Step 1: Transformation-First Foundation
We began by restructuring their data stack into a governed medallion architecture:
Bronze → Silver → Gold
Using 0to60.AI’s prompt-to-code transformation
capabilities, we:
- Standardized financial and clinical metrics
- Rebuilt claims calculation logic
- Normalized regional contract terms
- Encoded reimbursement formulas into machine-readable pipelines
- Validated transformation outputs automatically
This eliminated cross-department inconsistencies before any agent was introduced.
Step 2: Enterprise Data Dictionary & Semantic Layer
We then built a formal AI-ready semantic layer that included:
- Unambiguous definitions (e.g., Gross Margin vs Adjusted Margin)
- Calculation formulas
- Refresh frequency
- Data ownership
- Regulatory applicability (HIPAA, regional policies)
- Access control rules
- This step alone resolved 70% of inconsistencies observed in executive dashboards.
Step 3: Domain-Constrained Multi-Agent Design
Rather than deploying a single enterprise agent, we architected specialized agents:
1. Revenue Cycle Agent
- Claims validation
- Contract reconciliation
- Three-way matching logic
- Anomaly detection
- Escalation routing
2. Clinical Operations Agent
- Bed occupancy thresholds
- Staff scheduling alerts
- Supply chain reordering signals
- Operational bottleneck detection
3. Compliance Sentinel Agent
- Audit trail validation
- HIPAA access monitoring
- Documentation completeness checks
- Regulatory deadline tracking
Each agent:
Operated within its gold-layer domain
Had strict access boundaries
Was grounded in department-specific system prompts (digital SOPs)
Logged all actions for audit
No agent had cross-domain autonomy.
Step 4: Orchestration Layer Implementation
To prevent chaos, we implemented an orchestration layer that:
Routed queries to appropriate domain agents
Enforced validation gates before actions
Maintained audit logs
Required human approval for high-risk actions
Coordinated cross-agent communication only through structured interfaces
This prevented:
Conflicting recommendations
Duplicate actions
Unauthorized escalations
Think of it as AI air traffic control inside the hospital network.
Step 5: Phased Autonomy Deployment
We rolled out in three phases:
Phase 1: Insight Mode (0–3 months) Agents observed and recommended only.
Phase 2: Assisted Mode (3–6 months) Agents executed actions with human approval.
Phase 3: Controlled Autonomy (6+ months) Agents autonomously acted within predefined financial and compliance thresholds.
For example:
Claims under a certain validated threshold were auto-processed.
Procurement anomalies triggered contract validation automatically.
Compliance gaps generated Jira tickets with escalation routing.
At no point was unrestricted autonomy allowed.
