Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Healthcare Services • 240 Provider Clients

Case Study 38: Medical Billing Company

Marketing-optimized case study for Canadian SMBs.

$218,000
Annual Savings
370%
ROI
3.9 months
Payback
Healthcare Services • 240 Provider Clients
Industry Focus

Executive Summary

Automated Coding: AI analyzes provider notes and assigns appropriate CPT/ICD-10 codes with 94% accuracy. Flags complex cases for certified coder review.

Challenge

Solution

  • Automated Coding: AI analyzes provider notes and assigns appropriate CPT/ICD-10 codes with 94% accuracy. Flags complex cases for certified coder review.
  • Claim Scrubbing: ML model identifies common rejection reasons before submission. Corrects errors automatically or routes to billing specialist.
  • Denial Management: AI analyzes denial patterns, generates appeal letters, and tracks resubmissions. Learns from successful appeals.

Technical Stack

Key Metrics

8
Coding time reduced from 8 minutes to 45 seconds per encounter
87%
First-pass claim acceptance rate improved from 87% to 96%
11%
Denial rate reduced from 11% to 4%
38%
Appeal success rate improved 38%
8
Eliminated 8 FTE billing specialist roles ($218k savings)

Implementation Timeline

14 weeks including model training and compliance validation

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