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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 is an Artificial Intelligence (AI) Tech company.  Our Approach is to provide precise strategies that drive AI Medical Coding and Revenue Cycle Management (RCM) Systems, and Supply Chain Clarity.

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Your AI is Coding.  But what happens when it Codes Incorrectly?

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ARTIFICIAL INTELLIGENCE (AI) EXPERTISE 

We disrupt the Status Quo. As Independent AI Risk Advisors, we evaluate unsound risks and inefficiencies.  We use our Research Skills to Analyze AI and uncover hidden inconsistencies.

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​OUR CORE VALUES

  • People, Processes & Principles

  • Human In the Loop (HITL) Oversight​​​​​

 

SOLUTIONS:​​

  • Identifying errors that are hard to spot

  • Improving AI Throughput Outcomes

 

TRUSTED, UNBIASED LEADERSHIP & SUPPORT​​​

  • HIM, CDI, Revenue Integrity & RCM Functions

  • High Work Queue Volumes​

  • Unexpected Denial Spikes

  • CDI/Coding Disagreement Rates

    • Misaligned Benchmarks.

      • Caused by Hidden AI Risks 

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RISK MITIGATION PRODUCT

  • AI AuditME™, our RCM Intelligence Framework

    • Risk Identification, Discovery, and Disclosure

    • 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

THE AI AUDITME™ METHODOLOGY:

Our Methodology focuses on establishing Corporate,  

Accountable, Responsible, and Ethical Use of AI Initiatives:

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​​​​NAIC-ALIGNED STRATEGIES:

  1. Corporate Governance and Disclosure 

  2. Transparency 

  3. Risk Mitigation and Internal Control 

  4. Regulatory Oversight | Non-Compliance 

  5. AI Third-Party Systems and Data Risks​ 

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​​​​​​​​

AI-RCM AuditME™ FRAMEWORK

 

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 RCM SOLUTIONS:  

  • Eligibility Verifications 

  • Prior Authorizations

  • Clinical Documentation Integrity (CDI)

  • Charge Integrity 

  • AI Medical Coding 

  • AI Vendor Landscape 

  • AI Needs Assessment

  • AI Project Oversight 

  • BAA & SLA Alignment  

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