Our
Approach
WHO WE ARE:​
​P3 Quality™ is a Healthcare Tech company specializing in AI
In Revenue Cycle research, development, and issue resolution management.
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AI AuditME™ Framework & Method product designs:
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Significantly invested in AI-driven technologies?
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Still seeing high volumes of documentation and coding denials?
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Rework consuming ROI gains?
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You have questions? | We have Answers!
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OUR CORE VALUES:
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People, Processes & Principles
OUR SOLUTION:
Detect, Diagnose, Resolve & Improve
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AI Model Data Drift & Hallucinations​
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Automated Coding Abstraction & Validation
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Quality Assurance | Quality Control Inconsistencies
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KEENLY FOCUSED ON:​
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Revenue Integrity, HIM, CDI & RCM Functions
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Advancing Human In the Loop (HITL) Oversight
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Standardizing Broken Processes
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Building Frameworks ​
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P3 QUALITY is a WBE, nationally certified by the Women's Business Enterprise National Council (WBENC).
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Our
Methodology
OUR METHODS:
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
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HEALTH DEMOGRAPHICS VALIDATION WORKFLOW ​
Patient Encounter or Hospital Admission Data
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Inaccurate Demographics
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​Cause AI in RCM Downstream Issues
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Misalignment with Payer Requirements
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QUALITY/COMPLIANCE AUDIT RISKS
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Undercoding (Patient presented as less sick)
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Overcoding (Patient's illness presented as more severe) ​​
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ROOT CAUSE OF AI INCONSISTENCIES
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Patient Data Models
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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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MEASURE CHARGE ERRORS
AI CODING/CDI AUTOMATION
Inconsistent CDI Flag Errors
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​Upcoded/Undercoded E/M Levels​​​​
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Incorrect Procedural Hierarchies
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Misassigned Chronic Conditions
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AI ERRORS CREATE
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Quality/Compliance Exposure
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Payer Audit Risks
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Ethical/Legal Implications
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THIS STEMS FROM
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AI Drift and Hallucinations​
MONITOR AI DRIFT & HALLUCINATIONS​​
AI Algorithm errors erode trust between patients, providers, and payers:
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Coding Errors Increase
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Providers Lose Confidence in Automated Coding Systems
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Patients Dispute Bills
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RCM Leaders Distrust Their AI Investment
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MONETIZE KEY PERFORMANCE INDICATORS (KPIs):
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Accuracy Variance (AI model output)
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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 Inconsistencies
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​Unexpected MUE, CCI, HCC, or DRG shifts
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Payer Rule Incompatibilities
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