Why we built a healthcare-focused agentic AI platform

I still remember the wall-sized butcher paper with hundreds of point solutions and HL7 interfaces. That's what it looked like to manage a hospital's clinical information systems before the EHR. Everything was a patchwork of point solutions. Lab systems, radiology systems, transcription tools, order entry, physician notes, nursing flowsheets - each lived in its own silo. Clinicians had to navigate multiple logins and workflows to deliver care, and IT teams had to manage a complex web of integrations, contracts, and governance.

The move to enterprise EHR platforms was about necessity. At a certain scale, managing dozens of disparate systems separately became unsustainable.

The same story is now unfolding with AI.

Health systems are rapidly adopting AI, but much of it is coming in the form of point solutions, each built to solve one problem in isolation. For example, one vendor for coding, one vendor for quality chart reviews, one vendor for prior authorizations, one vendor for outbound phone calls, and so on. That means each solution requires separate testing, performance monitoring, reporting, governance, risk assessments, security reviews, and vendor management. Multiply that across dozens of AI vendors, and the operational burden becomes overwhelming.

The limits of today’s options

From years of working with health systems, we’ve seen the same patterns:

• Point solutions – While appealing because they solve one problem well, they create disconnected workflows, messy analytics, and increase vendor overhead.
• Horizontal AI platforms – Technically powerful, but lack healthcare-specific capabilities, deep EHR integrations, and require extensive customization.
• EHR-vendor AI – Deeply integrated within a single EHR but closed to broader ecosystem connectivity, limiting flexibility.
• Homegrown tools – Tailored to one organization’s needs but costly to build, maintain, and scale.

What an enterprise healthcare AI platform must deliver

Healthcare AI can’t be just about algorithms. It needs a foundation that supports scale, governance, and integration across the entire organization. That means:

• Governance and leadership – Clear decision-making structures, integration plans, evaluation metrics, and a single platform for performance monitoring and oversight.
• Security and compliance – Encryption, access controls, identity management, and auditability.
• Operational resilience – Disaster recovery, risk management, and contingency planning.
• Configurability and interoperability – Low-code workflow orchestration, third-party connectors, and EHR-agnostic integrations.
• Lifecycle management – Versioning, change control, and performance testing.
• Adoption and engagement – Training plans and user feedback loops.

Without these capabilities and without centralized governance tooling, AI initiatives risk stalling after pilots, unable to deliver sustained value at scale (see my previous piece here on success criteria for pilots).

Why we built Notable’s Platform

We purpose-built our agentic AI Platform for healthcare for specifically these reasons.

Our platform combines:

• Dedicated healthcare focus – Built for the unique workflows, regulations, and stakes of healthcare delivery.
• Agentic AI at scale – AI Agents that execute end-to-end workflows across clinical and administrative domains.
• Extensive pre-built automations – A library of proven healthcare task automations ready to deploy - from revenue cycle to value based care to patient access and contact center, and more.
• Flow Builder – Enabling low-code configuration of AI Agents without engineering resources.
• EHR-agnostic integrations – Seamless interoperability across major EHRs and beyond.
• Third-party ecosystem connectivity and orchestration – Integrating and orchestrating tools and partners that health systems already use.
• Unified governance framework – One set of tools for oversight committees to manage testing, QA, performance, safety, compliance, and reporting across all agents.

We recognize that health systems already have a complex network of existing enterprise systems. Our approach reduces the need to evaluate, manage, and govern dozens of separate AI tools, while working within the context of what you already have.

Why governance is the linchpin

AI in healthcare doesn’t just need to “work” - it must work safely, consistently, and compliantly across a complex organization. That’s where governance comes in.

Today, most health systems’ AI Governance Committees face a nightmare scenario: dozens of different AI vendors, each with their own dashboards, metrics, monitoring tools, and reporting formats. This means:

• No source of truth for performance, quality, or safety monitoring.
• Inconsistent definitions and thresholds for success.
• Fragmented audit trails make compliance and risk review harder.
• More time spent collecting and normalizing data than making decisions.

With a platform approach, governance teams can get closer to a single set of tools to:

• Monitor performance, quality, and safety across all deployed AI agents.
• Track compliance and risk indicators in real time.
• Standardize reporting and metrics for AI oversight.
• Manage version control, approvals, and change tracking from a single place.

A platform approach makes it possible to govern AI at scale, without exponentially scaling the burden on committees and oversight teams.

The future will belong to platforms

Just as healthcare moved from dozens of disconnected IT systems to unified EHR platforms, it will move from dozens of disconnected AI tools to enterprise AI platforms. The organizations that make this shift first will have a compounding advantage: the ability to deploy, adapt, and scale AI across their operations with speed, consistency, and confidence, all while maintaining the highest standards of governance and safety.

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