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AI Machine Learning (ML) in RCM

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

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Women Owned Business Certified Logo

Our
Approach

 

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

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

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We are an Artificial Intelligence (AI) Tech company. We disrupt the Status Quo in RCM by uncovering hidden errors before they become denials, compliance risks, or lost revenue. â€‹

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BUILT FOR HEALTHCARE 

  • Advancing Human In the Loop (HITL) Oversight​

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​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​

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P3 FLAGSHIP RISK MITIGATION PRODUCT

  • AI AuditME™

    • See How It Works | No Obligation 

 

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

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Our
Methodology

AI AUDITME™ METHODOLOGY:

Compliant, Accountable, Responsible, and Ethical Use of AI:

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​​​​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)

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DEMOGRAPHIC ERRORS​

  • While AI can Automate Tasks

  • Issues Arise from Flawed Input Data 

    • System Hallucinations â€‹

    • Algorithmic Bias

    • Trust in Automation Erodes

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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)

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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​

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AI-RCM AuditME™ SOLUTIONS 

 

AI GOVERNANCE 

Automate and Regulate Checks/Balances

  • Coding/CDI Governance ​​​​​

  • Risk Exposure 

  • Compliance

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AI ETHICS

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

  • Transparent AI & Explainability Standards

  • Algorithmic Bias Audits & Monitoring 

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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 

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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

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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 

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