How AI helps SIU Investigations

Many wonder the impact technology and automation will have on human-driven roles. A health plan’s SIU unit is not any different. Will AI replace humans in health plan SIUs in fraud detection?

By Saurav Subedi, Director of Data Science, Codoxo

With the rapid movement toward data-driven decision making, healthcare payers are adopting many tools, technologies and processes to optimize their organizations. People are part of the equation also, guiding strategy and best practices. These components comprise the ideal toolkit that healthcare payers use to achieve organizational goals. 

As Special Investigations Units (SIUs) increasingly adopt technologies such as artificial intelligence and machine learning, let’s look at how these components work together as they continue to evolve.

The role of an SIU unit is critical to helping to combat rising healthcare costs. SIUs are responsible for identifying and investigating insurance fraud, waste, and abuse. Its investigators scrutinize the claims and billing process to uncover the signs of overbilling, false coding, and other non-compliant or wasteful practices. Largely, this unit supports broader cost containment efforts and helps proactively mitigate risk. 

There are well-established processes and practices built around traditional SIU toolkits, including Excel spreadsheets and/or home-grown solutions paired with deep domain healthcare investigators. But in recent years, digital solutions have been adopted to automate manual processes and create efficiencies across operations. More importantly, application of artificial intelligence (AI) and machine learning in healthcare cost containment efforts has gained significant momentum, primarily because of advancement in tools, technology, algorithms, and availability of skilled human resources. 

This has changed the nature of the SIU toolkit, from composition to pan-industry usage. Will fewer healthcare investigators be needed as a result?     

Technology & Automation vs. Humans 

When done well, health plans use technologies such as AI and as one of many tools in their kit. In our experience, successful implementations rely on the tripod balance of state-of-the-art machine learning methods and big data architecture in close collaboration with subject matter experts. There exists a symbiotic relationship between the experts and these algorithms. Machine learning workforce leverages human expertise to properly navigate the legal and legacy complexities of the healthcare payment integrity space, incorporate their thoughts and processes into feature engineering, and establish business reasonability checks and validations. 

Subject matter experts, on the other hand, use these innovations to complement their efforts and help them do their job better, more efficiently. Traditionally, vast amounts of data were handled by humans. Today, we have the ability to empower these experts with solutions that ease the burden of labor-intensive tasks and help implement better practices. What this equates to is data-driven decisions made faster. 

For instance, for identifying a typical fraud, waste and abuse case, traditional approaches would include navigating through the universe of different codes, including International Classification of Diseases (ICD) codes, Current Procedure Terminology (CPT) codes, and Healthcare Common Procedure Coding System (HCPCS) codes, individually or through a known combination pattern. This is naturally a time-consuming process and would be less likely to detect novel schemes. On the other hand, an AI-based approach could analyze such multidimensional utilization profiles efficiently and effectively to identify novel schemes and complement the traditional rules-driven architecture.

Investigators vs. Data Scientists

In our AI-focused work with healthcare payers, we see the challenges they face collaborating across their investigators and data science teams. Each team has their own unique role: investigators have deep roots and experience in discovering, investigating and optimizing processes around fraud, waste, abuse, and non-compliance. Data scientists are experts at understanding data and, importantly, surfacing meaningful insights from the data. Pairing these skill sets together can create unsurpassed ability to manage costs and shed light on business problems. Where one skill set leaves off, the other picks up. Both automation and data science give healthcare SIUs the edge in battling fraud, waste and abuse. 

For instance, with the rise of telemedicine since the onset of COVID-19, we observed increased telehealth code utilizations in specialties such as psychiatry and sleep medicine.  At the same time, we also observed significant increase in telehealth practices in other non-traditional specialties from a telemedicine perspective. Since this is an unknown territory, our health plan clients want to gain more insights and potential impact on cost containment – providing an interesting collaboration opportunity between subject matter experts and AI. While AI could identify anomalies and potential FWA based on significant changes in patterns and peer comparison through big data analytics for SIUs to launch a targeted investigation, it required guidance and business reasonability checks from the SMEs to assist in the design of the algorithms and refine the results.

The Right Cost Containment Toolkit Requires a Common Understanding and Collaboration  

The Wall Street Journal reports that in the first quarter of 2021, 37,000 new jobs were posted for artificial intelligence human experts, up 45% from Q4 last year.  While these positions are cross-industry, it’s evident that people are essential to implementing and managing technologies that are driving significant positive results. 

A major evolution is happening across the healthcare spectrum. Health plans that strike the right balance between platforms, digital solutions, data and domain expertise drive better financial, speed and efficiency outcomes. And within SIUs, people will remain a key part of cost containment toolkits, today and in the future.