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Top waste, fraud, and abuse red flags, and how to identify them


: In some cases of healthcare fraud, it’s easy to spot red flags. But in large healthcare organizations, or on the payer end, fraud and waste can be more difficult to detect through layers and layers of data.

In some cases of healthcare fraud, it’s easy to spot red flags. But in large healthcare organizations, or on the payer end, fraud and waste can be more difficult to detect through layers and layers of data.

During his presentation, “Using Analytics to Drive Payment Integrity and Reduce Fraud,” on November 16 at the annual National Health Care Anti-Fraud Association Annual Conference in Orlando, Ben Wright, AHFI, senior payment integrity solutions architect at SAS Fraud & Security Intelligence Global Practice, discussed how health systems and payers can  meet these challenges.

 “In larger healthcare organization, you want to make sure you are protecting from larger loss,” Wright said. “Waste and abuse are clearly much more expensive in most cases than intentional fraud.”

Managed care has not reduced fraud, waste, and abuse in the way it was hoped at inception, Wright he said, and the need to coordinate efforts and create enterprise-wide solutions has never been greater. 

How technology can help

Analytics platforms can help identify subtle changes in behavior and practice that can be indicative of fraud, waste or abuse, Wright said. This can include identifying errors and duplicates in the billing system, and fraud and wasteful or abusive practices.

He noted three types of analytics that can help:

1.              Behavior analytics is the closest approximation of true fraud detection, he said. It can help systems identify behaviors that are most likely to indicate fraud. For example, a provider who prescribes outside of the norm for their specialty, or a practice that documents more patient encounters than makes sense.

2.              Claim analytics uses customized product or policy data to sift through abuses of rule sets, coding designations, prescription rates, and more.

3.              Clinical targeting reviews level of care issues.

They key to using these analytics, Wright said, is to view them as enterprise-wide and to coordinate efforts across platforms and services, not within silos.

A hybrid of the above analytics methods is most effective, he said, using behavioral analytics, payment policy and coding guidelines, and clinical targeting together.

Examples of big red flags

Wright shared with Managed Healthcare Executive several examples of service line issues or red flags to watch out for.

·      Provider specialty mismatch. A provider who has general medical training but a specialty in neurology might warrant closer investigation if he is prescribing outside his specialty’s norm. Say a neurologist is prescribing a lot of opioid medications, Wright said. Investigators may want to review what tests are being ordered and why. It may become clear that the tests ordered and the level of evaluation of the patients for which those medications were ordered does not match what you might expect from a neurologist. “If you find that the tests don’t match, that would be a behavior that would be atypical for a community of neurologists,” Wright said. “That provider might get additional scrutiny. It’s a combination of their behavior versus behavior that might be expected.”

·      Locum tenens physician rates. These physicians fill in for absent physicians, but sometimes can be used to increase patient volume and revenue. In one case, Wright said, a physician was seeing patients in an emergency department while a locum tenens provider saw the physician’s other patients in the office. All of patient visits were paid at the provider’s rate, but the locum tenens should have been paid at a lower rate. This is a violation of locum tenens rules, but also indicates oan inappropriate agreement between the provider and the hospital, as well as an exploitation of the locum tenens physician.

There is so much data at a plan or health system’s fingertips now, the key is managing it to get the information you need. Increased specificity of coding, for example, provides a lot of data on disease management, but not a lot toward improving payment integrity.

 “Intentional fraud is a very small percentage of the community, but it’ a huge amount of dollars,” Wright said. “Meanwhile, waste and abuse can happen on many levels.”

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