Uncover, recover, and prevent

Claims overpayment represents 7% to 8% of medical expenses for the typical payer, and payers spend as much as 8% to 10% of all administrative expenses on efforts to recapture overpayments. However, the traditional, manual approach to payment integrity can’t keep pace with the growing size and complexity of the overpayments challenge.

It doesn’t have to be this way. Combining established AI technologies with new large language models (LLM) has enabled payers to maximize recoveries at minimal administrative cost. This AI-enabled approach also helps payers “shift left” to identify—and systematically address—the root causes of errors earlier in the claims lifecycle to help change provider practices and prevent overpayments from happening in the first place.

A new, more efficient, effective, and transparent approach

Traditional PI programs use a combination of insourced and third-party resources, with the third-party portion typically resembling a “black box” where a vendor is paid as a percent of savings— i.e., a contingency pricing model—without having to reveal how or where it found the savings. Not only does the traditional approach lack the transparency needed to address systematic flaws; it also requires excessive administrative resources, can contribute to provider friction, and doesn’t address problems at the root.

Advancements in data and digital technology are disrupting today’s PI landscape, transforming the current paradigm to remove waste from the end-to-end claims value chain and actually reduce the total magnitude of overpayments—instead of perpetuating the broken processes and systems that lead to what payers must deal with today.

This new data-led and digitally enabled approach is not only more efficient and effective in recapturing overpayments, but also identifies and mitigates the causes of overpayments so they’re far less likely to happen again. By empowering data with advanced technologies including AI, machine learning models, NLP, and an analytics workbench, payers can identify potential erroneous claims and streamline operations with AI-enabled insights that support – not replace – the work of clinical subject matter experts and highly trained auditors. The outcomes of this process are greater yield and specificity, enabling payers to uncover and recover a greater share of overpayments, while minimizing false positive claims that impact provider relations negatively.

Disruption in payment integrity is accelerating

With technology and data now allowing payers to do what they’ve previously only dreamed of doing, the question for payer leadership is an important one: Are they satisfied with the status quo, only treating the symptoms of overpayment, in an endless cycle of audits; or do they want to both recover more of what’s due to them, more efficiently, while creating a claims value chain that’s better for everyone involved?

Download our white paper to learn how a data-led and digitally enabled approach can help payers achieve new levels of cost avoidance, productivity, administrative savings, and benefit expense savings all while reducing friction with providers.