Coding Suggestions That Land Inside the Chart, Not Beside It

An AI coding and revenue cycle platform built for orthopedic practices, embedded directly inside the EHR. A SMART on FHIR app validates E/M levels and suggests procedure codes with one-click writeback, so a coder never leaves the chart to see a suggestion.

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Customer Focus

Orthopedic practices, whose coding and billing teams reconstruct visit detail from encounter notes to catch denials before they happen.

Scope

EHR-embedded coding intelligence layer (E/M validation, Auto-Coder, Patient History) plus a multi-tenant admin platform for practice configuration.

Stack

React, Java, NestJS, Python for LLM services, SMART on FHIR, Athenahealth and eClinicalWorks integrations.

The Problem

Denials Start Long Before a Claim Is Submitted

Billers and coders read through encounter notes and diagnosis detail to reconstruct what happened in a visit, then decide what to bill. It is slow, it varies by person, and it is where denials start.

01
Manual chart reconstruction is slow and inconsistent

Coding decisions depend on a person reading through notes to reconstruct a visit, so accuracy and speed vary by who is doing the reading.

02
Denials are the expensive end of the problem

A documentation error caught at the point of coding costs a fraction of fighting the same error at the payer after a denial delays payment and triggers an appeal.

03
Placement decides whether a coding tool gets used

A separate coding tool that requires copying encounter data across screens reintroduces the friction it was meant to remove, which is where most clinical AI products lose adoption.

The Tech Stack

Built as a SMART on FHIR app on a multi-tenant, database-per-practice backend.

  • React
  • Java
  • NestJS
  • Python (LLM services)
  • SMART on FHIR / SMART App Launch
  • Athenahealth
  • eClinicalWorks
  • Multi-Tenant, Database-per-Practice
What We Built

Four Tools, One Embedded App

E/M Levels

A coder selects an encounter inside the EHR. The backend pulls it through the EHR APIs, and an LLM checks whether the assigned evaluation and management code and level match what the physician actually documented.

  • Validates the assigned E/M code and level against documented encounter and diagnosis detail
  • Pulls encounter context automatically through the EHR APIs once a coder selects it
  • Returns a pass or fail report on the assigned code and level
  • Runs in an end-of-day batch cycle so results are ready when the coding team picks up the work

Ready to get AI coding suggestions in front of coders without pulling them out of the chart?

Talk to us about what a SMART on FHIR build looks like against your EHR.

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Let’s #Transform Healthcare,# Together.

Partner with us to design, build, and scale digital solutions that drive better outcomes.

Location

Global Tech Teams LLC, 525 Washington Blvd, Industrious at Newport Tower, Jersey City, NJ 07310, United States.

Contact

+1 408 786 5974
contact@mindbowser.com
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