top of page
AI Machine Learning (ML) in RCM

        Artificial Intelligence (AI) IN RCM   
     MEASURe | MONITOR | MONETIZE

Women Owned Business Certified Logo
Women Owned Business Certified Logo

Our
Approach

​​P3 Quality™ Audits AI-driven (Medicare /Medicaid) Medical Coding and Revenue Cycle Management (RCM) systems.  

​

Your AI is Coding.  But, is it Coding Correctly?

 

​We are an Artificial Intelligence (AI) Tech company.  As Independent AI Risk Advisors, we disrupt the Status Quo in RCM by uncovering hidden errors before they become denials, compliance risks, or lost revenue.​

​​

FRAMEWORKS BUILT FOR HEALTHCARE 

  • Advancing Human In the Loop (HITL) Oversight​

​​

​OUR CORE VALUES

  • People, Processes & Principles​​​​​

 

AUTOMATED CODING SYSTEMS​​

  • Improves Throughput​

  • But introduce errors that are hard to spot

 

​​​UNCOVER HIDDEN AI RISKS:

  • Revenue Integrity, HIM, CDI & RCM Functions

  • ​​ If you are still seeing high work queue volumes​

  • Unexpected Denial Spikes, CDI/Coding Disagreement Rates

  • That Do Not Align with your Benchmarks.

    • Hidden AI Risks may be the cause​

​​ â€‹â€‹â€‹â€‹

P3 FLAGSHIP RISK MITIGATION PRODUCT

  • AI AuditME™ is an RCM Intelligence Layer

  • Risk Identification, Discovery and Disclosure Engine

    • See How It Works | No Obligation 

 

P3 QUALITY is WBE / WBENC certified and a Georgia (SBSD) Certified Small Woman-Owned Business.

​

Our
Methodology

THE AI AUDITME™ METHODOLOGY:

Corporate, Accountable, Responsible, and Ethical

Use of AI Initiatives:

​

​​​​NAIC-ALIGNED STANDARDS

  1. Corporate Governance and Disclosure 

  2. Tranparency

  3. Risk Mitigation and Internal Controls

  4. Regulatory Oversight

  5. Third-Party AI Systems and Data​ 

AI-RCM PERFORMANCE GAPS

HUMAN-CENTRIC AI

In Human-Centric AI, the Smart AI assistants should adhere to prompts and commands without deviating.  Sometimes AI Becomes Intelligently Disobedient (Ignoring Instructions)

​​

DEMOGRAPHIC ERRORS​

  • While AI can Automate Tasks

  • Issues Arise from Flawed Input Data 

    • System Hallucinations â€‹

    • Algorithmic Bias

    • Trust in Automation Erodes

​

THE QUALITY/COMPLIANCE RISK IMPACT

  • AI Predictive Models and Generative AI should flag any risks. But it Misfires 

    • Causing Inaccurate Forecasting​​

    • False Negatives/Positives

    • Model Drift (AI Degrades over time)

​​

AI EXPLAINABILITY/DEFENSIBILITY 

  • AI Systems Operate as "Black Boxes"

    • Failures lead to Denial Spikes

    • Rework causes High Operational Costs​
    • Significant Risk Exposure 

 

MAJOR BARRIER

  • Selective Transparency​

​

​​​​​​

AI-RCM AuditME™ SOLUTIONS 

 

AI GOVERNANCE 

Automate and Regulate Checks/Balances

  • Coding/CDI Governance ​​​​​

  • Risk Exposure 

  • Compliance

​​​

AI ETHICS

  • Human-In-The-Loop (HITL) Oversight

  • Transparent AI & Explainability Standards

  • Algorithmic Bias Audits & Monitoring 

​​​​​

AI POLICY â€‹â€‹

Initial Policy Strategies:

  • Standardized Data & Privacy Rules

  • Human Sign Off on AI-RCM Denial Patterns/Processes

  • AI-Driven Transparency and Accountability Reporting 

​​

RESPONSIBLE AI: 

  • An Internal Authority has to be the AI Standard Bearer

    1. Set an AI Quality Bar​

    2. Own the AI Policy Framework

    3. Bridge AI Ethics and Operational Gaps

    4. Champion Governance Across Functional Areas

    5. Schedule, Measure, and Monitor AI Audit Activities

  • Implement AI Impact Assessment Requirements 

  • Codify Core AI Development Standards

  • Establish Third-Party Data & Systems Accountability

​​

​​

​​

END-TO-END AI-DRIVEN REVENUE INTEGRITY | HIM | CDI & RCM   

  • Eligibility Verifications 

  • Prior Authorizations

  • Clinical Documentation Integrity (CDI)

  • Charge Integrity 

  • AI Medical Coding 

  • AI Vendor Selection 

  • AI Needs Assessment

  • AI Project Oversight 

  • BAA & SLA Review 

image.png
bottom of page