Standardizing Clinical AI Infrastructure
Standardizing clinical AI infrastructure across heterogeneous legacy systems: one generative layer in front of many EHRs, modern capabilities without disruption, and operational cost kept firmly under control.
The brief
We standardized clinical AI infrastructure by putting one generative layer in front of a set of heterogeneous legacy EHRs.
We bridged those legacy systems to modern generative capabilities and held operational cost down by routing each request to the cheapest model that could handle it.
How it works in the clinic
Legacy EHR Integration
- Wraps legacy connections in modern healthcare-standard interfaces for consistent data flow.
- Brings historical patient records along, so the AI sees a comprehensive timeline.
The Right Model for Each Task
- Routes each task to the model best suited to it, automatically.
- Complex clinical reasoning goes to the most capable models; routine work to fast, economical ones.
Cost Controls
- Implements dynamic prompt caching and token-limit enforcement.
- Schedules batch operations during off-peak hours for reduced compute cost.
Provider Portal Integration
- Embeds contextual AI widgets directly within existing portal environments.
- Eliminates the need for practitioners to learn entirely new software.
What it runs on
A highly modular architecture designed to swap foundational models as capabilities evolve.
One Generative Core
A single layer that simplifies access to leading AI models:
- Grounded Knowledge. Internal practice guidelines ground every response in validated protocols.
- Multi-Step Queries. The AI can complete multi-step clinical tasks, like gathering the relevant results before summarizing.
- Cost Attribution. Every AI request is attributed to a department, so spend is always accountable.
Integration & Access
The connective tissue bringing AI to end-users:
- A Unified Data View. Patient records and real-time AI insights come together in one consistent surface.
- Role-Based Access. Strict controls define exactly who can run which AI tasks.
- Lightweight Orchestration. Requests flow to the right model pipeline with minimal moving parts.
Run, monitor, stay compliant
Continuous Optimization
Ensuring model usage stays economical without sacrificing quality:
- Usage Monitoring. Granular dashboards track AI usage per session, flagging anomalous spikes.
- Model Validation. Controlled experiments validate cheaper models on small slices of traffic before any switch.
Security Posture
A defense-in-depth approach protecting the AI layer:
- Prompt-Injection Defense. Malicious inputs are filtered before they ever reach a model.
- Private Networking. All AI traffic stays on private networks, never the open internet.
Tuned for clinical relevance
Systematic prompt version control manages iterative improvements across development life cycles.
Templating Engines
Separates prompt structure from injected patient parameters for reusability.
Evaluation Frameworks
Runs offline batches of golden-standard prompts against new models to measure degradation.
Persona Shifting
Adjusts language complexity depending on whether output is for a patient or physician.
Few-Shot Learning
Embeds specific clinical examples in the system prompt to guide expected formatting.
The outcome
In production this standardized clinical AI across legacy EHRs behind one provider portal, and kept cost predictable by routing each request to the cheapest model that could handle it. Clinicians stayed in the software they already knew while the generative layer underneath was swapped and tuned without touching their workflow.
Frequently asked questions
How do you standardize AI across legacy EHRs?
Legacy connections are wrapped in modern healthcare-standard interfaces that feed one generative layer, surfaced through the providers’ existing portal, so clinicians never have to learn new software.
How is the cost of generative AI controlled?
Each request is routed to the cheapest model that can handle it, with caching, usage limits, off-peak batch scheduling, and per-department cost attribution.
How is the AI layer kept secure?
Malicious prompt-injection attempts are filtered before they reach any model, all AI traffic stays on private networks, and role-based access controls define exactly who can run which AI tasks.