Our
Approach
​P3 is a team of AI Professionals who work with business partners to evaluate, develop, and revise AI in Revenue Cycle Management (RCM) Readiness frameworks for AI Medical Coding (i.e., ICD-10, CPT & HCPCS) and Documentation Integrity.
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​We are Certified AI Professionals and ISO 9001:2015-Certified Internal Auditors. Using ISO 9001 to assess, develop, or revise end-to-end AI in RCM best practices.
Quality-Assurance Approach, compliant, accountable, responsible, and ethical AI.
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P3 Quality Assurance Q-CARE System:
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AI Quality-Driven Strategy
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Process: Ensures that processes used to manage and create deliverables are effective and consistently applied appropriately.
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Goal: Minimize risks by building and integrating Quality into AI in RCM Processes.​
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Example: Establish a procedure with standardized guidelines for implementing and maintaining artificial intelligence (AI) systems in revenue cycle management processes.
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Our
Methodology
P3 Responsible AI focuses on achieving end-to-end RCM operational Compliant, Accountable, Responsible, and Ethical AI results.
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P3 Quality Control Q-CARES Approach:
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AI Governance-Oriented Strategy
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Process: Ensures the AI in RCM Products. Services and Deliverables meet specified requirements.
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Goal: Reduce errors and improve quality/compliance.
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AI Quality Checks
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Inspect, Detect & Assess Inconsistencies
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Determine RCM Performance, AI Impact vs. Operational 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)
How is AI Used in RCM?
AI in revenue cycle management (RCM) leverages advanced technologies like Machine Learning (ML), Large Language Models (LLMs), and AI Medical Coding Prompts to enhance medical coding efficiency and accuracy:
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P3 Quality Eyes are on AI!​
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​​Top AI Medical Coding Concerns:
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Accuracy and Consistency
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Quality/Regulatory Compliance
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Contextual Understanding
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Usability and Integration
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Overreliance on Automation
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Limited Specialty-Specific Expertise
​​AI MEDICAL CODING QUALITY
Enhancing AI Medical Coding
Enhancing Medical Coding Quality requires a combination of data quality, training model knowledge, and regulatory compliance checks/ balances.
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A Formula for Quality:
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Incorporate Regular Audits to assess data quality and coding accuracy
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Automate Quality/Compliance Checks
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Build Contextual NLP Understanding into AI Training Models
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Analyze Successful vs. Rejected Claims
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Implement Consistent Human AI Collaboration and Feedback Loops
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P3 Q-CARES Standards (for)
AI in RCM Success
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Accuracy (% Error-free transactions; coding, billing, claims processing/payments)
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​Efficiency (Speed to which tasks are performed)
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Compliance (Adherence to quality/regulatory standards)
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User Satisfaction (Ease of use, clarity, understanding, overall performance)
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Risk Factors (inspected/detected/resolved)