An AI coding and revenue cycle platform for orthopedic practices needed its recommendations to appear where coders already work: inside the EHR. ConnectHealth supplied the SMART on FHIR interoperability layer that made that possible, reading encounter data in and writing approved codes back out.
Talk to UsAn AI coding and revenue cycle platform serving orthopedic practices
SMART on FHIR EHR integration, encounter read/write, multi-practice interoperability
SMART on FHIR, FHIR/EHR APIs, React, Java, NestJS, Python LLM Services
Delivered
ConnectHealth removed EHR interoperability from the team's critical path, so engineering effort went into coding intelligence instead of integration plumbing.
Every coding decision depended on information that lived in separate systems, and every new EHR connection risked becoming its own integration project.
Coders and billers had to review diagnosis details, clinical documentation, and prior visits by hand before deciding what to bill, a process directly connected to claim denials.
Recommendations needed the patient and encounter already open in the EHR, and approved codes needed a path back into that same encounter.
Each new orthopedic practice introduced another EHR connection that had to be supported without turning onboarding into a custom integration project.
SMART on FHIR interoperability paired with a custom coding and revenue cycle application layer.
ConnectHealth supported the interoperability layer behind the SMART on FHIR application, launching the coding experience with patient and encounter context already available. Coders worked without leaving the chart or copying information between systems.
The integration layer supported the EHR data exchange required by E/M Levels and Auto-Coder. Encounter and diagnosis information moved into the coding workflow, and approved procedure-code recommendations wrote back against the encounter in one click.
ConnectHealth provided a reusable interoperability foundation across customer EHR environments, including Athenahealth and eClinicalWorks, instead of requiring the product team to treat every practice connection as a separate integration effort.
PHI was redacted before clinical content reached the LLM, while the application layer handled E/M validation and procedure-code generation. ConnectHealth stayed focused on getting the right EHR context into and out of those workflows.
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