Case Study: Manual vs. AI-Assisted Radiology Coding

Overview

Customer: Major Healthcare Network, USA

Company: Artigen

Industry: Healthcare

Date:
May 2025

Major Healthcare Network, a 250-bed facility in a bustling region, struggled with manual radiology coding within their Electronic Health Record (EHR) system. Errors in Current Procedural Terminology (CPT) code selection, missed modifiers, and incomplete documentation led to frequent claim denials, delayed reimbursements, and significant revenue loss. Artigen deployed Conrad AI, an AI-assisted radiology coding solution, to overhaul the hospital’s coding process. This case study compares manual versus AI-assisted radiology coding, emphasizing transformative outcomes derived from real-life data, distinct from anesthesia coding challenges.

Challenges Faced in Manual Radiology Coding

Manual radiology coding involved coders manually reviewing imaging reports to assign CPT codes, modifiers, and ensure payer compliance. Unlike anesthesia coding, radiology coding focuses on imaging modalities, anatomical specificity, and technical/professional components rather than time units or patient physical status. The process was error-prone, particularly in documenting contrast use, laterality, and multi-region studies. Major healthcare Network faced high claim denial rates, undercoding, and non-compliance with Centers for Medicare & Medicaid Services (CMS) and American College of Radiology (ACR) guidelines, resulting in financial and operational inefficiencies.

Manual Coding Metrics (Pre-Conrad AI)

Period Analyzed: Q1 2024
Total Procedures Coded: 2,300
Average Revenue per Procedure: $110 (based on average reimbursement rates)
Claim Denial Rate: 21%
Average Coding Time per Procedure: 14 minutes
Annual Revenue (Estimated): $1,012,000

Key Issues:

Missed modifiers (e.g., -26 for professional component, -TC for technical component, -LT/-RT for laterality).
Undercoding of complex imaging studies (e.g., omitting contrast or multi-view codes).
Incomplete documentation of clinical indications and anatomical details.
Increased administrative burden from claim rejections and resubmissions.

Example Manual Coding Data

Solution: AI-Assisted Coding with Conrad

Artigen introduced Conrad, a cutting-edge AI-powered coding assistant tailored for radiology and integrated with the hospital’s EHR system. Unlike anesthesia coding, which emphasizes time-based units and patient comorbidities, Conrad focuses on imaging-specific details, such as modality, contrast administration, and anatomical precision. Using natural language processing (NLP) and machine learning, Conrad analyzes radiology reports in real-time, extracting details like imaging type, region, and clinical indications to suggest accurate CPT codes, modifiers, and documentation requirements, ensuring compliance with CMS and ACR standards.

Implementation Details

Timeline: Deployed in Q2 2024, fully operational by Q3 2024.
Training: Coders underwent a 2-week training program to master Conrad’s intuitive interface.
Integration: Seamless compatibility with Cerner EHR, supporting real-time coding during report generation.

Features:

Automated Code Suggestions: Recommends CPT codes and modifiers based on imaging report content and clinical context.
Real-Time Documentation Validation: Ensures accurate documentation of contrast, laterality, views, and clinical indications.
Coder Assist: Provides AI-driven guidance for complex studies, such as multi-region or combined modality imaging.
Audit Trail: Maintains a record of coding decisions for payer audits and compliance reviews.

AI-Assisted Coding Metrics (Post-Conrad)

Period Analyzed: Q3 2024
Total Procedures Coded: 2,700
Average Revenue per Procedure: $170 (based on average reimbursement rates)
Claim Denial Rate: 5%
Average Coding Time per Procedure: 5 minutes
Annual Revenue (Estimated): $1,836,000

Key Improvements:

Accurate application of modifiers (e.g., -26, -TC, -LT/-RT for laterality).
Comprehensive coding for complex studies, including contrast, multi-view, and multi-region imaging.
Improved documentation of clinical indications and anatomical specificity.
Reduced administrative burden from fewer claim denials.

Example AI - Assisted Coding Data

Comparative Analysis

Qualitative Improvements

Error Reduction: Conrad eliminated undercoding and missed modifiers, capturing all billable components specific to radiology, unlike anesthesia’s focus on time units.
Staff Satisfaction: Coders reported reduced stress and greater confidence due to AI-guided workflows tailored for imaging studies.
Payer Relations: Lower denial rates improved reimbursement timelines, enhancing financial stability.
Scalability: The hospital handled a 17% increase in procedure volume without additional staffing.
Clinical Accuracy: Enhanced documentation supported better diagnostic reporting, aiding radiologist workflows.

Key Benefits of Conrad

1. Precision Coding:

Conrad ensures accurate CPT codes and modifiers for radiology-specific procedures, maximizing reimbursement.
Example: Adding -26 and -TC modifiers for an MRI knee study increased revenue by $120 per procedure.

2. Efficiency Gains:

Coding time dropped by 64%, from 14 to 5 minutes per procedure, enabling coders to process 3.3x more cases.
Real-time suggestions eliminated manual searches for radiology coding guidelines, saving hours weekly.

3. Revenue Optimization:

Average revenue per procedure rose by 55%, adding $60 per procedure.
Annual revenue increased by $824,000 for 2,700 procedures, an 81% uplift.

4. Provider and Coder Empowerment:

Real-time coding support minimized administrative burden, allowing radiologists to focus on image interpretation.
Coders gained proficiency in complex imaging studies through AI-driven insights, distinct from anesthesia’s patient-status focus.

Net Impact

Real-Time Coding Support: Conrad enabled concurrent coding during report generation, improving accuracy and speed for radiology-specific workflows.
Accurate CPT/Modifier Mapping: Eliminated errors in code selection, modifier application, and documentation, tailored to imaging modalities.
CMS and ACR Compliance: Ensured all codes met regulatory and payer standards for radiology.
Coder Productivity: 3.3x increase, freeing resources for quality assurance and training.
Revenue Impact: $824,000 additional annual revenue for 2,700 procedures.
Elimination of Provider Abrasion: Fewer claim denials and appeals reduced friction with payers and staff.
Agnostic EHR Integration: Seamless compatibility with Cerner, Epic, and other EHR platforms.
Net Revenue Increase per Procedure: ~$60 per procedure.
Time to Close EHR Gaps: Less than 5 minutes per patient, down from 14 minutes.
Clinical and Financial Alignment: Accurate coding supported improved diagnostic documentation and resource allocation.

Conclusion

The deployment of Conrad by Artigen at Major Healthcare Network transformed radiology coding, delivering precision, efficiency, and revenue optimization distinct from anesthesia coding challenges. By addressing inaccuracies in CPT code assignment, modifier usage, and documentation specific to imaging studies, Conrad increased average revenue per procedure by 55%, reduced claim denials by 76%, and tripled coder productivity. The ability to close coding gaps in under 5 minutes per patient streamlined operations, reduced administrative burden, and empowered radiologists to prioritize diagnostic excellence. This case study highlights the transformative potential of AI-driven coding in radiology billing, establishing a benchmark for healthcare providers seeking financial and operational excellence.

Case Study: Manual vs. AI-Assisted Radiology Coding

Overview