Our
Approach
WHO WE ARE:​
​P3 Quality™ is a Healthcare Tech company specializing in conducting AI In Revenue Cycle Management (RCM) research and designing AI AuditME™ Frameworks and Methods.
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WHAT WE DO:
We extract and diagnose core Drift in AI Medical Coding Models.
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Quality Specs + Compliance Checks
= Revenue Optimization Success
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OUR CORE VALUES | People, Processes & Principles
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​OUR CORE FOCUS | Auditing AI Frameworks
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Revenue Integrity, HIM, CDI & RCM Consulting
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Auditing AI, Mitigation & Optimization
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​DNFB Intelligence, Mitigation & Optimization
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​​ ​​​​​​​​We are nationally certified by the Women's Business Enterprise National Council (WBENC).
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Our
Methodology
OUR METHODS:
AI in RCM and Responsible AI AuditME™ Workflows:
Compliant, Accountable, Responsible, and Ethical Use of AI:
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NAIC-Aligned Standards:
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Governance
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Transparency
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Accountability & Responsibility
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Data Quality, Privacy & Protections
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Testing & Validation
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Security & Risk Mitigation
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Third-Party BAA & Vendor Risks
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Consumer Protections
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Ongoing Audits, Monitoring & Maintenance​
MEDICAL RECORD VALIDATION
ELECTRONIC MEDICAL RECORDS
AI Misinterpretations & Misclassifications start with the
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HEALTH DEMOGRAPHICS & DATA VALIDATION WORKFLOW ​
Patient Encounter or Hospital Admission
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Inaccurate Demographic Data Collected
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​Cause AI in RCM Downstream Issues
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Misalignment with Payer Expectations
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QUALITY/COMPLIANCE AUDIT RISKS
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Undercoding (Patient appears less sick)
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Overcoding (Patient symptoms appear more complex)
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ROOT CAUSE OF INCONSISTENCIES
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Inaccurate Patient Data
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Wrong Diagnosis Codes
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Incorrect Procedure Codes
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Improper Modifiers
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Missing Comorbidity Codes
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Incorrect Severity or Risk Scores
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MEASURING CHARGE ERRORS
AI CODING/CDI AUTOMATION
Inconsistent CDI Flags Cause Charge Errors
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​Upcoded E/M Levels​​​​
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Incorrect Procedural Hierarchies
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Misassigned Chronic Conditions
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THESE MISTAKES CREATE
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Quality/Compliance Exposure
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Payer Clawback Risks
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Potential Legal Implications for Providers
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ALL OF THIS STEMS FROM
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The drift in how AI reads, interprets, and classifies Clinical Documentation.​
MONITORING AI ALGORITHM DRIFT​​
AI Algorithm Drift erodes trust between patients, providers, and AI in RCM Augmented Systems:
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Coding Errors Increase
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Providers Lose Confidence in Automated Coding
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Patients Question Bills
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RCM Leaders Begin to Distrust Their AI Investment
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MONETIZING KEY DRIFT INDICATORS (KDIs):
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Accuracy Variance (AI model output vs. validated truth)
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Coding/CDI Disagreement Rates
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Claim Denial Spikes tied to AI-Generated logic
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Charge Capture Changes > 10 YOY w/o Clinical Justification
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DNFB Increase linked to AI Routing or Edits
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​Unexpected MUE, CCI, HCC, or DRG shifts
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Payer Rule Incompatibilities Detected
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