Building Readiness for AI Agents in Healthcare Systems - with Raheel Retiwalla of Productive Edge
🎯 Summary
Podcast Summary: Building Readiness for AI Agents in Healthcare Systems
This 33-minute episode of the AI and Business Podcast, featuring Raheel Retiwalla, CSO of Productive Edge, focused on defining Agentic AI in the context of enterprise healthcare and outlining the necessary infrastructure and strategic readiness required for its adoption. The core narrative shifted from traditional, insight-providing AI to autonomous, action-orchestrating AI agents.
1. Focus Area
The discussion centered on Agentic AI in Enterprise Healthcare, specifically differentiating it from traditional AI/ML, defining the architectural layers needed for deployment, and exploring practical use cases in payer and provider settings (e.g., care management, claims processing).
2. Key Technical Insights
- Agentic vs. Traditional AI: Traditional AI excels at informing (e.g., risk stratification alerts), whereas Agentic AI excels at acting and orchestrating the necessary steps based on that insight, significantly reducing manual handoffs across organizational silos.
- Three Layers of Readiness: Readiness requires a layered approach: 1) Foundational Capability (data/cloud infrastructure, MLOps, governance); 2) Agentic AI Platform (modular architecture featuring memory, tool use/API orchestration, and policy enforcement); and 3) Healthcare Tools (existing domain-specific models like risk stratification algorithms that agents can tap into).
- Tool Use and Memory: A critical component of the Agentic AI Platform is the ability for agents to possess memory (retaining past interactions) and tool use (connecting to domain-specific APIs, CRMs, or existing predictive models to execute tasks).
3. Business/Investment Angle
- Workflow Transformation: Agentic AI is positioned as a “workflow transformer,” not just an automation tool, capable of fundamentally rethinking processes by handling complex, multi-step orchestration previously requiring significant human cognitive load.
- ROI Through Throughput and Burnout Reduction: Real-world examples showed massive time savings (e.g., care plan drafting reduced from 45 minutes to 2-5 minutes), directly impacting operational throughput and mitigating staff burnout.
- Strategic Partnership for Readiness: Business leaders are advised to partner with IT by identifying high-pain, high-manual-work operational use cases and quantifying their ROI potential before charting a technical roadmap.
4. Notable Companies/People
- Raheel Retiwalla (Productive Edge): Provided the expert framework for agentic readiness and shared practical healthcare use cases.
- Hyperscale Vendors (Microsoft, Google): Mentioned as vendors already beginning to offer components of the modular Agentic AI Platform.
5. Future Implications
The industry is moving toward AI systems that can autonomously manage complex, cross-system workflows. This shift necessitates a focus on AI Governance—extending traditional data governance to include auditing, instrumentation, observation of agent paths, and bias checking—to manage the increased autonomy of these systems.
6. Target Audience
This content is highly valuable for Healthcare Technology Leaders (CIOs/CTOs), Healthcare Operations Executives (Payers and Providers), and AI Strategy Professionals operating within regulated industries who need to move beyond conceptual generative AI to practical, actionable enterprise deployment.
Comprehensive Summary
The podcast episode provided a deep dive into the strategic and technical requirements for adopting Agentic AI within complex healthcare systems, featuring Raheel Retiwalla of Productive Edge.
Narrative Arc: The conversation began by establishing the fundamental difference between traditional AI (which informs) and Agentic AI (which acts and orchestrates). Retiwalla used the example of a high-risk patient alert: traditional AI stops at the alert, while an agentic system would automatically draft follow-up scripts, schedule appointments, and coordinate necessary actions across disparate systems. This capability is poised to “turn workflows on their heads.”
Key Themes and Technical Concepts: Retiwalla introduced a crucial three-layer framework for enterprise readiness:
- Foundational Capability: The necessary base layer including robust data infrastructure, MLOps, security, and initial governance structures.
- Agentic AI Platform: The core innovation layer, characterized by modular architecture, memory (retaining context), and tool use (the “hands and legs” that connect to external systems via APIs).
- Healthcare Tools: Existing, domain-specific algorithms (e.g., quality coverage models) that agents can intelligently leverage.
Business Strategy and Governance: The discussion stressed that agentic AI blurs the lines of traditional automation by handling higher-order thinking. For regulated industries like healthcare, this autonomy demands enhanced governance. This AI Governance must build upon existing data governance, focusing on the agent’s ability to be audited, instrumented (to record specific actions), and observed to ensure transparency and check for bias.
Practical Applications: Several high-impact use cases were detailed:
- Care Management: Agents drafting comprehensive service plans for high-risk members by synthesizing intake notes, EHR history, and eligibility rules, reducing preparation time from 45 minutes to under five.
- Claims Pre-Adjudication: Agents intelligently pre-triaging claims by checking documentation gaps and flagging mismatches across multiple systems using reasoning, not just hard-coded rules.
- Behavioral Health Follow-up: Monitoring missed appointments and medication lapses, providing care managers with context-rich nudges (e.g., “Member has a refill in 12 days; noted mood decline”).
**Actionable Advice
🏢 Companies Mentioned
đź’¬ Key Insights
"build a governance so that it's explainable, it's transparent so people can feel again confident about investing in this technology and prove the ROI."
"The key is to act with clarity. Don't try to boil the ocean. Pick one high-value use case. Assemble the right people. Move with intention."
"this isn't a layer, no pun intended, on top of generative AI. You don't need to go through deterministic use cases to get to, and then generative AI use cases to get to agentic AI."
"notice the stark difference in how the systems behind generative AI differ so greatly from the layers that you've described in agentic AI."
"The service client one doesn't need to include PII in the beginning, where you're just assisting your service plan coordinators, for example, in just being very efficient at creating a more accurate at creating service plans. And that doesn't have to include PII. So that's high impact. It's highly feasible because it doesn't require massive investment in any foundation layer, for example."
"That's where I believe the agent is really shine, not in a place in clinicians, but in unburdening them from stitching systems together manually just to make them arrive at the right decisions."