Explainable AI for Healthcare RCM

A PE-Backed Healthcare RCM Provider Turns a Black-Box Engine into an Explainable AI Foundation

Modernizing legacy RCM decision logic into a validated, governed foundation for AI.

Rules decoded and rebuilt

95 %+

ML-to-rule parity

14 -Project

AI mandate

About

A PE-backed healthcare Revenue Cycle Management provider ran daily collections decisions on millions of patient accounts through a legacy decision engine with no documentation or explainability. Veltris combined RCM domain expertise, explainable AI engineering, and phased delivery to transform the black-box system into a validated, AI-ready foundation.

Trusted AI starts with understanding the decisions already driving the business.

Challenge

Modernizing RCM decisions without losing control of the logic.

The organization relied on a legacy Propensity-to-Pay engine with end-of-life infrastructure, limited documentation, and no explainability for auditors or operators. More than 45 business rules ran daily, but only 12 were documented, creating operational, compliance, and modernization risks.

PAIN POINTS

Undocumented legacy decision logic
Limited explainability for operators and auditors
End-of-life infrastructure
Need to modernize without disrupting collections operations

Solution

Explainable AI built around validated decision logic.

Veltris combined black-box rule mining with structured human validation to decode the existing decision logic and create a fully explainable Validated Rulebook signed off by operations teams. The solution used regression and scorecard models, with SHAP-based reason codes applied across predictions to provide transparent explanations.

A HIPAA-ready, on-premises architecture kept the engagement within the client’s environment, while a phased, gated approach across Discovery, Pilot, Validation, and Scale established formal parity gates and human-in-the-loop governance.

Step Flow
Decode → Validate → Model → Govern → Scale

Explainability turns legacy decision logic into a foundation for responsible AI.

WHAT CHANGED IN THE WORK

Black-box rules → Validated Rulebook
Undocumented decisions → Explainable decision logic
Manual validation → Structured human-in-the-loop governance
Pilot initiative → Sequenced AI mandate

WHAT WE DELIVERED

Explainable AI Engineering
Validated Rulebook
SHAP-Based Reason Codes
ML Model Governance

Ready to turn legacy RCM decision logic into an AI-ready foundation?

Results

Validated decisions. Explainable AI. A clear path to scale.

KEY RESULTS DELIVERED

business rules decoded and rebuilt with full explainability

95%+

ML-to-rule parity with 0.8+ AUC validated and 100% SHAP reason-code coverage

$97K

six-week pilot expanded into a 14-project AI mandate

Why Veltris

Veltris combined RCM domain expertise, explainable AI engineering, model governance, and phased delivery to modernize a critical collections decision engine without relying on lift-and-shift or rip-and-replace. By validating existing decision logic before introducing new models, Veltris established a governed foundation that operations teams could understand, approve, and scale.

CAPABILITIES APPLIED

Explainable AI Engineering
Propensity-to-Pay Modeling
HIPAA-Ready On-Premises Architecture
SHAP-Based Reason Codes
ML Model Governance
Phased Gated Delivery

Modernizing a legacy collections engine without compromising daily operations is a hard ask: it demands engineering rigor and deep domain understanding. Veltris brought both. They decoded our existing rules with full transparency, delivered explainable AI logic our auditors and operators could trust, and built the foundation for a scalable, AI-ready platform.

Chief Operating Officer PE-backed healthcare revenue cycle management company

See how explainable AI can modernize critical RCM decisions.