Our
Approach
We are Certified AI Professionals and ISO 9001:2015-Certified Internal Auditors who partner with our clients, business associates, and others to research, evaluate, and develop efficiency, quality, and compliance solutions.
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P3 helps to review and establish Responsible AI integrity, governance, and best practice frameworks as it relate to health IT, revenue cycle management(RCM), medical coding, and documentation integrity processes:
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P3 Quality Control Approach:
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Front End: Eligibility verifications and prior authorizations.
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Mid Cycle: AI Documentation Integrity, AI Medical Coding (i.e., ICD-10, CPT & HCPCS) data integrity/ charge integrity and reconciliation auditing.
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Back End: Claims Management and Denials Management, remediation and mitigation.​
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Our
Methodology
P3 leverages quality control audit techniques that help to evaluate and address provider and payer AI algorithm and software inconsistencies. AI coding/CDI regulatory recommendations must be followed to achieve optimal performance results:
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P3 Quality Control Focus
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AI in RCM Analysis
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Coding Algorithms and Rules Review
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CDI Quality Checks and Governance Strategy
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Inspect, Detect & Assess Inconsistencies
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Leverage Data Analytics and Reporting
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Establish a Continuous Quality Improvement Program
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Determine Performance Metrics
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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) is a transformative force that harnesses advanced technologies like machine learning, natural language processing (NLP), and robotic process automation (RPA) to revolutionize and enhance the RCM Administrative and Claims management process.
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P3 Quality Eyes are on AI!
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​​Examples:
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Scheduling/Pre-Registration Errors
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Incomplete Insurance Verifications
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Procedure Code Inaccuracies
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Charge Integrity Issues
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Timely Filing Deadlines Missed
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Denials Not Tracked or Monitored
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Policies/Procedures Not Clearly Defined
​​AI MEDICAL CODING
​What about AI Medical Coding?
AI in medical coding refers to the use of machine learning algorithms, natural language processing (NLP), and automation tools that are used to analyze clinical documentation, extract relevant information, and assign accurate codes for billing purposes.
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Are you seeing a 95 to 97% accuracy rate when using AI Medical Coding software? If not, how are you identifying, addressing and correcting errors and inconsistencies?
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Examples:​
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Incorrect Code Use
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Quality and Compliance Discrepancies
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System Integration Compatibility Issues
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Training and Adaptability Challenges
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Vendor Performance