A healthcare technology company built AI-powered clinical intelligence to catch documentation gaps before claims go out. ConnectHealth provided the SMART on FHIR interoperability layer, embedding the query workflow directly inside the provider's EHR session so the client's team could focus on the query experience and analytics itself.
Get in TouchA healthcare technology company applying AI-driven clinical intelligence to mid-revenue cycle performance for hospitals and health systems.
EHR-embedded clinical documentation query workflow, built on ConnectHealth's SMART on FHIR interoperability layer.
SMART on FHIR, FHIR, HL7v2, AWS (customer VPC)
Delivered
ConnectHealth handled authentication, FHIR and HL7 exchange, and data mapping, so the client's engineering team could focus on the query experience and documentation intelligence itself.
Clinical documentation queries flag missing diagnoses, incomplete procedure descriptions, or insufficient coding justification. Getting a response meant leaving the EHR entirely.
Providers had to leave their workflow, log into a second system, find their queries, and respond mid-patient-load.
Documentation stayed incomplete until claims time, and gaps that should have been caught upstream became denials downstream.
The documentation team and providers had no real-time way to collaborate on a query.
Every incoming HL7 and FHIR payload from the EHR had to be reshaped into the client's own data model before their application could use it.
SMART on FHIR authentication and HL7 MDM-based filing sit on ConnectHealth's reusable connector library, deployed inside the customer's own AWS VPC.
ConnectHealth used its SMART on FHIR embedded-app pattern to surface the documentation workflow inside the provider's existing EHR session, authenticating through SMART on FHIR so providers never left their chart or logged in twice.
ConnectHealth handled FHIR read/write and HL7v2 exchange, including an HL7 MDM-based workaround so every completed query auto-filed into the official record, working around a documented Epic DocumentReference API limitation. This ran on ConnectHealth's reusable FHIR and HL7 connector library, so the exchange layer didn't need to be rebuilt for each EHR the client might connect to next.
ConnectHealth's mapping engine transformed incoming FHIR resources and HL7 segments into the client's internal data model automatically, replacing what would otherwise be one-off, hand-written mapping code per data type and per EHR.
Helix AI, ConnectHealth's built-in integration assistant, took the query-and-response workflow requirements in plain language and produced a staged implementation plan for the integration engineer to review and approve before it was built out on the canvas, cutting down manual workflow design time. ConnectHealth was deployed inside the customer's own AWS VPC, keeping clinical data inside the customer's environment while supporting HIPAA-ready controls and BAA requirements.
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