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

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Your AI Processes are Inconsistent. What are you doing to resolve the issue(s)? 

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ARTIFICIAL INTELLIGENCE (AI) EXPERTISE 

As Independent AI Risk Advisors, we evaluate unsound risks and inefficiencies. We Audit Heathcare and AI-RCM system patterns, trends, processes, and procedures. We disrupt the Status Quo!

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​OUR CORE VALUES

  • People, Processes & Principles

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

 

SOLUTIONS:​​

  • Identifying errors/inconsistencies that are hard to spot

  • Improving Healthcare and AI-RCM Outcomes

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TRUSTED, UNBIASED LEADERSHIP & SUPPORT​​​

  • Healthcare, HIM, CDI, Revenue Integrity & RCM Quality 

    • CMS, MAC, HIPAA, PHI, TPA, etc

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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.​

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

 

GOVERNANCE 

Automate and Regulate Checks/Balances

  • Coding/CDI Governance ​​​​​

  • Risk Exposure 

  • Compliance

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ETHICS

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

  • Transparent AI & Explainability Standards

  • Algorithmic Bias Audits & Monitoring 

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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-RCM SOLUTIONS:  

  • Audits 

  • AI-RCM HITL Oversight

  • AI-RCM Maturity Model

  • Client Success Optimization

  • Denial Prevention 

  • Governance 

  • Patient Financial Experience  

  • Payer Intelligence 

  • Technology Vendor Landscape

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