SmartLight Analytics

ANALYTIC EXPERTISE - CLINICAL EXPERTISE

fraud

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In healthcare claims, does distance matter?

Can the distance between your medical provider and the lab used to process your test results tell you anything about potential fraud, waste, or abuse? In fact, it can.  

In reviewing healthcare claims, data specialists use geospatial analysis to compare distances traveled for lab services. This strategy is used in one of many inferential analysis models that is not searching for a specific medical code within claims, but uses statistics and probability to determine if an observed action differs from an expected action. This can lead to a great deal of information inside of healthcare claims.  

How? It begins when analysts calculate a mathematical score that evaluates the actual distance traveled for lab services compared to what is the expected distance. One claim reviewed by the SmartLight Analytics team expected a distance of seven miles for lab work, the actual distance was two miles from the provider. That claim was then compared to another claim where the expected distance for lab work was 10 miles, but the actual distance was 2,976 miles. This drastically different mileage might indicate a pass-through billing lab scheme and at the very least requires further investigation.  

When working with businesses across the country, data analysts have found instances where providers have sent lab work or even bill for services across thousands of miles and sometimes even across state lines unnecessarily. Geospatial analysis has also been used to find claims where services were billed hundreds of miles from where the member employee could have reasonably been assumed to be that day. For example, a claim was flagged because it contained billing for an office visit to a nurse practitioner on a specific day as well as billing for tests conducted at a rural hospital 350 miles away on the same day. To believe this occurred as billed, the member would have had to travel more than 350 miles from his or her home on the same day for these tests. What likely happened was that the member saw the provider near home and the provider sent the claims to the rural hospital for billing. Such claims would then be investigated further to determine whether or not it was fraud or just an unusual case or a very sick patient who needed a specialized service. 

Geospatial analysis is just one of 40 different proprietary models used by SmartLight analysts to find fraud, waste, and abuse within healthcare claims.  

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SmartLight joins forces with Health Transformation Alliance to reduce healthcare costs for some of America’s largest employers

DALLAS, TX – SmartLight Analytics formed a new partnership with Health Transformation Alliance recently to reduce healthcare spending by identifying and eliminating fraud, waste, and abuse in employer self-funded healthcare claims. HTA, a core group of powerhouse companies, combined forces to negotiate lower healthcare costs. SmartLight brings a new level of depth in analytical, clinical and data expertise to HTA member companies.  

SmartLight Analytics helps HTA accomplish our mission of providing superior health outcomes at lower prices by helping plan members in need while reducing errors and outright fraud within our health plans in way that has not been done before by most large self-funded employers.

Lee lewis, chief strategy officer & gm medical solutions at HTA

The new partnership could save millions, if not billions, in spending for HTA members at a time when healthcare costs are predicted to rise dramatically in a post-pandemic environment. With more than 50 of America’s largest companies a part of HTA, the joint agreement with SmartLight Analytics to work with its members could shake up the industry by exposing the fraud, waste and abuse that has existed in the U.S. healthcare system for years but remained largely under the radar. 

“HTA members appreciate our focus on fraud and our carrier-friendly approach to not only finding issues but resolving them and preventing them from reoccurring so wasteful spending does not happen in the first place,” said Asha George, co-founder and CEO of SmartLight Analytics.  

HTA aims to combine the purchasing power and talent of its members to improve health outcomes and get superior value for the healthcare investment made within its members’ self-funded employee healthcare plans. Its membership includes some of America’s largest corporations including American Express, Quest Diagnostics, IBM, Prudential, Pitney Bowes, The Hartford, and The Coca Cola Company. SmartLight Analytics brings to the HTA the ability to take a deep dive into the corporations’ employee healthcare claims. Using inferential analytics that does not presuppose data outcomes or require knowledge of schemes beforehand, SmartLight can identify wasteful spending which employers and third-party administrators may not know exists. SmartLight’s team of statistical and clinical experts have been able to identify fraud, waste and abuse that amounts to 3-5% of a company’s total healthcare spend. 

“SmartLight Analytics helps HTA accomplish our mission of providing superior health outcomes at lower prices by helping plan members in need while reducing errors and outright fraud within our health plans in way that has not been done before by most large self-funded employers,” said Lee Lewis, Chief Strategy Officer & GM Medical Solutions at Health Transformation Alliance. “HTA is transforming healthcare to deliver more value to everyone, which is exactly what SmartLight Analytics is doing with its analytic methods and expertise. They are passionate about reducing healthcare spending.”  

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HTA is a cooperative of self-insured market leaders with employees across the U.S. aiming to drive better health outcomes for employees and their families covered by self-insured employer health plans.  

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How does looking at pharmacy claims combined with medical claims help find fraud?

When it comes to healthcare claims analysis, combining both medical and pharmacy claims data provides a more thorough picture of what is happening for specific members (or dependents) and adds an additional analytical layer to help find different types of fraud, waste or abuse that might exist.

A case study from SmartLight Analytics provides an example of how the combined data can uncover abuse. The case uncovered prior authorization abuse in pharmacy claims resulting in thousands of dollars of on-going fraud.

 SmartLight analysts noticed a set of pharmacy claims for one member with several red flags:  the member was receiving multiple controlled substances and using multiple pharmacies (at least three) to fill and repeatedly refill prescriptions for Oxycodone and Fentanyl. The Fentanyl prescription was for Subsys, which is a spray specifically approved for breakthrough pain in cancer patients. Dr. Franklin Baumann, SmartLight Analytics CMO and co-founder, called this a “very specific drug used in very specific cases.”

The analysis found that for over a year, every single month the member was receiving a prescription for Subsys at the cost of $10,907 per month. The employer’s plan was continuing to pay for this large claim. It was verified that the employer’s plan allowed this drug with a prior authorization only in cases where an employee has cancer. Once SmartLight’s statistical team noticed these pharmacy claims, they turned their research over to the clinical team (including board certified physicians) to compare these findings with the member’s medical data.

“We know there are very strong policies around this particular Fentanyl. It is only used for cancer and, specifically for cancer related breakthrough pain. In this case, it received prior authorization which means the prescribing physician indicated that in the records.”

Having looked at the bigger picture of this member’s medical and pharmacy claims data, there was no indication of any kind of cancer diagnosis.

The clinical team performed detailed historical research on all diagnosis and procedure codes submitted for this member and found “very clearly” that there was no indication of current cancer, current treatment for cancer and probably no prior treatment for cancer.

“We can say that with some confidence because of the way medical codes work.” Dr. Baumann explained. “Generally, when you have a serious cancer and residual effects of cancer, it’s going to get carried along in your medical diagnosis codes.”

SmartLight then brought its findings to the insurance carrier. “The way we bring this up with the carrier (who paid the claims) is that we don’t see it on our end (a cancer diagnosis). Based on everything we saw, it’s not there. When they looked at it, they agreed with us.”

Dr. Baumann said the repeated claims had fallen through the cracks after a prior authorization was issued initially. SmartLight partnered with the carrier to stop the payment on these claims and continues to monitor the case for the employer.

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The Differentiator: Inferential Analytics

What is inferential analytics? This analytic method is used by the SmartLight team to find fraud, waste, and abuse in employee healthcare claims. Simply put, it does not rely only upon algorithms, which are a set of rules created to solve specific problems. Instead, it looks beyond the expected “rules” to find outlier patterns of behavior.

Inferential Analytics

Algorithms look for known issues and targets. For example, an algorithm might be set to look for a specific rule — all providers who bill two x-rays for every patient – which could possibly be an indicator of fraud or overbilling. However, that specific rule could be overlooking a more creative or different fraud scheme. In contrast, inferential analysis does not rely on rules but instead uses evidence, reasoning, and content knowledge to look for anomalies in the data. It models “normal behavior” such as the average spend per patient then compares all the data against that model.

When using algorithms as your primary tool, the downside is you are never ahead of the curve. As soon as a provider finds that claims are getting denied using one code, then the provider will switch those codes to something else. With inferential analytics, data analysts are not searching for a specific code but using statistics and probability to determine if an observed action differs from an expected action.

As an example, geospatial analysis used by SmartLight compares distance traveled for services such as lab processing. Analysts calculate a mathematical score that evaluates the actual distance traveled between a provider and a lab for routine lab services, compared to the expected distance.  One claim reviewed had an expected distance of seven miles for lab work, and the actual distance was two miles from the provider. The claim was then compared to another claim where the expected distance for lab work was 10 miles, but the actual distance was 2,976 miles. This drastically different mileage might indicate a “Pass-through Lab Scheme” and at the very least requires further investigation.

Inferential analytics allows employers to see beyond the surface of their employee healthcare claims and prevent wasteful spending.

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How does analytics save employers money?

The American healthcare system has become increasingly more elaborate and difficult to navigate over the past several decades. COVID-19 did nothing to help that, in fact, the crisis made it more complicated as restrictions were reduced to deal with the pandemic. Within this environment of complexity and rising costs, over 95% of employers are now funding their own employees healthcare and are going to need to contain those costs now more than ever. Industry experts estimate that billions of dollars are leaking out of the system with no measurable improvement in patient health. Are employers left with any options? 

To significantly reduce this waste, SmartLight Analytics, LLC was formed by a group of industry insiders who wanted to make a meaningful impact on the rising cost of healthcare. With this end in mind, SmartLight uses statistical, clinical, and data expertise to deliver the most complete cost reduction solution directly to the actual stakeholders – self-funded employers. Since processing claims is outside the core competency of most employers, patterns of error often go unnoticed and result in the unnecessary spend of health plan dollars. Regular, deep analytic reviews deliver a low-touch, high-impact solution to self-funded employers. 

Millions, even billions, of dollars are spent annually providing healthcare coverage for employees across the U.S. Adjudication of medical services is complex and requires specialized expertise. Employers focus on selling their own products and outsource medical claim payments to third-party administrators. Consequently, there is limited transparency for the employers in this process resulting an avoidable cost being spent on the employer’s behalf. An expert in inferential analytics and clinical data is needed to evaluate payments for appropriateness. Having the independent review of payments reduces annual spend between 3 and 5%.  It is an option more employers will want to have in their toolkits, especially those with 10,000 or more employees. Employers with high numbers of white-collar employees or union members can most benefit from the recovery of these lost dollars due to fraud, waste, and abuse. It is money left on the table when every dollar is needed to keep businesses afloat and back into recovery.