Our
Approach
​P3 is a team of Corporate AI Responsibility Professionals who work with business partners to evaluate, develop and revise AI Governance Framework for AI Product, Policy and Process Improvements in health IT, revenue cycle management (RCM), medical coding, and documentation integrity environments.
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We are Certified AI Professionals and ISO 9001:2015-Certified Internal Auditors.
We use a proprietary compliant, accountable, responsible, ethical, solution systems (C.A.R.E.S.S.).
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P3 Quality Control Approach:
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Front End: Evaluate Eligibility Verification and Prior Authorization performance against AI Best Practices
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Mid Cycle: Assess Compliant AI Documentation Integrity/Medical Coding (i.e., ICD-10, CPT & HCPCS) data integrity and charge reconciliation coding/audit tools.
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Back End: Minimize unintended AI related Claims and Denials Management ethical implications and risks.​
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Our
Methodology
Responsible AI-Powered coding/CDI technologies should achieve optimal performance results. But what happens when AI Medical Coding Accuracy falls short? Track AI algorithm and software inconsistencies.
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P3 C.A.R.E.S.S. Quality Model; Compliant, Accountable, Responsible, and Ethical AI.
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Our Quality Control Focus
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AI Governance Strategy
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AI in RCM Coding Analysis
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AI Coding Algorithm and Rules Review
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AI CDI Quality Checks
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Inspect, Detect & Assess Inconsistencies
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Leverage Analytics and Reporting Data
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Determine RCM Performance & Revenue Implications
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Additional Service Offerings
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Assess Outsourced Vendor Performance
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Evaluate Service Level Agreements (SLA)
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SLA Metrics Review
AI REVENUE CYCLE MANAGEMENT (RCM)
What is AI RCM exactly?
AI in revenue cycle management (RCM) leverages advanced technologies like Machine Learning (ML), Generative AI (GenAI) and Large Language Models (LLMs) to optimize coding, CDI financial processes in healthcare:
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P3 Quality Eyes are on AI!​
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​​Key Concerns:
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AI Algorithm Data Errors
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Inaccurate Insurance Verifications
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Diagnosis Prediction Inconsistencies
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Procedure Code Discrepancies
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Charge Integrity Issues
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Denials Tracked, but not Worked Timely
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AI Black Box Predicaments
​​AI MEDICAL CODING
​What about AI Medical Coding?
AI in Medical Coding uses ML, GenAI and LLM algorithms are trained to automatically analyze documentation and assign medical codes.
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How does AI Algorithm Medical Coding Bots read, review, interpret and translate complex medical terminology to assign codes accurately?
What is the nature of AI Medical Coding Black Box Algorithm vulnerabilities?
The Medical Coding Algorithm Issue!​
The Coding ​Quality Challenge:​
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AI Makes Specific Coding Decision
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Using Nuanced Medical Documentation
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Minimal Clinical Contextual Understanding
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Lacks Ambiguous Terms Clarity
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Leads to Interpretation Discrepancies
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And Inaccurate Code Selection
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