AI and infrastructure that passes clinical scrutiny
We deploy healthcare engineering teams that understand HIPAA at the architecture level, FDA guidance at the integration level, and clinical workflows at the design level. Systems that pass audit on deployment day.
The Problem We Solve
Healthcare technology fails when the vendor doesn't understand the clinical context. A perfectly architected data pipeline that routes information the wrong way relative to a clinician's workflow is not just unusable — it's potentially dangerous. A compliance infrastructure that satisfies HIPAA's technical requirements but creates friction that causes clinical staff to route around it has achieved nothing. Healthcare technology requires the intersection of engineering depth and clinical domain knowledge that most vendors simply don't have.
The US healthcare technology market is dominated by a handful of legacy vendors — Epic, Cerner, Meditech — whose architectures were designed in the 1990s and whose integration models reflect that era. Every healthcare application has to integrate with these systems. Our engineers know Epic's HL7 and FHIR implementation constraints. They know where Cerner's API deviates from the standard. They know how to build SMART on FHIR applications that pass Epic's certification process. This knowledge is not in a textbook — it's accumulated from shipping clinical integrations.
The largest EHR vendor in the US faces antitrust litigation from multiple state attorneys general. Implementation costs for major health systems routinely exceed $500M. One federal agency reverted to paper records after a failed deployment. The dominant claims processing AI has been documented denying claims at scale while its parent company faces Medicaid fraud allegations. A major healthcare IT vendor's subsidiary was breached for 12 months before detection. These are not isolated incidents — they are the systemic output of an industry that has concentrated market power in vendors with no structural incentive to improve.
Healthcare interoperability mandates are creating a technical reckoning. ONC's information blocking rules, CMS's interoperability requirements, and the TEFCA network requirements are forcing health systems to expose data through FHIR APIs that their legacy architectures were never designed to support. The health systems that are compliant with these requirements today built modern data architectures that treat FHIR as a first-class interface, not a bolt-on API layer over a 1990s data model. Our healthcare technology teams build for this reality — not for the architecture of the systems that are currently failing to comply.
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How Our Teams Approach This Differently
Healthcare technology engagements begin with a clinical workflow review that happens before architecture begins. Our clinical domain specialists map the workflows the technology will support — not the requirements the client describes, but the workflows as they actually operate in clinical environments. A medication ordering workflow that looks simple on a requirements document may involve seven system interactions, three role transitions, and two override scenarios that the requirements didn't capture. Understanding the clinical reality before the architecture is designed prevents the workflow failures that make clinical technology systems dangerous.
FHIR R4 is the integration standard — but every major EHR implements FHIR differently. Our Epic integration engineers know which FHIR resources Epic supports, which it has extended with proprietary fields, and which require specific workflow scopes to access. Our Cerner integration engineers know where Cerner's FHIR implementation deviates from the specification and how to handle the deviations without creating data integrity gaps. This is not documentation knowledge — it's the accumulated learning from shipping dozens of healthcare integrations against production EHR environments.
Clinical AI systems require a validation pathway that most AI vendors don't plan for. FDA Software as a Medical Device (SaMD) classification may apply depending on the clinical function — and SaMD classification triggers validation requirements that change the entire development process. Even for systems below the SaMD threshold, clinical staff will not trust a model they cannot interrogate. Our healthcare AI engagements include explainability interfaces that allow clinicians to understand why the model produced a specific output, presented in clinical terms that clinical staff can evaluate — not feature importance scores that only data scientists can interpret.
What You Get
At the end of a healthcare technology engagement, you have a production system that integrates with your EHR environment through certified interfaces, processes clinical workflows in the sequence and with the data access patterns that clinical staff actually use, and maintains HIPAA compliance at every data access and transformation point. Your clinical staff have been through a structured onboarding that produced usable proficiency — not an 8-hour training session that clinical staff forget before they reach their first real patient interaction. Your CISO has the HIPAA Security Rule compliance evidence package for the new system.
The integration documentation includes: the HL7 and FHIR interface specifications for every EHR connection, the SMART on FHIR application configuration where applicable, the PHI data flow maps that satisfy HIPAA's required documentation of all PHI flows, and the technical safeguard implementation evidence. When your EHR vendor upgrades its API, your team has the documentation required to assess the impact and plan the update without requiring a vendor engagement to understand what was built.
How Our Engineers Deliver This
Our healthcare engineers are domain-qualified before they touch your codebase. They understand HIPAA at the architecture level, clinical workflows from experience, and FDA validation requirements from engineering practice. clinIQ and Vizier are embedded capabilities — clinical documentation intelligence and operational analytics shipped as standard in every healthcare engagement.
Relevant Compliance Frameworks
Engagement Models
Where We Deploy
Build vs. Outsource Decision Framework
A structured framework — with scoring — for deciding whether to build in-house, outsource, or adopt a hybrid model. Adapted for regulated industries where the cost of the wrong decision is highest.