Navigating the Surge in Payer Takebacks

In the ever-evolving landscape of healthcare reimbursement, one of the significant challenges that providers face (but that doesn’t get near enough attention) is the surge in payer takebacks. These instances, where payers recoup prior payments following audits, have seen a nearly 2X increase in recent years, constituting almost 2% of debit accounts receivable.?

The Challenge of Tracking Takebacks

Tracking takebacks is a unique challenge for healthcare providers. The communication of takebacks often comes in the form of letters, which are then scanned into document management systems. While these systems are gradually incorporating optical character recognition for data extraction, the extracted information is often left uncategorized or untracked throughout the appeal process. This lack of systematic tracking can lead to delays and inefficiencies (read: slow reimbursement).

Establishing an Effective System

Providers need a robust system to navigate the complexities of the takeback process effectively, most notably an efficient approach to gathering and routing the necessary information to the appropriate department. Additionally, evaluating the clinical rationales that support appeals is crucial. Meeting the appeal timeframes outlined in payer contracts and understanding an appeal's return on investment (ROI) are critical components of this process. Having a well-organized and streamlined system ensures that providers can address takebacks in a timely and effective manner.

Challenges in Reporting on Takebacks

Reporting on takebacks can be a daunting task for many healthcare organizations. Payers often bundle takebacks with new payment adjudications, making separating and analyzing the data difficult. Implementing a strategic approach is essential to address this challenge. At Sift, we leverage ML models that equip providers to flag takebacks and manage them against their ROI. Sift’s ML leverages payer patterns and uses EDI and EHR data to identify takebacks. This data is then presented to allow for easy analysis, enabling healthcare providers to slice and dice it by denial reason, payer, and aging date.

As revenue cycle teams increasingly grapple with payer takebacks, it will become essential to establish effective systems for tracking, managing, and reporting on these instances — the foundation of which is aggregated data. ?

Sift’s ML and analytics close the gap on takebacks, moving organizations from reactive to responsive to preventative, preparing revenue cycle teams to challenge this payer behavior effectively, and fostering collaboration with payer representatives and contracting counterparts. Learn more about Sift's approach in our comprehensive (and free) Denials Insights Guide.

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Justin Nicols is the Founder and CEO of Sift Healthcare. Sift's AI and machine learning integrate actionable intelligence into revenue cycle workflows to maximize payments and improve operations.

Connect with Justin on LinkedIn and learn more about Sift Healthcare at www.sifthealthcare.com.