Harnessing AI to combat fraud, waste and abuse

Health care fraud, waste and abuse - also known as FWA - has reached epic proportions. This causes payers billions of dollars annually in unnecessary overpayments and deteriorates the oftentimes contentious relationship between payers and providers.

The COVID-19 pandemic exacerbated the issue, further straining payers' resources and impeding their ability to achieve strategic objectives. Addressing FWA has become an imperative for health care payers, necessitating the adoption of advanced artificial intelligence tools to support their efforts. These tools should enable payers to swiftly identify suspicious claims, enhance the efficiency and efficacy of FWA detection, indirectly enhance the member experience, and alleviate friction with health care providers.

More than $100 billion may be lost annually due to health care FWA. This undermines costs and quality during a time when payers continue to make the monumental shift away from fee-for-service to value-based care.

In fiscal year 2021, the federal government recovered more than $5 billion in health care fraud settlements and judgments. Although the dollars recovered in fiscal year 2022 were lower, that didn’t mean health care FWA decreased. It means FWA has become more difficult to detect.

As health care has become more complex, the ever-evolving nature of medical codes, payer policies and federal regulations makes it difficult to understand and address the breadth of potential vulnerabilities. Even well-intentioned providers may commit health care FWA. Unless payers have a comprehensive AI strategy in place, they may not be able to detect FWA.

The post-pandemic rebound of health care usage and subsequent increase in claim volume means the probability of FWA is greater than ever before. Without a robust AI solution, health care FWA may go under the radar. Understaffed FWA teams don’t have time to investigate every potential lead. Without AI, staff struggle to prioritize cases and may become burned out, leading to costly turnover and missed opportunities.

Health care FWA is a multi-faceted problem that payers can address only with a robust AI-driven strategy in combination with targeted analytics.

Detecting FWA

Payers can’t rely on FWA investigators to catch every FWA scheme. Schemes constantly evolve, and not all of them may be obvious. Instead, payers need a robust solution that is created and maintained by FWA subject matter experts — one that detects data anomalies signaling potential problems with a high degree of accuracy based on experts’ validation.

Advanced AI tools also include a comprehensive set of rules to capture known FWA activity. By leveraging these tools, payers avoid paying improper claims prospectively or retrospectively. The goal is to uncover suspicious billing patterns, coding errors, policy and contract violations, ineligible providers and members, and collusion — and then act.

Empower FWA investigation

Advanced AI technology automates time-consuming tasks and quickly isolates potential problems so teams spend less time reviewing data and more time pursuing high-value FWA leads. In addition, natural language generation summarizes the story of each provider’s behavior, helping payers understand why certain providers are potential leads.

Advanced AI also provides investigators with similar existing leads they can use to inform decisions and identify next steps. Finally, with machine learning, advanced AI solutions provide payers with the types of leads in which they’re most interested. The result? More efficient and effective FWA detection focused on the health care payer’s strategic goals.

Leverage AI-driven insights

Payers possess a multitude of options for leveraging data-driven insights to combat health care FWA. One approach involves automating FWA detection through the integration of AI and proprietary rules, which aids in identifying both known FWA patterns and emerging schemes.

Machine learning can be used to continually refine detection algorithms based on user feedback, ensuring the most effective detection solutions are employed. Data-driven insights can uncover systemic issues within a payer's claim edits, policies or contracts, presenting an opportunity for targeted improvements that benefit payers, providers and members alike.

By emphasizing their capacity to filter out FWA and collaborate with ethical providers, payers can enhance their appeal to potential members and reinforce loyalty among existing ones. This not only contributes to lower premiums but also shields health care consumers from financial and medical harm. The adoption of automated, actionable education letters facilitates a nonpunitive, proactive approach to addressing provider behavior, resulting in substantial cost savings. AI-driven insights aid in reducing false positives, mitigating the risk of unjustly penalizing providers.

Looking ahead

Payers won’t ever be able to eliminate health care FWA completely. However, that isn’t the goal. The goal is to detect it immediately and then take steps to mitigate risk. Detecting FWA will only become more difficult in the years ahead as health care providers use AI solutions to maximize reimbursement. AI-generated clinical documentation and medical coding automation will make it increasingly difficult for payers to determine the legitimacy of services and supplies.

As the health care industry continues to evolve, payers need an AI-driven strategy that leverages targeted predictive analytics validated by subject matter experts. Payers that take the time to vet potential AI solutions will be well-positioned to contain costs and promote payment integrity.

Karen Weintraub is executive vice president of Healthcare Fraud Shield. Contact her at [email protected].

© Entire contents copyright 2024 by InsuranceNewsNet.com Inc. All rights reserved. No part of this article may be reprinted without the expressed written consent from InsuranceNewsNet.com.