design, govern, and implement clinically safe multiagent AI systems for real PHC clinics and hospital workflows.

Presented by

Hosted by

Authored by Dr Ruchi Saxena

Introduction

Health systems are under pressure from rising demand, workforce shortages, cost escalation, and information overload. Most “AI for healthcare” projects add to that burden instead of reducing it. This course gives you the conceptual foundations, practical frameworks, and worked examples to design and govern multiagent systems that actually fit primary care and hospital realities.

Learning Outcomes

By the end of the course, you will be able to:

• Explain, in clear language, what AI agents and multiagent systems are, and why a single model or agent is not enough for complex clinical work.
• Map real PHC and hospital workflows (triage, bed management, sepsis management, oncology, mental health, virtual wards) into multiagent designs with defined agent roles and oversight levels.
• Use the MAS Design Canvas to design a new multiagent system for your own setting.
• Apply safety patterns – redundancy, graceful degradation, audit trails, circuit breakers – so systems fail safely, not silently.
• Assess institutional readiness with a structured scorecard before committing to a MAS deployment.
• Build or evaluate governance frameworks that clarify accountability, incident management, and regulatory alignment.
• Design equity‑aware MAS deployments that do not leave PHC and low‑resource settings behind.

If you are a developer or technical lead, you will also: