Exponential AI

3 Key COB Decisions AI Can Automate  🔍 Accurately identifies coordination of benefits scenarios.  🔀 Automates the determination of primary versus secondary coverage.  💡 Assigns appropriate reason codes for claims adjustments. #benefitscoordination #PayerAI #healthcare #healthtech

In today’s complex healthcare landscape, AI is transforming COB management by addressing some of the most persistent challenges. 3 COB Challenges AI can Solve 1. Subpar Yield More than 50% of COB issues are never identified leading to financial leakage and reactive processes leading to inefficient “pay-and-chase”. 2. High Cost of Recovery It takes 5x longer to settle a COB claim and vendor dependent model relies on contingency payments that make the cost of recovery exorbitant. 3. Lack of Automation Nearly 5% of claims need COB adjustments and the lack of integration between internal teams and systems lead to manual processes and handoffs with no visibility & traceability. By leveraging AI, health plans can overcome these challenges, enhancing efficiency, accuracy, and financial outcomes in COB management.

Health plans face costly challenges due to poor provider data quality. Manual processes lead to errors, delays, and downstream issues like member/provider dissatisfaction, claims problems, and compliance penalties. Our AI-powered Smart Provider Contracts solution streamlines contract loading, directory verification, and updates, improving data accuracy and operational efficiency. This directly enhances three key financial outcomes for health plans. 95% Faster Contract Loading: AI streamlines contract processing, enabling rapid implementation and updates. 10-30% Less Claim Interest: AI ensures timely and accurate contract integration, minimizing interest payments on delayed claims. 3-5% Fewer Claim Adjustments: AI captures precise contract details, reducing errors and adjustments in claims processing.

Every fourth claim failing auto-adjudication is linked to errors in provider data. While provider data quality issues can arise from various sources, addressing specific areas like errors and delays in provider contract loading can have a significant impact. Our AI solution focuses on extracting accurate demographic information, including providers' fee schedules, services covered, locations, and license/credential status, ensuring no errors exist in provider information. This precision helps: •⁠ ⁠Reduce Claims Errors & Overpayments: Ensuring accurate provider information in claims, whether specialties or fees, minimizes errors and saves you money. •⁠ ⁠Enhance Compliance: Prevents costly penalties from CMS and state agencies for directory inaccuracies. •⁠ ⁠Improve Member Satisfaction: Reliable information on in-network providers avoids surprise billing and builds trust. Clean provider data also indirectly boosts risk adjustment revenue. It fosters better collaboration between your plan and providers, freeing up time for more accurate clinical documentation and coding. This ultimately leads to richer data for risk adjustment. Poor provider data costs you money. Leverage AI to improve your health plan efficiency, member experience, and ultimately, bottom line.

Denials impact nearly 20% of claims, and can lead to nearly 5% loss in Net Patient Revenue. Manual processes and constantly changing payer rules make it difficult to prevent them. AI can transform denial management. It can : 1. Identify Denial Risks Pre-Submission 2. Pinpoint Root Causes for Pre-bill Interventions 3. Increase Clean Claim Submissions #providers #revenuecyclemanagement #healthcareai #rcmsolutions #claimsai

While issues with provider data can stem from various sources, addressing specific areas like errors and delays in entering provider contracts into systems can make a real difference. By using AI, health plans can improve provider data quality, a crucial factor that impacts three key areas: claims processing, network planning, and helping patients find care. Accurate provider data ensures important details, like fees, are recorded correctly, leading to fewer errors, delays, and rejections in claims processing. Reliable data also helps plans identify coverage gaps, optimize networks, and ensure members have access to comprehensive healthcare services. Up-to-date provider information, like specialties, locations, and when they're available, help members find the right care at the right time, improving delivery, outcomes and member satisfaction.

Every fourth claim failing auto-adjudication is linked to errors in provider data. While provider data quality issues can arise from various sources, addressing specific areas like errors and delays in provider contract loading can have a significant impact. Our AI solution focuses on extracting accurate demographic information, including providers' fee schedules, services covered, locations, and license/credential status, ensuring no errors exist in provider information. This precision helps: •⁠ ⁠Reduce Claims Errors & Overpayments: Ensuring accurate provider information in claims, whether specialties or fees, minimizes errors and saves you money. •⁠ ⁠Enhance Compliance: Prevents costly penalties from CMS and state agencies for directory inaccuracies. •⁠ ⁠Improve Member Satisfaction: Reliable information on in-network providers avoids surprise billing and builds trust. Clean provider data also indirectly boosts risk adjustment revenue. It fosters better collaboration between your plan and providers, freeing up time for more accurate clinical documentation and coding. This ultimately leads to richer data for risk adjustment. Poor provider data costs you money. Leverage AI to improve your health plan efficiency, member experience, and ultimately, bottom line.

Payers are using AI to track membership and coverage changes in real-time, enabling faster and more accurate COB identification and primary payer determination.   Automating these tasks with AI speeds up COB, prevents errors and overpayments, and helps identify misdirected or overpaid claims.   #AIinHealthcare #COB #Automation #HealthcareInnovation #BenefitsCoordination

AI is transforming how we handle COB by improving both pre-pay and post-pay processes. 1. Pre-Pay COB Review: Focuses on cost avoidance by ensuring the appropriate adjudication of future claims. 2. Post-Pay COB Review: Identifies recovery opportunities through a retrospective review of past claims against new COB information. By integrating AI, health plans can achieve greater accuracy and efficiency, reducing costs and enhancing the overall claims process. #AI #COBManagement #CostAvoidance #ClaimsProcessing

Happy Thanksgiving! 🍁 May your holiday be filled with warmth, happiness, and the joy of togetherness. Here’s to a season of appreciation and continued success together.✨