Tag: 2018 News

Understanding MSA trends – and the CMS review process – can assist workers compensation carriers and administrators as they identify cost drivers. This NCCI 2018 research update builds on a September 2014 study that NCCI conducted on MSAs and is based on data from approximately 11,500 MSAs submitted to CMS between September 2009 and December 2015.

CMS’s processing times have declined since 2012 and more recently hit their lowest level since 2010. In 2015, the average MSA processing time was about 70 days, which is the lowest average processing time between 2010 and 2015. This is in sharp contrast to 2011 and 2012, when average processing times were much longer. Median processing times appear to be longer for the largest MSA submissions than for smaller MSA submissions.

Estimated future drug costs are the main reason for CMS requiring increases of MSA amounts. CMS generally requires larger percentage increases to MSAs when the submission amounts are smaller. For example, CMS has requested an average 51% increase when the submitted MSA is under $25,000, while it has only requested an average 6% increase when the submitted MSA exceeds $200,000. Across all sizes of submitted MSAs, estimates of future prescription drug costs for injured workers are the main reason that CMS requires increases.

The gap between approved and submitted MSAs appears steady between 2013 and 2015 mainly because of the decrease between approved and submitted prescription drug amounts. While the gap between approved and submitted amounts for Medicare Parts A and B amounts (covering hospital stays, office visits, and related services) appears stable between 2010 and 2015, the same could not be said for Part D amounts (covering prescription drug services). The gap between prescription drugs approved and submitted MSA amounts was rather large between 2010 and 2012, but has significantly decreased since then and appears to be stable since 2013. This stabilization is the main contributor to the steadiness in the overall MSA amounts between 2013 and 2015.

The larger the MSA, the larger the share of drug costs. Prescription drug shares typically increase as the MSA gets larger. MSA settlements above $100,000 are typically associated with more serious injuries, such as back injuries, limb or finger amputations, burns, or head trauma. Many of these claimants are experiencing chronic pain or depression, so it is not surprising that the majority of MSA settlement costs are for prescription drugs.

Most MSAs are for claimants who are Medicare-eligible at time of settlement. About 64% of claimants are eligible for Medicare, not because of age but because they have been on Social Security Disability for at least two years. Another 29% of claimants are eligible due to age, and about 7% are likely to become eligible within 30 months.

The largest percentage of MSA submissions occur four years after the work-related accident. Submissions gradually decrease after that, but it is not uncommon to have a submission 20 or 25 years after an accident. Very few MSAs are submitted in the same year as the accident.

Overall, MSAs represent more than 40% of total submitted workers compensation settlement costs. On average, 22% of the total settlement is for the portion of the MSA covering Medicare Part D (prescription drugs) and 20% is for the portion of the MSA covering Medicare Parts A and B (hospital stays, office visits, and related services). About 58% of the submitted total workers compensation settlement is for costs other than MSAs, typically including indemnity coverage, medical costs not covered by Medicare, and other expenses such as attorney fees.

More than half of MSA submissions involve an attorney. About 54% of the time, MSA claimants seek assistance from an outside attorney when establishing their MSA arrangements. About 46% do not. The involvement of outside legal counsel does not vary much based on whether the total submitted settlement amount is large or small.

A six-year-old lawsuit against California’s State Compensation Insurance Fund, EK Health services – their utilization review vendor – and over a dozen utilization review physicians, recently settled. The case was filed by Electronic Waveform Lab, Inc., maker of H-Wave® – a physician prescribed drug-free medical device that helps those with chronic pain.

In 2011, Electronic Waveform Lab filed a lawsuit against EK Health and eleven Utilization Review Physicians alleging improper conduct in denying the H-Wave® Electrotherapy Device in Utilization Review. The lawsuit was amended to add State Compensation Insurance Fund and three additional Utilization Review Physicians as a party to the action and the case proceeded in federal court with a third amended complaint..

The case alleged, for the first time ever, a RICO statue violation accusing the groups of conspiring to deny physician treatment requests for the FDA cleared H-Wave® device, which can be prescribed by doctors as an alternative to opioids. The parties reached a confidential financial settlement in October 2017.

H-Wave®, a device made by Electronic Waveform Lab, Inc. of Huntington Beach, California, claimed that California’s State Compensation Insurance Fund, allegedly conspired with their contracted utilization review vendor and their independent contractor physicians who are responsible for approving or denying the use of any device for patients on a case-by-case basis, to deny all H-Wave® requests no matter what the medical necessity issues were.

As a result of the litigation settlement, State Fund, EK Health and EK Health’s Utilization Review Physicians will now recognize that Home H-Wave® is a drug-free treatment option, medically necessary in appropriate circumstances, and may be approved for use by injured patients pursuant to current evidence-based medicine and prevailing guidelines.

Authorization requests that prescribe Home H-Wave® should be evaluated on a case-by-case basis, focusing on the needs of the injured patient.

This could be perceived as a big win for medical device makers which deal with denials from claims adjusters on a daily basis. With today’s widespread focus on the national opioid epidemic, this case is encouraging for patients and their physicians who are demanding alternative treatments for chronic pain.

H-Wave, a scientifically-reviewed device that uses electrotherapy to treat chronic pain and speed recovery from injury, has been used by physicians and their patients for more than 35 years.

Most patients who are prescribed H-Wave have been suffering with chronic pain for more than 500 days and have exhausted other conservative treatments, according to the company. Besides being prescribed by doctors for work-related injuries or pain, H-Wave is currently used by more than 70 professional sports teams. More than 200,000 thousand people have been prescribed H-Wave over the last 35 years.

New York Attorney General Eric T. Schneiderman announced that his office has filed a lawsuit against Insys Therapeutics, Inc., a company that sells a highly addictive fentanyl drug called Subsys.

Although Subsys was approved by the Food and Drug Administration to treat excruciating cancer-related breakthrough pain, the complaint alleges that Insys recklessly marketed the drug for much wider use, covering a much broader set of patients.

Additionally, the company allegedly engaged in a pattern of deceptive and illegal conduct by downplaying the drug’s risks of addiction, bribing doctors to prescribe the drug, and lying to healthcare providers to skirt their authorization process.

As a result, the Attorney General’s office is seeking penalties and disgorgement of all revenues accumulated during the period of misconduct—up to $75 million.

“At a time when the opioid epidemic was ravaging New York, Insys Therapeutics allegedly marketed a drug illegally by blatantly disregarding the grave risks of addiction and death that opioids pose,” said Attorney General Schneiderman.

“As we allege, Insys showed a wanton disregard for the law and the lives of New Yorkers and we will hold them accountable. My office will continue to fight the opioid crisis at every level and hold corporations that prioritize profits above New Yorkers’ health and wellbeing to account.”

In late 2012, the FDA approved Subsys for the specific, limited treatment of breakthrough cancer pain in opioid-tolerant patients. Its purpose was to provide relief to cancer patients who were suffering from excruciating pain.

Beginning in 2012, Insys allegedly ignored this limited approval and instead broadly targeted many types of providers and patients, and represented that the drug was appropriate for flares of mild pain. Insys also downplayed Subsys’ risk of addictiveness.

The company further directed its sales representatives to urge providers to prescribe Subsys in high doses, which were more expensive than lower doses. A 30-unit prescription of the lowest strength medicine costs approximately $700, while a prescription for the highest strength costs over $3500.

Additionally, the complaint alleges that Insys sales representatives called on medical offices that employed providers who had been arrested for illegal opioid distribution.

In furtherance of their deceptive and illegal scheme, Insys allegedly formed a business unit devoted solely to securing prior authorization from health plans for as many patients as possible. Insys trained its prior authorization staff to imply that patients had cancer pain, even when they did not.

Additionally, Insys allegedly bribed prescribers to write prescriptions for Subsys. The company paid certain providers between $3000 and $5000 to act as “speakers,” at sham promotional events and implored its sales staff to obtain a “return on investment” on those payments by increasing prescription numbers.

According to Insys’ latest quarterly filing published in November, the company has received subpoenas from at least 15 other states and has already settled lawsuits filed by Oregon, Illinois, New Hampshire and Massachusetts.

Jose A. Guzman was operating a compactor when he was injured. He had been employed by Carmel Valley Construction as a laborer for a little less than six months.The compactor, which is used to pack down soil, hit a rock while Guzman was working on a hillside with a 45-degree slope. The compactor rose in the air, caused Guzman to fall backwards, and then fell on top of him.

The WCJ determined that Guzman sustained an injury to his back and psyche arising out of his employment, and that the psychiatric injury was caused by a “sudden and extraordinary employment condition” thus circumventing the Labor Code 3208.3 requirement for six months of employment as a prerequisite for a psychiatric injury..

In reaching this determination, the WCJ referred to the following evidence: Guzman had never been injured by having a compactor fall on him, he had never experienced such an incident before, there was no evidence that this type of injury had occurred in a similar fashion before, Guzman never had any close calls involving an injury when using a compactor, he never had an accident using a compactor before, and he never thought there was any risk of injury while using the compactor.

The WCJ concluded that “having a compactor fall on top of an employee is not something that would reasonably be expected to occur. This type of injury is not a frequent, regular, or routine part of the job. In fact, there is no evidence that having a compactor fall on an employee had ever occurred before. For this reason, it is reasonable to find that this injury was not something that could have been anticipated. This was not the type of injury that would be foreseeable.”

The State Fund  petitioned for reconsideration which was denied. The Court of Appeal reversed in the unpublished opinion of State Compensation Insurance Fund v W.C.A.B. and Jose Guzman

The Court noted that “the language and legislative history of section 3208.3 instruct that the Legislature’s public policy goals should be considered when determining whether an award of benefits is warranted. The Legislature made quite clear that it intended to limit claims for psychiatric benefits due to their proliferation and their potential for fraud and abuse. Therefore, any interpretation of the section that would lead to more or broader claims should be examined closely to avoid violating express legislative intent.”

Guzman failed to meet his burden of proving that a “sudden and extraordinary employment condition” caused his injury. Guzman did not provide any evidence establishing that it is “uncommon, unusual, and totally unexpected” for a rock to be in soil, for a compactor to rise when striking a rock, or for an operator to become unbalanced and to fall when the compactor rises on a 45-degree hillside. Indeed, he did not introduce any evidence regarding what regularly or routinely happens if a compactor hits a rock on a slope.

Machine learning offers more efficient tools to smoke out fraudsters, as students from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) learned during the recent ComputeFest 2018 Student Data Challenge, organized by Institute for Applied Computational Science (IACS).

During the nine-hour hackathon, student teams put their computational skills to the test in a race against the clock, using machine learning techniques to detect fraudulent insurance claims. Presented with data from more than 18,000 health care providers, the open-ended problem challenged students to devise, test, and then refine the best algorithmic technique to identify fraud.

The winning team, consisting of Xuefeng Peng, M.E., a computational science and engineering student, and T.H. Chan School of Public Health master’s students Yi Ding and Linying Zhang, who were able to find fraud with 95.7 percent accuracy. They used an autoencoder, a type of neural network, which learned common patterns in the dataset to decode genuine data points. Since the autoencoder was unable to decode anomalies, it was sensitive to fraudulent claims, Peng explained.

Teammates Amil Merchant, A.B., an applied math concentrator, and Kate Zhou, a first-year mechanical engineering Ph.D. candidate, scoured the web for examples of medical fraud, such as overbilling or prescribing too many drugs.

Another team, comprised of visiting graduate student Christoph Kurz, and Chan School graduate students Hannah James and Anna Zink, tried two approaches in parallel – a linear regression model and a random forest algorithm – to study patterns and distinguish outliers. The students were surprised to find that the simplest technique, linear regression modeling, yielded the best results.

The massive data set included 86 features, such as percentage of a provider’s patients who suffer from depression or diabetes. Representing those features in a model through linear and nonlinear combinations was a challenge, said Alexander Munoz, A.B., an applied math concentrator.

Each team was able to submit an answer three times per hour, but only received feedback on how accurate their results were collectively. Using their most recent feedback, Munoz and teammates Eshan Tewari, A.B., and statistics Ph.D. candidate Niloy Biswas considered which features to include in the next iteration of their model.

The challenge was designed to teach students some fundamental machine learning techniques, while emphasizing their practical applications, said competition architect Marouan Belhaj, an IACS Fellow.

Many students had never encountered an unsupervised problem before, but those are the types of situations fraud detection agents often face, where billions of dollars and millions of lives are at stake. With so many ways to trick the system, machine learning is an ideal method to detect fraud quickly and precisely, while there is still time to intervene, he said.

“In real-world fraud detection, you rarely get feedback from inside the company about how your model is performing,” he said. “To improve the model is very difficult. You really need to think like a hacker or someone trying to defraud the system to understand which techniques you might use to trick the system and then try, through the modeling, to see if your results actually confirm your ideas.”