Author: WorkCompAcademy

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 The Court of Appeal reversed in the unpublished opinion of State Compensation Insurance Fund v W.C.A.B. and Jose Guzman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.”

After years of lobbying in Washington, U.S. telehealth providers have the first hints that the dam could break on public funding for an industry they say could save taxpayers billions.

Reuters reports that four bills that could be signed into law over the next year carry the solutions to barriers that have prevented the United States’ huge over-65 health program Medicare from reimbursing doctors’ and medical visits, which often start over the phone.

The bills come at a time when the industry’s claims of cost savings – powered by apps and mass smartphone usage – have begun to gain traction with private insurers striving to save on healthcare costs.

One issue for public spending on telehealth has been the inability to charge across state lines. Another is that Medicare does not recognize medical consultations that do not happen in person as the equivalent of a visit to the doctor.

The fate of legislative amendments that unlock these barriers is far from clear in a fractured U.S. Congress, but investors and some of the world’s big healthcare providers are already circling firms like the U.S. sector’s dominant player Teladoc Inc.

Analysts say Teladoc racked up 75 percent of reported video or phone visits, according to 2016 estimates, but European insurance company Allianz Group earlier this month committed $59 million to American Well, one of a handful of smaller privately-run operations expanding in the sector.

Apple Inc’s Heart Study app, which flags irregular heart rhythms in users wearing Apple Watches, allows them to instantly connect with a doctor using American Well’s technology.

American Well Chief Executive Roy Schoenberg says that while revenue is steadily rising in the industry, it could grow 10-fold if payment parity, state-line and location-based constraints were lifted.

“There is a big black line between the availability of telehealth services to Americans under the age of 65 and Americans that are above the age of 65,” Schoenberg said.

“This (legislation) would be an earthquake.”

The California Self-Insurers’ Security Fund is a nonprofit organization charged by the state Legislature with continuing payment of workers’ compensation claims when a self-insured entity is unable to do so. When the Fund steps in to provide such payments, it is required by law to seek reimbursement from the employer. The law permits groups of employers to band together into self-insurance groups, and the Fund is also responsible for paying workers’ compensation claims when such groups cannot.

The Healthcare Industry Self-Insurance Program is one such group. In 2013, the California Department of Industrial Relations ordered the Fund to assume the Program’s workers’ compensation claims.

The Fund hired the law firm of Nixon Peabody to represent it in order to seek reimbursement from the employer group. In November 2013, the Fund filed a lawsuit naming 304 members of the Program as defendants, approximately 170 of which have since settled.

Attorney Andrew Selesnick served as Chair of the Health Care Department at Michelman & Robinson, LLP (M&R), overseeing and managing a team of attorneys who represented clients in the healthcare industry. Since 2014, M&R served as attorneys for employer defendants in this collection case. The representation of those parties was handled primarily by four attorneys at M&R, including Selesnick. Selesnick was actively involved in the case, including participation in a confidential discussion pertaining to moving parties’ liability and damages.

In February 2017, Selesnick left M&R and joined the Nixon Peabody law firm. Nixon Peabody was promptly advised of the potential conflict issue by M&R. On or about March 8, Selesnick “parted ways” with Nixon Peabody.

On March 15, moving parties filed a motion to disqualify Nixon Peabody. They argued that Selesnick had done prior work for the moving parties in the same action, and as a result, Nixon Peabody and all its attorneys had a conflict of interest as a matter of law. M&R claimed that Selesnick had not shared confidential information with them, and the firm had put an “ethical wall” in place. The trial court granted the motion and M&R appealed. The Court of Appeal reversed in the published case of California Self-Insurers Security Fund v Superior Court, concluding that “automatic disqualification was not required under the facts.”

There “is no question that if Selesnick were seeking to represent the Fund, he could not do so. There is also no question that if Selesnick continued to work at Nixon Peabody, the entire firm would be disqualified. The question that is left is whether Nixon Peabody and all its attorneys are also prohibited from representing the Fund given all the relevant facts, including that Selesnick no longer works at Nixon Peabody, and was only there for a very brief period.”

“We conclude the trial court must perform an analysis regarding whether confidential information was, indeed, transmitted from Selesnick to the attorneys working on the matter at Nixon Peabody.” The question, then, is whether Selesnick’s tenure at the firm endangers the duty of confidentiality he owes to real parties; if it does, disqualification is required. If it does not, then the court must exercise its discretion to determine whether other reasons compel disqualification.

A former National Football League player accused of participating in a $1.5 million scheme to defraud a Sacramento business that managed workers’ compensation claims has been sentenced to 24 months in federal prison.

Marcus Buckley, 46, of Weatherford, Texas, was sentenced Thursday in U.S. District Court in Sacramento. Judge Troy L. Nunley also ordered Buckley to pay more than $1.58 million in restitution, according to a U.S. Attorney’s Office news release.The Court recommended that he be incarcerated at FCI Seagoville or FCI Texarkana, Texas.

Nunley’s sentence was nine months less than what prosecutors had asked for, and that Buckley had apparently agreed to, after his guilty plea on a single count of money laundering.

Buckley played professional football for seven seasons, from 1993 to 2000, with the New York Giants. During that time, the Giants had workers’ compensation insurance coverage through Pennsylvania Manufacturer’s Association Insurance Group, or PMA.

In 2006, Buckley filed a workers’ compensation claim against the Giants for cumulative stress injuries sustained while playing football, part of that time in California. During the first week of November 2010, Buckley, the Giants and PMA settled Buckley’s claim for $300,000 as part of a “compromise and release” agreement, according to the news release.

After Buckley’s claim was settled, between late 2010 and June 2011, Buckley prepared and filed numerous requests for additional reimbursement under his claim. He prepared false invoices and statements from medial providers for medical service purportedly provided. He also prepared false credit collection notices from collection agencies purportedly seeking payment from Buckley from various medical providers for past-due medical bills, the news release said.

Buckley sent the false invoices, statements and credit collection letters to his co-defendant, Kimberly Jones, who was a claims adjuster with Gallagher Bassett Services at its Sacramento office. The firm was a third-party administrator that managed workers’ compensation claims in California on behalf of PMA.

Jones was aware that Buckley was not entitled to additional reimbursement under his disability claim, and that the documentation and requests he submitted were false, the news release said. Jones nevertheless caused Gallagher Bassett to issue checks payable to Buckley, and he ultimately received more than $1.58 million to which he was not entitled.

Jones pleaded guilty to wire fraud in October 2015, is to be sentenced Feb. 8 by Judge Nunley at 09:30 AM in Courtroom 2.