The SONG Framework
Signal • Orchestration • Normalization • Governance
A Framework for Healthcare AI Agents That Actually Scale
Download The SONG Framework Whitepaper
The SONG Framework
SONG is a diagnostic and design framework that identifies whether an AI agent is positioned to scale—or destined to stall. Each dimension is load-bearing. Weakness in any one predicts failure.
Signal
Signal refers to the reliable, timely availability of raw clinical facts. FHIR availability alone is insufficient. Production agents must handle latency, batching delays, partial data, and fallback strategies for non-connected systems.
Orchestration
Orchestration determines how agent outputs enter human workflows. Without trust-based routing, AI increases review fatigue instead of reducing burden. Confidence scoring and escalation logic are essential.
Normalization
Structural interoperability is not semantic interoperability. Agents must normalize across LOINC, SNOMED, RxNorm, ICD-10, units of measure, and local terminologies before reasoning can be trusted.
Governance
Production AI requires explicit auditability. Every decision must be traceable to agent logic version, data inputs, timestamps, and human attestation to support legal and regulatory scrutiny.
The Convergence Moment (2024–2027)
Why Healthcare AI Is Being Forced Into Production
Regulatory Mandates with Hard Deadlines
TEFCA, CMS-0057-F, Da Vinci
Interoperability is no longer optional. Payers must expose FHIR-based Prior Authorization APIs by 2027. TEFCA is live, but incomplete—forcing agents to operate across mixed data ecosystems.
AI Capability Has Matured
Models Are “Good Enough”
Large language models can now reliably parse clinical documentation, reason across workflows, and generate structured outputs. The AI model is no longer the bottleneck—the surrounding data infrastructure is.
Unsustainable Administrative Burden
25–40% of Healthcare Spend
Prior authorization, eligibility checks, and documentation consume billions annually. Workforce shortages mean organizations can no longer scale by adding people.
Interoperability Gaps Are Now Visible
FHIR ≠ Real-Time Signal
FHIR servers often update in batches, rely on document-based C-CDA, and lack semantic consistency. AI agents built without latency resilience fail silently in production.
Workforce Constraints & Review Fatigue
AI Can’t Add “More Homework”
Clinicians are already overwhelmed. Agents that generate large volumes of low-confidence outputs increase review fatigue instead of reducing burden—forcing orchestration and trust-based routing.
Legal, Risk & Governance Pressure
Auditability Is Mandatory
Healthcare leaders must answer: What data did the agent see? Which logic version ran? Who approved the outcome? AI without explicit governance is legally indefensible.
The Model Isn’t the Bottleneck
Healthcare AI conversations focus heavily on models—accuracy, hallucinations, and benchmarks. But in real-world deployments, AI fails for a simpler reason: the data isn’t available when it matters.
Modern AI models are already capable of handling most healthcare workflows. What limits them in production is interoperability. Clinical data is fragmented across systems, delayed by batch updates, and often inconsistent even when FHIR APIs exist.
As a result, AI pilots succeed in controlled demos but break down in live environments. The problem isn’t intelligence—it’s access to reliable, timely clinical signal. SONG starts with this reality and treats interoperability as the foundation, not an afterthought.
Modern AI models are already capable of handling most healthcare workflows. What limits them in production is interoperability. Clinical data is fragmented across systems, delayed by batch updates, and often inconsistent even when FHIR APIs exist.
As a result, AI pilots succeed in controlled demos but break down in live environments. The problem isn’t intelligence—it’s access to reliable, timely clinical signal. SONG starts with this reality and treats interoperability as the foundation, not an afterthought.
See the Framework That Makes
AI Work
in Production
Learn how leading healthcare organizations design AI agents around interoperability, workflows, and governance—before choosing a model.
