Our
Approach
WHO WE ARE:​
​P3 Quality™ is a Healthcare Tech company specializing in designing AI AuditME™ Methods. We work with healthcare leaders and organizations to develop AI-driven revenue cycle management (RCM) Mitigation and Optimization (MO) Frameworks, as well as provide Consulting Services.
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P3 Core Values | People, Processes & Principles
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WHAT WE DO:​
P3 Core Focus | AI in RCM Audits
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RCM Consulting (Revenue Integrity, CDI & HIM)
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AI Audits, Mitigation & Optimization
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​DNFB Intelligence, Mitigation & Optimization
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​​The P3 Quality™ Quotient = Quality First | Quality Forward ​​​​​
​​​​We are proud to be certified nationally by the Women's Business Enterprise National Council (WBENC).
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Our
Methodology
METHODS:
AI in RCM and Responsible AI AuditME™ Frameworks:
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
PAPER MEDICAL RECORDS
Eligible, Compliant & Safe Discard
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ORGANIZATIONAL RETENTION
Destruction Validation
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Record Identification & Eligibility
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Retention Schedule Validation
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Completeness & Conversion Validation
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Quality Assurance Before Destruction
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Privacy, Security & Compliance Checks
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Inventory & Tracking Standards Review
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ELECTRONIC MEDICAL RECORDS
Claims Data Validation ​
Ensure All Data Element Are Accurate
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Patient Identity & Demographics
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Admission, Encounter & Visit Information
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CDI Documentation Elements
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Diagnosis?
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(e.g., ICD-10 Codes)
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Medically Necessity Alerts?
(payer LCD/NCD policies)
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H&P, CPT Codes, Charge Notes, etc.​
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CHARGE CORRECTION
AI CODING/CDI AUTOMATION RULES
Charge Errors
When CDI Automation Flags a Charge Issue that requires Correction, is the root cause of the inconsistency analyzed? Was it an AI or a human error?
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When Routed to the CDI/Coding Teams
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Are AI documentation/coding inconsistencies reviewed, verified, validated, and discussed with the vendor? ​
AI AUTOMATION DRIFT​​
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AI Model's Accuracy
AI Algorithm Drift occurs when an AI Model's accuracy or decision quality decreases over time. Real-world data, patterns, or rules evolve, causing the model to become misaligned with current clinical data, coding, and billing standards. Who is measuring and monitoring this?
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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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