HHS just released their “OneHHS” AI Strategy with five pillars to guide the Department’s integration of AI into clinical care and the public health system. Pillar 2, which promotes a reusable AI infrastructure and platforms across divisions and cases, poses a difficult question: how do you actually operationalize a “value layer” for AI?
This is where most AI programs break down. Teams pilot tools, secure budget, and then hit an execution or value wall.
Keywell’s AI Platform is specifically designed to make the execution of an AI strategy simple, straightforward, and HIPAA-compliant for health and human services organizations. We make sure you see value with reusable foundations, rather than endless capital expenditure on point solutions. Here’s how the Keywell AI Platform delivers the capabilities your organization needs.
HHS Requirement | Keywell Capability | Risk if You Don’t |
Reusable AI value layer so new AI solutions can be developed, tested, and deployed rapidly. | Unified AI Platform with a Web-based control panel. Users can quickly enable new agents and use cases, and manage them within a centralized infrastructure. | Duplication of functionality and cost across agencies and divisions; long time-to-value and implementation risk for new use cases; many isolated black-box point solutions. |
API-First Orchestration across systems, models, tools. | API-First AI Agents (Tool Calling, Multi-Step Workflows, Role-Based Agents). Custom agents are deployed rapidly into a common infrastructure accessed via the control panel. | Siloed capabilities; pilot paralysis; inability to automate end-to-end processes such as prior auth, eligibility, and care management end-to-end. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Centralized data + FAIR standards; eliminate duplication; secure access. | Open-Source Healthcare Data Ontologies & Data Quality Pipelines unify claims, clinical, provider, and social data into standard vocabularies (ICD, CPT, HCPCS, NPI, SDoH). | Endless data prep; inconsistent definitions; vendor lock-in to healthcare analytics tools; technical debt from siloed transformations. |
AI-ready access to studies, EHR, claims, surveillance. | Data Curation for AI Models. Unified data layer and connectors inside the Keywell Platform ensure that data remains in place; definitions and data “training” zones ensure AI models have appropriate context for data usage. | Analysts remain stuck in backlog; data cannot be reused across AI applications; results differ across departments, and AI cannot interpret data correctly. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Real-time governance + monitoring of models. | Model Cards + Observability + Real-time Usage Tracking | Governance becomes a slide deck instead of live controls, resulting in a lack of visibility into inappropriate use cases, applicable populations, the source of model training data, or model drift. |
Cost visibility and budget control. | Budget Management Dashboards (Token, Compute, User-Level Spend) | Runaway LLM costs; lack of understanding of granular usage by models or users; inability to forecast spend. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Secure, compliant model environments (supporting private/open models). | Private Model Hosting inside VPC + On-Demand Compute + AI Endpoints With Guardrails and Managed Permissions | Compute bottlenecks; overshoot or undershoot compute costs; technical debt with complex infrastructure leading to HIPAA security and compliance risk. |
Reusable, portable model hosting. | Central Model Registry + Versioning | Every team builds its own infrastructure, resulting in zero reusability and inconsistent performance across departments. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Ability to test models prior to production using real data. | HIPAA-Compliant Evaluation Sandbox with SME Feedback Loops | Models perform well in demos but fail on real-world data, yielding inaccurate results, barriers to adoption, and rejection by operations teams. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Shared, secure APIs for reuse across divisions. | Keywell API Gateway (Authentication, Observability, Access Control). Built-in guardrails and monitoring, even with API usage in external environments; user-level API authentication with integrated SSO. | AI stays stuck in consumer-facing chatbots; EHR, MMIS, and legacy system integration becomes impossible; shadow IT emerges. APIs without built-in guardrails present compliance risks. |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Document ingestion → structured data. | Document Parsing + PHI Flags + Structured Extraction Templates. Accurately parse faxes, eligibility documents, and scanned documents, and integrate into data pipelines for known and unknown document types. | High-cost document processing, siloed outputs; inability to integrate structured and unstructured datasets for full patient or beneficiary case view (i.e., for Medicaid eligibility, appeals, PA, provider enrollment). |
HHS Requirement | Keywell Capability | Risk if You Don’t |
Text-to-SQL and AI-powered analytics integrated with existing BI. | AI/BI Layer + Natural Language Querying + Unified Data Catalog. AI data rooms enable users to curate datasets and continue training for AI interpretation. | Competing data truths; analytics backlog persists; insight delay affects compliance, quality, and cost management. |
| HHS Requirement | Keywell Capability | Risk if You Don’t |
| Integration into eligibility, PA, care management, and human services workflows. | AI Application Builder + Business Rules Manager. Keywell platform provides scaffolding for AI-accelerated applications and custom “fit-for-purpose” AI tools. API-first infrastructure allows integration in external systems with user-level security and single sign-on. | AI capabilities remain dormant in isolated environments; LLM outputs cannot be effectively operationalized; business users cannot refine the logic; and AI tools are not specific enough to accommodate actual use cases. |
| HHS Requirement | Keywell Capability | Risk if You Don’t |
| HIPAA privacy & auditability baked into infrastructure. | User/Group Permissions, Audit Trails, Model Logging, Document Source Controls. Unified user-level auditability and access to models, data sources, unstructured documents, parsed data, agents, and applications. | Adoption stops at security review; auditability is not granular enough at user access levels for HIPAA compliance; inability to prevent and investigate AI misuse. |
If your organization is being asked to operationalize AI infrastructure in healthcare, the foundational requirements are now clear. Many are responding to HHS direction, board expectations, or simple operational necessity. The real challenge is execution. We are already working with healthcare organizations that are implementing this infrastructure today.
Contact us if you are navigating these architectural decisions and want to discuss data ontologies, orchestration frameworks, evaluation sandboxes, and governance integration.
