Governed AI Tools for Healthcare

The challenge

Healthcare data and workflows are spread across clinical, care management, and operational systems. AI agents need a defined way to use approved capabilities without direct access to every underlying system.

The architecture

Douglas designed an agentic healthcare platform using Model Context Protocol (MCP). A shared interoperability layer presents authorized healthcare capabilities as discoverable tools for internal and third-party AI agents. Hospitals, clinics, health plans, and technology partners can build their own agents while access remains governed by the platform.

• Patient risk, care gaps, clinical context, admissions, and discharges
• Care management, patient outreach, and population health
• Clinical operations, analytics, and healthcare integrations

How access is governed

The MCP layer defines authentication, authorization, tool discovery, standardized interfaces, input validation, audit logging, and security controls. Agents invoke only the capabilities they are permitted to use. They do not receive unrestricted access to clinical systems or patient information.

A care management example

A high-risk patient workflow can bring together authorized context, risk indicators, care gaps, and clinical events to support intervention planning and care management. Clinical judgment and decisions remain with qualified people.

Reusable foundation

Standardizing healthcare capabilities as MCP tools creates a foundation for partner agents and future workflows involving multiple agents. Each connection follows the same access and governance patterns.