The healthcare industry is a prime target for fraud, with billions of dollars lost annually due to inappropriate claims. While post-payment detection methods have been the traditional approach, they often fall short. The ‘pay and chase’ method can lead to large exposure to inappropriate claims and can also lead to staffing and administrative burdens. This is where prepay FWAE (fraud, waste, abuse, and error) detection leveraging both real-time prepay rule creation post-payment analytics comes in; analyzing claims before they are paid. Prepay FWAE detection is a critical tool for combating FWAE and ensuring the integrity of the healthcare system.
The Benefits of Prepay FWAE Detection
The fiscal year (FY) 2024 Medicare FFS estimated improper payment rate is 7.66 percent, representing $31.70 billion in improper payments[1]. Prepay FWAE detection takes a proactive approach and can streamline the claims processing workflow to significantly reduce financial losses by preventing improper and/or fraudulent claims from being paid in the first place. Prepay FWAE detection software systems can analyze vast amounts of data to identify subtle patterns and anomalies that can lead to more accurate detection of improper billing. Detection software also allows healthcare payers to stay ahead of fraudsters who look for ways to exploit vulnerabilities in the healthcare payment systems.
Challenges and Considerations
Implementing a prepay FWAE detection system has many challenges. It requires seamless integration with existing healthcare systems and data sources. This can be a complex and time-consuming process. HCFS technology and healthcare experts simplify the process. The accuracy of prepay FWAE detection algorithms is crucial for their effectiveness; false positives can lead to unnecessary delays in claim processing and compliance issues. Improper billing is an ever-evolving trend, thus making it necessary for prepay FWAE detection systems to adapt and stay ahead of the curve.
A Scheme Example
Chemodenervation is a procedure involving Botulinum Toxin which is only approved for specific circumstances. Plans are seeing these services inappropriate billed. For example, HCPCS code J0585 (Botulinum Toxin Type A, per unit) should be billed for members who have conditions such as dystonia, blepharospasm and more. It should not be used for wrinkles.[2] PostShield detects inappropriate billing such as this with Alert [2672-01] – SUPPORTING DIAGNOSIS MISSING, CHEMODENERVATION identifies providers billing chemodenervation services without a diagnosis that supports medical necessity. As a reminder, refer to your respective plan policies.
How it Looks Once Implemented
One client took the above-mentioned PostShield (post-payment) alert and created a PreShield (pre-pay) rule from it, allowing the built in logic to do the analytic work for their small SIU team. They added additional logic to focus on a specific population of claims which allowed them to pinpoint their focus. This allowed their limited Payment Integrity/SIU resources to work on other tasks while still maximizing savings. Without this level of customization, their team wouldn’t have had the resources to identify and stop inappropriate payments from being made.
HCFS has you covered!
The HCFSPlatform™ is a comprehensive, fully integrated end-to-end platform. By leveraging advanced analytics, artificial intelligence, and machine learning algorithms, the HCFSPlatform™ can help identify suspicious patterns, anomalies, and red flags before a single dollar goes out the door. If you are already a PostShield client of ours, let us help you make the transition into using PreShield.
References:
[1] https://www.cms.gov/data-research/monitoring-programs/improper-payment-measurement-programs/comprehensive-error-rate-testing-cert
[2] https://www.cms.gov/medicare-coverage-database/view/article.aspx?articleId=57185
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