< + > From Complexity to Clarity: The Future of AI in Laboratory Payment Accuracy

The following is a guest article by Bernard Steinbacher, CPC, MS, MD, and Senior Medical Director at Lyric

The landscape of laboratory payments in healthcare is complex and evolving, presenting both significant challenges and opportunities for improvement. With approximately 26,519 diagnostic and medical laboratory companies operating in the United States as of 2024, the industry is diverse and expansive, encompassing independent labs, hospital-based facilities, and mammoth national chains like LabCorp and Quest Diagnostics.

The top 30 companies represent only about 25% of the overall market share​. This distribution highlights the competitive nature of the field, with many players contributing to the diagnostic testing landscape. For health plans, the potential for overpayment is considerable. 

Reviews of payer data reveal large gaps in payment accuracy. The sources of confusion and inefficiency are many. For example, there are around 800 CPT codes to represent genetic tests alone, which are used in nearly 2.2 million unique code combinations for billing. In fact, studies show one-third of genetic tests are ordered in error, which can impact treatment and outcome.  Health plans may be paying for services that they should not –– particularly in the areas of genetic and diagnostic laboratory testing. 

Despite comprising less than 1% of healthcare claims, genetic tests often account for 15-20% of medical services reviewed by health plans –– reflecting their high cost and inordinate impact on utilization. And until recently, there was no ID system to differentiate the more than 175k genetic tests currently available.

Today, there is a growing opportunity to address the complexity and associated challenges of laboratory payment integrity with emerging technologies related to and including artificial intelligence. 

Current State and Challenges

The current state of laboratory payment accuracy is difficult to assess comprehensively due to a lack of transparency in the industry. There are significant gaps in payment accuracy, particularly in the areas of genetic and diagnostic laboratory services, and the total annual spending on clinical laboratory services in the U.S. is estimated to be over $102 billion. This includes all settings such as hospitals, outpatient clinics, and independent laboratories –– with outpatient testing accounting for 30% of this expenditure.

Considering the scope and complexity of the market, it’s no wonder health plans face multiple challenges in managing laboratory payments. The industry’s fragmented nature, with thousands of labs offering various tests, makes it difficult to establish standardized pricing, leading to significant variations in costs for similar services. Today, managing contracts with participating providers and being educated on pricing for non-participating providers remains the biggest challenge for payers. This lack of transparency complicates efforts to assess payment accuracy comprehensively. 

The increasing complexity of tests, especially in genetics and molecular diagnostics, complicates accurate billing and payment, as coding systems struggle to keep pace with rapidly advancing technology. As these tests become more sophisticated (and common), ensuring correct coding and reimbursement becomes even more challenging.

High-profile cases have highlighted the vulnerability of the system to fraudulent practices, such as unnecessary testing and kickback schemes, which can result in high dollar losses for health plans and taxpayers. Additionally, it is important to consider incorrect coding or bundling of tests that can lead to overpayments and inefficiencies, causing financial strain on health plans and potentially increasing costs for patients.

These payment discrepancies can also strain relationships between health plans, providers, and patients –– potentially affecting the quality of care and patient/member satisfaction.

The Importance of Payment Accuracy

Ensuring payment accuracy in laboratory testing is crucial for several reasons. Overpayments due to errors or fraud can significantly impact health plan finances, potentially leading to increased premiums for members.

Inaccurate payments lead to increased administrative burdens, including clinical reviews and prior authorizations, which can slow down the healthcare delivery process.

Payment issues can potentially delay care and lead to unexpected costs for patients, causing financial stress and potentially deterring individuals from seeking necessary medical attention.

Accurate payments are essential for compliance with healthcare regulations, helping providers and payers avoid legal issues and maintain trust in the healthcare system.

Technological Solutions and Opportunities

Advancements in technology, particularly solutions rooted in and related to artificial intelligence (AI) offer promising opportunities to address these challenges. AI is proving to be an essential technology in this space because it has multiple modalities and capabilities to identify fraud, waste, and abuse schemes in a very quick and efficient manner. Such technologies include:

• Machine Learning (ML) algorithms that analyze historical billing data to detect anomalies and predict potentially fraudulent activities, allowing for proactive prevention of improper payments
• Natural Language Processing (NLP) processes unstructured data in medical records and billing documentation to verify the accuracy and necessity of laboratory tests, ensuring that only appropriate tests are billed and paid for
• Robotic Process Automation (RPA) automates the review of large volumes of claims, checking for compliance with billing rules and flagging suspicious claims –– significantly reducing the time and resources needed for manual review
• Advanced Neural Networks analyze complex data patterns to identify subtle fraudulent behaviors and build more accurate predictive models, catching sophisticated schemes that might escape traditional detection methods
• Data Mining techniques provide valuable insights for improving payment accuracy and detecting anomalies; for example, uncovering hidden patterns in billing data that may indicate potential fraud or unusual ordering patterns, helping to identify systemic issues or emerging trends in laboratory billing

The emergence of integrated solutions combines these AI technologies with proprietary market data and clinical expertise. These solutions can help in recognizing incorrect coding or services that don’t match billed procedures; support diagnostic test ordering, coding, coverage, claim editing, and payment integrity; and provide a seamless and transparent claims processing experience.

For example, these solutions can identify when individual codes are billed instead of a single panel code for multiple tests, preventing unnecessary costs to both insurers and patients.

The Future of Laboratory Payment Accuracy

Looking ahead, the field of laboratory payment accuracy is likely to see continued innovation:

• Enhanced AI Capabilities: As AI technologies advance, their ability to detect and prevent fraud, waste, and abuse will improve; continued innovation in AI will lead to more sophisticated and effective solutions for laboratory payment accuracy
• Increased Automation: More aspects of the claims review and payment process will likely become automated, reducing human error and increasing efficiency; automation will streamline operations and enhance the overall payment system
• Greater Transparency: There may be industry-wide efforts to create more standardized pricing and identification systems for laboratory tests; increased transparency will facilitate better payment accuracy and fairness in the industry
• Focus on Genetic Testing: With the rapid growth in genetic and molecular testing, solutions specifically tailored to these complex tests will likely emerge; specialized technologies and approaches will address the unique challenges of genetic testing payments
• Integration with Electronic Health Records (EHRs): Closer integration between payment systems and EHRs could further improve accuracy and efficiency; seamless data exchange between these systems will enhance the overall payment process and patient care

While these technological solutions offer significant promise, it’s important to note that they are tools to bolster human decision-making –– not to replace it entirely. The complexity of healthcare and the potential impact on patient care necessitates careful oversight and continuous refinement of these systems.

The current state of laboratory payment accuracy remains complex and fraught with challenges. Emerging technologies offer powerful tools to help improve cost, administrative efficiency, and accuracy in this fundamental and rapidly growing corner of healthcare finance. As these solutions evolve, they can significantly reduce overpayments, improve patient care, reduce fraud, waste, and abuse, and create a more transparent and efficient laboratory payment ecosystem.

About Bernard Steinbacher

Bernard Steinbacher, CPC, M.S., M.D., Senior Medical Director at Lyric, expertly navigates the complex intersection of healthcare, IT, and health insurance. With a solid foundation in claims, claims adjudication, and medical policy edits, Dr. Steinbacher collaborates to create appropriate prepay edits and medical policies, enhancing payment accuracy using content from the AMA, CMS, and other industry sources. His dual role as a practicing physician enriches his understanding and management of medical policies, ensuring they are both technically sound and practically applicable.