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Clinical Coding Specialist

Job Overview

Location

Remote (United States)

Job Type

Full-time

Category

HR & Recruiting

Date Posted

April 6, 2026

Full Job Description

đź“‹ Description

  • • As a Clinical Coding Specialist (Inpatient) at SmarterDx, you will play a critical role in validating and improving AI-driven diagnostic models by conducting comprehensive chart reviews and coding validation, ensuring clinical accuracy and supporting hospitals in recovering missed revenue and improving quality scores.
  • • Day to day, you will review and analyze medical records for coding accuracy, identify opportunities for improvement in AI coding models, apply ICD-10 and coding guidelines to assign appropriate diagnoses and procedure codes, participate in ongoing training to stay current with documentation standards, and contribute to process improvement initiatives that enhance coding practices and healthcare delivery efficiency.
  • • SmarterDx, founded by physicians in 2020 and part of Smarter Technologies, builds clinical AI that transforms how hospitals translate care into payment, helping health systems recover millions in missed revenue, improve quality scores, and appeal denials through its clinically intelligent platform.
  • • In this role, you will deepen your expertise in inpatient coding and AI validation, gain hands-on experience with cutting-edge healthcare AI systems, contribute to meaningful improvements in healthcare revenue integrity, and grow professionally through ongoing training and collaboration with a mission-driven team focused on optimizing the healthcare system.

Skills & Technologies

Remote
$75k-105k

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SmarterDx, Inc.
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About SmarterDx, Inc.

SmarterDx provides AI-powered clinical documentation integrity software that retrospectively reviews inpatient EHR data to identify missed diagnoses, quantify clinical quality gaps, and optimize revenue integrity for hospitals and health systems. Its machine-learning models surface overlooked conditions, prioritize cases for review, and feed corrected coding back to billing and quality teams, aiming to improve data accuracy, quality scores, and appropriate reimbursement without adding clinician workload.

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