Pre-pay reviews require speed and accuracy for success
The healthcare industry has been evolving to identify errors and inaccuracies prior to payment rather than after the fact. This proactive approach to auditing provides many benefits but is not easy for payers to achieve due to factors such as operational limitations, data complexities, contract requirements and regulatory compliance. The introduction of AI technology has immense potential to dramatically impact payment integrity (PI) programs and the ability to detect and address potential errors within the claims lifecycle, allowing this work to more easily shift upstream to be completed earlier in the process.
Current state of pre-payment auditing
A recent EXL and Healthcare Dive survey asked healthcare payers about their current versus desired pre-payment audit rates for claims. The vast majority (76%) stated they were currently conducting pre-payment audits between 5% and 30% of their claims. Compared with their ideal future state, most payer executives would like to audit a higher percentage of their claims before payment with most citing an increase of only 5-15% more from their current position.
What percentage of claims are currently audited prior to payment and what is percentage of claims do payers want to audit prior to payment?
Current state:
- Between zero and 5%: 8%
- Between five and 15%: 39%
- Between 16% and 30%: 37%
- Between 31% and 50%: 13%
- More than 50%: 3%
Future/Goal state*:
- Between zero and 15%: 8%
- Between 16% and 30%: 39%
- Between 31% and 50%: 37%
- More than 50%: 15%
*1% of respondents did not answer and therefore not included within above results
76% of payer executives would like to would like to audit a higher percentage of their claims before payment, with a range of 16-50% as their ideal future state.
Obstacles to pre-payment auditing success
EXL surveyed participants to understand the gap between a payer’s current and target pre-payment audit rates, identifying barriers to more proactive claims auditing. Recipients stated the main two obstacles to pre-payment audits are the need for rapid adjudication and better selection accuracy rates.
Top barriers preventing payers from auditing or reviewing a greater percentage of claims prior to payment, as percent reported by payer executives:
- Pre-payment audit/review slows down adjudication turnaround: 65%
- Low selection accuracy rates (i.e. the selected claims are not the right ones, and/or the precision of selection could be improved): 62%
- Front-end technology systems do not have adequate capabilities: 49%
- Concerns about provider abrasion: 32%
Turn to AI technology to find the root causes of errors
Speed and accuracy are essential to ensure the right claims are quickly identified, audited and adjusted to maintain regulatory compliance and meet service level agreements. Legacy workflow processes and technology solutions have struggled to meet these demands, but AI technology mitigates against existing challenges.
At EXL, we combine established AI technologies with new large language models to help our clients maximize recoveries at minimal administrative cost. This AI-enabled approach not only provides the necessary speed and accuracy, but it also helps payers shift upstream to identify and address the root causes of errors earlier in the claims lifecycle. By quickly finding these underlying issues, payers can help change provider practices and prevent overpayments from happening in the first place, shifting the focus from managing symptoms to addressing the issue at the source.
AI technology can play a major role in helping payers identify anomalies earlier within the claim continuum. AI-powered digital solutions can proactively identify errors and provide actionable insights about the root causes of overpayments.
Healthcare payer achieves $7.5 million in cost avoidance in one year and a 50% hit rate with prepayment program
A large national health plan needed a robust pre-pay program with minimal impact on providers while ensuring payment regulations were met. The payer faced challenges in processing speeds with medical record requests and turnaround time. The payer also was concerned that any new program would increase false positives and adversely impact provider abrasion.
EXL, working as a subsequent pass vendor, provided advanced analytics and clinical payment expertise to avoid payment errors. The team leveraged proprietary models to develop member, claim, and provider profiles and to score risk. They also used providers abrasion analytics to inform selection strategy and reduce medical records requests. Using machine learning algorithms to detect complex patterns, the team uncovered substantial pre-pay findings. Read the full case study here.
About EXL Payment Integrity Solutions
Harnessing the power of data, advanced AI and deep domain expertise, EXL helps our clients modernize legacy, siloed payment processes to ensure payment accuracy while reducing provider friction, administrative effort, and cost.
EXL provides health plans with AI-driven payment integrity solutions that extend far beyond traditional claims review to detect errors in real-time, uncover hidden risks, and minimize improper payments across diverse datasets and at every stage of the claim lifecycle. By transforming raw data into actionable insights, we empower our clients to make informed, strategic decisions while reducing manual intervention.
Our collaborative, agile approach combined with scalable, customizable solutions improve business results and drive sustainable growth, aligning to the unique operational and regulatory needs of large national payers and regional health plans alike. Whether managing high volumes of complex claims or enhancing overpayment detection accuracy, EXL drives sustainable cost savings and operational excellence at speed and scale, fostering long-term partnerships across the digital transformation journey.