The Origin Story
HDIM is the brainchild of a public healthcare IT architect — the AI-native infrastructure he, his team, management, and customers always wanted, but could never afford. Until now.
Purpose-built for the CMS ideals of better care, smarter spending, and healthier people — not retrofitted from a generic analytics stack.
Built by one architect, with AI as the engineering partner.
HDIM is the brainchild of a public healthcare IT architect with 15+ years building data infrastructure inside public health systems and HIEs — hearing the same conversation repeat every budget cycle. The team wanted better tooling. Management wanted it. Customers asked for it. And the answer was always the same: we can't afford to build that. AI changed the math. Working as a full engineering partner from day one, AI let one architect design, build, and validate the AI-native platform that a whole team would once have needed years and a much larger budget to deliver. Real-time, FHIR-native infrastructure engineered around CMS's own quality triad — better care, smarter spending, healthier people. Not a dashboard bolted onto a legacy warehouse. Not a compromise dictated by what the budget would allow. The system he, his team, management, and customers had always wanted — finally built, because AI made it affordable.
Healthcare organizations lose momentum in quality programs when data arrives late and workflows aren't built for how care actually happens.
We chose to build a real-time execution layer that connects the clinical reality to the CMS ideals of better care, smarter spending, and healthier people.
Most healthcare data platforms — including many built earlier in my own career — inherited their core architecture from banking and financial-services systems: batch-oriented, request/response, built around discrete transactions settled at the end of a cycle. Healthcare doesn't run on a nightly settlement cycle. Care happens continuously — dozens of encounters, referrals, labs, and admissions landing concurrently, all day, from every facility in a region. Forcing that reality through a banking-shaped pipeline is exactly why regional HIEs and quality platforms always feel a day behind. Drawing on 15+ years as a healthcare IT architect, I designed HDIM's execution layer around CQRS and event sourcing from day one — an event-driven data pipeline engine purpose-built so many customers can pull and process data concurrently, as it happens, instead of waiting on the next batch window.
commit refactor(privacy): Reduce PHI cache TTL to limit exposureWe chose harder engineering, privacy-by-design, and patient-first defaults. That's the difference — execution with integrity.
The Four Non-Negotiables
“You can't "move fast and break things" when the things you break are people's lives.”
Configurable PHI cache limits and audit-ready privacy controls.
“We chose openness when we could have chosen control.”
Built on HL7 FHIR R4, SMART on FHIR, and open healthcare standards.
“Untested code in healthcare is unethical.”
Extensive test coverage on critical paths with measure validation workflows.
“Every clinical decision is backed by peer-reviewed research.”
All quality measures derived from NCQA HEDIS, CMS, and clinical guidelines.
Live Portal
Built for real clinical workflows — every screen from our live portal.
Population-level risk scoring and cohort analysis
Urgency-sorted alerts with patient slide-out panel
Automated quality reporting and QRDA export
HDIM combines deep healthcare IT expertise with AI-native engineering — not a large team, a focused one.
Founder & Chief Architect
Healthcare IT · 15+ Years
A public healthcare IT architect who spent 15+ years watching teams, management, and customers all want the same AI-native infrastructure — and never being able to afford it. Closed that gap by designing, building, and validating HDIM in direct partnership with AI, end to end. Brought real-time, FHIR-native quality measurement from concept to a live, four-layer intelligence platform.
Design, Build & Validation
AI-native by construction
HDIM was not built with AI bolted on afterward — it was designed, implemented, and validated with AI as a full engineering collaborator from the first architecture decision, making transparency for both humans and machines the organizing principle of the whole stack.
Clinical Accuracy
HEDIS · CQL · FHIR R4
Every quality measure, care gap rule, and risk score traces back to NCQA HEDIS, CMS quality programs, and open HL7 FHIR R4 standards — never an in-house black box.
Security & Compliance
HIPAA-aligned · SOC 2 readiness
Privacy, auditability, and tenant isolation are architectural invariants, not an afterthought — PHI stays inside the regional boundary and sensitive text is redacted before it ever reaches a model.
Our path from pilot to enterprise general availability. Built iteratively with customer feedback at every stage.
See how HDIM can help your organization achieve quality measurement excellence.