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How to Help Reverse the Opioid Epidemic

Across the U.S., the number of reported events exemplifying the opioid and heroin epidemics continues to skyrocket. U.S. Government Publishing Office data shows that the usage of both prescribed stimulants and prescribed opiates increased by a factor of 19 in just two decades since 1994(1). On Dec. 18, 2015, the U.S. Centers for Disease Control and Prevention (CDC) released a report showing drug overdose deaths reached record highs in 2014, fueled in large part by the abuse of narcotic painkillers and heroin. In 2014, more than 47,000 Americans died from drug overdoses, an increase of more than 14% from 2013. About 61% of those deaths involved the use of opioids. From 2000 to 2014, the report noted that nearly half a million people have died from overdoses in the U.S. In 2014, there were approximately one and a half times more drug overdose deaths than deaths from motor vehicle crashes!(2)

A very worrisome statistic and trend…

For workers’ compensation insurers, opioid use in treating chronic pain has also exploded over the past two decades. Although there appear to be some signs that opioid use is finally cresting, insurers still have a long way to go in helping to ensure that physicians and the injured workers they treat are fully educated on the pros and cons of using opioids with various types of injuries and pain. As the Risk & Insurance article “Paying for Detox – The Opioid Epidemic Is Addressed by Detoxification Programs” notes, some workers’ compensation insurers have been funding tapering and detoxification programs to help dependent or addicted patients wean themselves off the very medications that were designed to ease their pain(3). Unfortunately, recidivism is common, with experts noting that it can take several attempts to wean someone off narcotics.

This article will highlight some of the challenges in front of us and share some innovative ideas on potential ways to help prevent opioid dependency and addiction before the habits requiring tapering and detoxification programs are ever formed.

The Challenge in Front of Us

In January 2011, USA Today shared a powerful story about David Fridovich, a three-star Green Beret general who has become an advocate for warning soldiers about the epidemic of chronic pain and the use of narcotic pain relievers sweeping through the U.S. military(4). Much like others across the country who have suffered a severe back injury, the general began taking narcotics for chronic pain in 2006. Over time, the general became addicted to narcotics. During one 24-hour period the general took five dozen pain pills. After going through a detoxification program, the general has been helping other soldiers avoid the complications he faced because he was unaware of the addictive nature of the pills he was taking.

In a recent book about the opioid and heroin epidemic in the U.S., Dream Land author Sam Quinones shares his research on the history of how we ended up where we are today. From a workers’ compensation perspective, the author shared a story about a prison guard who had injured his back during a fight with an inmate. The doctor, who took the guard off of work for six months, also prescribed opioids to be taken twice a day for 30 days. After becoming severely addicted, the guard said, “It really humbles you. You think you’re doing stuff the way it’s supposed to be done. You’re trusting the doctor. After a while, you realize this isn’t right, but there really isn’t anything you can do about it. You’re stuck. You’re addicted.”

Both stories illustrate how the use of painkillers can lead to dependency and addiction without warning. They also highlight the critical role prescribing physicians play in educating patients about the warning signs and addictive nature of opioid prescriptions. As part of this education process, prescribing guidelines and analytics can play an important role in driving better outcomes.

Opioid Prescribing Guidelines

For workers’ compensation insurers, it is critical to understand the opioid prescribing guidelines that underlie the way physicians are treating injured workers. The more the insurers can help educate physicians on best practices, the better off insurance companies may be in helping to prevent any issues that may arise because of unnecessary or excessive opioid prescribing.

The CDC worked with the National Drug Institute, Substance Abuse and Mental Health Services Administration and the Office of the National Coordinator for Health Information Technology to review existing opioid prescribing guidelines for chronic pain. Their review and analysis of eight prescribing guidelines highlighted a number of important provider actions, such as the review of pain history, medical and family history, pregnancy, prescription drug monitoring programs (PDMP), urine drug screening, evaluations of alternatives to opioids, rational documentation, tapering plans, referrals for medication assisted treatment, evidence review, conflicts of interest and more(5). In January, Kentucky Attorney General Andy Beshear announced his support for national guidelines for prescribing opiates for chronic pain, stating: “In Kentucky, we face a crushing epidemic of addiction. One of my core missions as attorney general is to better address the drug problem faced by our Kentucky families and workforce.”(6) In his speech, the attorney general mentions that he is joining other state attorneys general in voicing support for the CDC guidelines for prescribing opiates for chronic pain.

California’s “Division of Workers’ Compensation Guideline for the Use of Opioids to Treat Work-Related Injuries” documented treatment protocols for three specific pain categories:

  1. Opioids for acute pain (pain lasting as much as four weeks from onset)
  2. Opioids for subacute pain (one to three months)
  3. Opioids for chronic pain and chronic opioid treatment (three months or more)(7)

The guidelines state that, in general, opioids are not indicated for mild injuries such as acute strains, sprains, tendinitis, myofascial pain and repetitive strain injuries. Just as important, the guidelines clearly warn physicians to consider and document relative contraindications (e.g., depression, anxiety, past substance abuse, etc.). The document provides an abbreviated treatment protocol for the three pain categories that address important topics like prescribing a limited supply of opioids, documentation, accessing California’s PDMP, monitoring opioid use, evaluating the use of non-opioid treatments, completing opioid use, educating patients on opioid usage and potential adverse effects, responsibly storing and disposing of opioids, tracking pain level, screening for the risk of addiction, testing urine for drugs and more.

At the end of the day, it is important for workers’ compensation insurers and physician employees to clearly understand the opioid prescribing guidelines that help physicians achieve a proper balance between treating workers’ pain and keeping them safe from any adverse impacts of excessive opioid usage. With more insurance companies leveraging early physician peer-to-peer outreach to open a dialogue between the insurance company physician and the treating physician, knowing prescribing guidelines and sharing that knowledge will be more important than ever in improving outcomes and return to work.

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The Inspiration for Using Analytics

For more than a decade, Deloitte Consulting’s Advanced Analytics & Modeling practice has been developing claim predictive solutions designed to help insurance companies, self-insureds and third-party administrators better segment and triage predicted high-severity from low-severity claims, enabling business decisions and actions that can help drive loss cost savings of as much as 10% of an organization’s annual claims spending. (See Claims Magazine articles “Analytics on the Cloud: Transforming the Way Claims Leverages Advanced Analytics “(2011)(8), “Enhancing Workers’ Comp Predictive Modeling With Injury Groupings” (2012)(9), “Reaping the Financial Rewards of End-to-End Claims Analytics” (2014)(10) and “The Challenges of Implementing Advanced Analytics “(2014).(11) A large part of the claims modeling success is attributed to gaining actionable insights as early as first notice of loss before adverse chain reactions can set in, and shortly thereafter with the three-point contact investigation where additional information is learned about the patient’s history and co-morbidities.

The authors, having observed the success of predicting claims complexity outcomes early in the claim’s lifecycle, became excited about the application of similar models to help identify early warning signs of future excessive opioid usage by injured workers. With as much as 60% of workers’ compensation spending going toward medical costs, one-fifth of that related to prescription drugs(12), we believed the use of predictive models… combined with physician peer-to-peer outreach and proper prescribing guidelines… could help workers’ compensation insurers improve the lives of the injured workers while significantly reducing medical expenditures. The following sections explain the analytics journey undertaken to help move the needle on this issue.

Defining the Target Variable: Predicting Future Excess

An important part of any analytics journey is defining the target variable (i.e., what we are trying to understand and predict). Excessive opiates usage is difficult to ascertain, as higher consumption may indeed be necessary for the most severe injuries. Therefore, various tests on the most appropriate target variables were conducted to probe these hypotheses. Many versions of opioid supply days were tested (i.e., ultimate total supply days across all opiates drugs prescribed to, and consumed by, the injured worker). Variations of opiates prescription counts were also considered (i.e., ultimate count of opiates prescriptions through the lifecycle of the claims). Similarly, supply units were analyzed (i.e., ultimate sum of all individual opiates pills prescribed to, and consumed by, the injured worker from the day of the injury until the claim closure). Figure 1 illustrates the calculation of total supply days for three different opiates that were prescribed to, and consumed by, the injured worker over the duration of his workers’ compensation claim:

fig1

Figure 1. Supply Day Illustration

Methodology and Data Considered

Using predictive analytics and data science, a number of algorithms were built, tested, iterated and fine-tuned to better understand those like-injury cohorts (i.e., same injury sustained) that consumed more opiates than their corresponding peers who managed to consume a lower amount. Various thresholds of “excess” were analyzed by injury and venues, thus controlling for differences that affect the prescription base.

By testing these algorithms, it was determined that segmentation was similar across the different target variables. However, total supply days seemed to exhibit the most robustness from a modeling perspective and had intuitive interpretability (i.e., number of days an injured worker consumes opioids).

The algorithms used more than eight years of lost time workers’ compensation claims to accumulate enough data credibility. Claims were selected for various injury groups where opiates were prescribed and consumed for at least one prescription. The data was organized for a longitudinal study observing a claimant over time and quantifying her consumption of opiates. The comparison to this usage to like-injury counterparts over thousands of cases and using hundreds of attributes is what helped the model shed light on claimants who consumed excessive amounts of opioids relative to the entire population.

Over the years, Deloitte healthcare practitioners and claims professionals used ICD-9 codes that describe a disease or condition, as well as National Council on Compensation (NCCI) nature of injury and body part codes, to create more than 70 proprietary injury groups that are factored into the model to provide enhanced segmentation within like injury claims.(13) For illustration purposes in this article, we presented results for the injury group representing medium- and high-complexity spinal disorders (e.g., ICD-9 codes 722.0 – displacement of cervical intervertebral disc without myelopathy, 722.10 – displacement of lumbar intervertebral disc without myelopathy, 724.9 – other unspecified back disorders, etc.). We selected medium- and high-complexity spinal disorder claims because they are significantly more severe than the average workers’ compensation claim, and, as expected, these claimants typically have more prescriptions filled by their physicians. In addition, the models aren’t run on just any injury group. For example, an injury group containing low-complexity injuries such as finger cuts and minor open wounds would not be part of our analysis. Claimants with these types of low-complexity injuries do not require opioids, given the nature of injury, so it would not make sense to include these injury groups in the model.

Predictive variables

The information attributes used to understand excessive consumption were sourced from similar data sources used in developing our claim-severity models. They are large in number and varied in terms of coverage. They include claimant data (e.g., claimant age, gender, job classification, years of employment, wage, claim filing lag, cause and nature of injury, etc.), prior claims data (e.g., prior frequency and type of claims), employer information (e.g., financial characteristics, years in business, etc.), injury circumstance (e.g. location, type, body part injured), three-point contact information (e.g., co-morbidities, early medical services) as well as other standard external third-party data sources (e.g. lifestyle, behavioral, geo-demographic).

Modeling Results

The lift curves shown in Figure 2 illustrate the segmentation achieved by using multivariate equations to predict total supply days. Each claim below was scored using the model, which generated scores from 1 to 100, with lower scores corresponding to smaller predicted supply days and higher scores corresponding to larger predicted supply days. This score is represented on the x-axis of Figure 2, where each “decile” refers to a group of claims that compose 10% of the data. The actual supply days are tracked and plotted on the y-axis in the appropriate decile.

fig2
Figure 2. Lift Curve – GLM model

As one can see from Figure 2, injured workers studied who are predicted to fall in decile 10 have more than 18 times the supply days as workers predicted to fall in decile 1. Injured workers studied who scored in decile 10 consume, on average, more than three and a half years of opioid supply days! This very large and widespread segmentation suggests that individuals sustaining the same injury can still vary significantly in their future consumption of opioids… and this variation ranges from a couple months to more than three and a half years.

In Figure 3, we compare two 24-year-old male claimants with very similar injuries but drastically different predicted outcomes.

fig3
Figure 3. Similar Injuries, Drastically Different Outcomes

As one can see from Figure 3, the claimant scoring in decile 10 has a number of variables that correlate with the potential for excessive opioid use. Given the combination of co-morbidities, worker health, reporting lags, employer business conditions and additional attributes collected on the individual from external sources (e.g. lifestyle and behavioral data), it is possible for the insurance company to identify and analyze the early drivers that may lead to future excessive opioid the first few days after receiving notice of the claim.

With more than 60 predictive variables in the model (e.g., co-morbidities, prior claims history, job classes, injury causes, business characteristics, claim characteristics, etc.), the most influential categories and reason codes driving the score represent “eyeglasses” for the insurance company physician. The model helps the insurance company physician weigh together multiple pieces of information but doesn’t replace his judgement. Analogously, many of us wear eyeglasses to read a dinner menu, but those eyeglasses do not order the food for us.

Armed with a plethora of facts and the opioid prescribing guidelines, a physician can open a dialogue with the treating physician to help guide the discussion in a direction that best benefits the injured worker. The physician, using the prediction from the model, can tailor appropriate decisions and actions – from low touch or regular prognosis for the first claimant above, to a much more closely managed case for the second individual.

Figure 4 provides a drill-down into the actual versus predicted supply days achieved in the highest-scoring 30% of medium- to high-complexity spinal disorder claims for the train/test data and validation data. Using the train/test/validation approach, the models were trained and enhanced using approximately 70% of the claims data. The validation results shown below were derived from the remaining 30% of the claims data that was held in “cold storage.” Using this kind of blind-test validation data helps ensure that the model’s estimated “lift” (i.e., segmentation power) is true and unbiased.

fig4
Figure 4. Highest Score Drill-Down

Approximately 60% of claims scoring in deciles 8, 9 and 10 exceed one year in supply days. For a quarter of the claims, the injured workers take in excess of four years in supply days of opioids. At the far end of the spectrum, roughly 4% of medium- to high-complexity spinal disorder claims scoring in deciles 8, 9 and 10 will exceed a decade’s worth of opioids in supply days.

One Last Check

In addition to the generalized linear models (GLMs) discussed above, focused on predicting the actual supply days, we also ran a logistic regression model focused on predicting which claimants would take more than a year’s supply of opioids. Using classical statistical measures of precision (i.e., how many of the positively classified results are relevant), recall (i.e., how accurate the model is at detecting the positives) and specificity (i.e., how good the model is at avoiding false alarms), we achieved the following results: a precision of 59%, a recall of 64% and a specificity of 72%.(14) As one last test of the logistic regression model’s segmentation power, we calculated the receiver operating characteristic (ROC) curve.  At almost 80%, it represented a good model from a statistical perspective. Although illustrative, we prefer the GLM model presented above.

Behavioral Economics and Nudges

All across the country, physicians and medical boards are spreading the word about the responsible prescribing of opioids. State and federal agencies are toughening criminal and administrative penalties for doctors and clinics that traffic in prescription drugs. Governors across the country are forming opioid working groups that include senior Health and Human Services professionals, attorneys general, drug courts, hospital professionals, elected officials and more.

Research shows that a number of factors can help insurance companies better understand the severity of claims early on in the life cycle of a claim. Two studies by the National Council on Compensation Insurance, Inc. (NCCI) highlight the effect of obesity on workers’ compensation claims. According to “Reserving in the Age of Obesity,” a Nov. 1, 2010, NCCI study by Chris Laws and Frank Schmid, the ratio in the medical costs per claim of obese to nonobese claimants deteriorates over time from a ratio of 2.8 at the end of one year, to 4.5 at the end of three years, to 5.3 at the end of five years.(15) In a following study from May 29, 2012, “Indemnity Benefit Duration and Obesity,” authors Frank Schmid, Chris Laws and Mathew Montero found the duration of obese claimants is more than five times the duration of nonobese claimants, after controlling for primary International Classification of Diseases (ICD)-9 code, injury year, state, industry, gender and age for temporary total and permanent total indemnity benefit payments.(16) Deloitte’s claim predictive models have shown that the number of medical conditions at the time of injury plays a significant role in determining the ultimate severity and potential for excess opioid usage (e.g., claims with three or more existing medical conditions are 12 times more costly than claims with no existing medical conditions).

With energy and momentum building around addressing the opioid epidemic, insurance companies can leverage behavioral economics and data-driven nudges to help treating physicians improve outcomes and return to work. Leveraging prescribing guidelines and the model results and reason codes that help explain the top five drivers behind the model prediction, insurance company physicians can be more strategic in shaping the discussions they have with treating physicians. For the highest-scoring claims, the insurance company may want to use a mix of peer-to-peer contact and data-driven nudges (e.g., “did you know that 95% of physicians we work with follow the state prescribing guidelines and only prescribe 30 days of opioids for this type of claim,” ”for injuries of this type, physicians we work with usually prescribe less than x milligrams of strength,” etc.). For lower-scoring claims, the insurance company may touch base with the treating physician but skip any reference to data-driven nudges.

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Conclusion

In the end, it is important for workers’ compensation insurers and their medical professionals to clearly understand opioid prescribing guidelines and the internal and external factors that could affect the opioid usage and habits of their injured workers. A Business Insurance white paper titled “Opioid Abuse and Workers’ Comp – How to Tackle a Growing Problem,” described the challenge well: “Monitoring or managing opioid abuse is another key step for workers’ comp managers. It’s not enough to simply dive into the data and look for claimants who appear to be using lots of opioids. Nor is preventing doctors from prescribing opioids a desirable action. The goal is to find claimants who are struggling with a problem they never intended to have, and support those claimants in solving that problem.”(17)

However, our hope is that through the use of predictive analytics (i.e., the ability to identify, in the first few days of receiving a claim, individuals most likely to become high consumers of opioids), prescribing guidelines and physician peer-to-peer outreach, we can help increase insurers’ and treating physicians’ awareness as they work to help prevent injured workers from struggling with dependency and addiction before the behaviors or habits ever form.

As former British Prime Minister Benjamin Disraeli once said, “What we anticipate seldom occurs; what we least expect generally happens.” The science and passion exists today to better anticipate opioid trends and help prevent opioid dependency and addiction before it happens.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

This communication contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collectively, the “Deloitte Network”) is, by means of this communication, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this communication.

Copyright © 2016 Deloitte Development LLC. All rights reserved.

[1] James W. Harris, PhD, CSO Vatex Explorations LLC, www.gpo.gov

[2] http://www.cdc.gov/media/releases/2015/p1218-drug-overdose.html, http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6450a3.htm?s_cid=mm6450a3_w

[3] http://www.riskandinsurance.com/paying-detox/

[4] http://usatoday30.usatoday.com/news/military/2011-01-27-1Adruggeneral27_CV_N.htm

[5] http://www.cdc.gov/drugoverdose/prescribing/common-elements.html

[6] http://harlandaily.com/news/6473/cdc-guidelines-will-help-ky-with-rx-drug-abuse

[7] http://www.dir.ca.gov/dwc/ForumDocs/Opioids/OpioidGuidelinesPartA.pdf

[8] http://www.propertycasualty360.com/2011/02/22/leveraging-analytics-in-workers-comp-claims-handli

[9] http://www.propertycasualty360.com/2012/07/23/enhance-workers-comp-predictive-modeling-with-inju

[10] http://www.propertycasualty360.com/2014/02/03/reaping-the-financial-rewards-of-end-to-end-claims

[11] http://www.propertycasualty360.com/2014/10/01/the-challenges-of-implementing-advanced-analytics

[12] www.ncci.com

[13] http://www.propertycasualty360.com/2012/07/23/enhance-workers-comp-predictive-modeling-with-inju

[14] Precision measures the ratio of true predicted positives to the ratio of true predictive positives plus false predicted positives. Recall, also referred to as sensitivity, measures the ratio of true predicted positives to the ratio of true predicted positives plus false predicted negatives. Specificity measures the ratio of true predicted negatives to the ratio of true predicted negatives plus false predicted positives.

[15] https://www.ncci.com/Articles/Documents//II_research-age-of-obesity.pdf

[16] https://www.ncci.com/Articles/Documents/II_Obesity-2012.pdf

[17] http://www.businessinsurance.com/article/99999999/WP05/120509952

connected

How to Insure the Sharing Economy

During the snowstorm that hit the East Coast in January, I took some time to clean up my office and read reasonably current newspapers and trade magazines. I quickly identified many opportunities for new insurance products, mostly around shared assets. For example, an article on Millennials and the sharing economy explained that (primarily young) people make money by sending selfies of what they are wearing every day to a website called CovetMe; they get paid based on the brands and looks they are sporting.

They Uber their way to work, school or social events (when did Uber become a verb?) as a driver or passenger; they use their subscription to a shared car service such as Zipcar to take occasional trips; or they get paid for allowing advertising on their own car by subscribing to companies such as Carvertise. FlightCar gives you free parking at big airports if you let other travelers use your parked car when you are traveling. Similar sharing activities take place with homes, clothing and accessories, occasionally used tools and equipment and even medical equipment.

All these shared assets need to be covered in different ways than the traditional, personal lines homeowner’s or car insurance policies. Occasionally renting out assets to third parties or shared ownership of one asset between non-family members creates a different risk profile than self-use only, both for property coverages and especially for liability.

Think about deductible coverage between multiple owners in case of a claim, good driver discounts or multiple non-familial owners getting involved in the same accident, as liable parties and as claimants. The insurance market has been pondering insurance solutions for the shared economy for a while now and found ways to cover Uber drivers or Airbnb landlords or offer non-owner car insurance. As an industry, however, we defaulted to our classical model of insurance and put a commercial coverage, bought by the shared economy company for their members,  on top of individual personal insurances where needed.

It works, but, as one can imagine, it is a bit clunky. Especially on larger claims, I expect delays and issues to occur concerning liability, wear and tear, acceptable use of assets and confusion around which policy should pay followed by subrogation. Now, most shared-economy companies have stated that they will reimburse their members for losses and will figure out later what is covered by which insurance. This is a good thing for their members, of course, but it doesn’t necessarily help insurers very much.

We should be able to do better and create truly new insurance coverages for the shared economy. For example, why wouldn’t an insurer work with one of the new tech companies that provides people with a cloud solution to document all of their assets with pictures, videos, sales receipts or warranty documents? Why wouldn’t an insurer create a comprehensive coverage for property and liability for all these clients’ assets, under the assumption that they will be shared? Tag the key assets with a sensor and learn from usage data. Use telematics data on the car use. Limit home rentals to one or two partner companies and learn from usage analytics.

Why wouldn’t a carrier try a pilot with a segment of young people with limited assets, in a single location?

I know that this is not a simple proposition and that, in creating these kind of coverages, many hurdles will be encountered. I do think, however, that the market is ready, and that the sharing economy will become a force to be reckoned with soon. So, we might as well figure out how to insure and service that force.

As my colleague Mark Breading stated in his recent research brief, Insurance in the Connected World: Observations on Opportunities and Threats, “Actively participating in the rapidly growing sharing economy will be critical for personal lines insurers. Asset ownership is shifting and requiring a different approach for managing and protecting the assets.”

It is not going to be easy, but customers will count on our industry to develop solutions to protect their shared assets. We have successfully been supporting changing economies and technologies for centuries now – I am sure we’ll also find a solution for the new sharing economy in a connected world.

How to Avoid Work Comp ‘Fact-cidents’

Every workers’ compensation claim is not preventable, when you consider that some are deliberate. With due respect to the art and science of safety, preventing real physical accidents and repetitive traumas is essential. However, we also need to be mindful of and prepared for the non-accident accident. Let us refer to these situations as “fact-cidents” because their construct relies on the ability of a claimant to tell a credible story void of facts.

First of all, let’s establish fact-cident detection as an employer’s responsibility. An adjuster with the best list of “red flags” cannot match the gut instinct of an astute employer who knows an employee’s history and extraneous issues and has opportunity to look that employee in the eye. An unwitnessed fall out of a chair or a bump against a restroom-stall door, or a “giving out” of the knee when turning with a parts tray in hand can be very valid claims… until they are not, mainly because the employer knows something deeper about the employee’s motivation.

The employer must share concerns with the adjuster within the early hours or days of a claim to support heightened focus. Most fact-cidents cannot simply be denied. Very quick work is required. The good news is that fact-cident defense is time-consuming but not complicated. It simply involves obtaining multiple verifications of the story. Just like the old saying, “there is no such thing as the perfect crime,” there is also no such thing as the perfect false claim. Enough prodding will diminish credibility and isolate the fact-cident for the house of cards that it is.

Quick Tip: Ask, Ask Again and Ask Some More

An injured worker should be required to reiterate his story four to six times within the first 48 hours. Here is an optimal sequence:

– Report to supervisor, who writes down claimant’s account

– Call in to triage line, where a nurse interviews and records claimant’s detailed account

– Workers’ comp lead (WC or risk manager, HR, benefits, company nurse, etc.) requires discussion and writes down another reiteration of the incident

– Treating doctor requires a detailed reiteration of the incident as part of history

– Adjuster takes recorded statement of the claimant’s account

– Adjuster and employer-leaders separately circle back to claimant after doctor visit to get claimant’s version of the doctor’s assessment

With these multiple stories and queries, the true detective work begins in comparing and sharing claimant versions. Fact-cident claimants notoriously will assume what certain parties want to hear and adjust stories accordingly. They also may enhance their story gradually with each reiteration. After medical visits, they often alter what actually happened or was said by the doctor. Sadly enough, many seem to think they can play all sides to the middle with no cross-checking among the crowd. Don’t let that happen!

The investigative test relies on comparing all versions and then, as might be indicated, sharing with other parties. For example, if the initial supervisor and HR manager reports mention non-falling incident with ankle pain but the version to the doctor claims a fall to the floor adding hip, back and elbow pain, you have an immediate piece of evidence validating suspicions. You can confidently invest and engage denial, defense, independent medical exam (IME), surveillance, field nurse, et. al.

Inconsistencies can also be presented to the doctor for review and revision or re-exam to correct any false reliance on claimant’s story. If possible, with cooperative providers, the early internal reports can be shared with treating doctor in real time so she can diligently test the employee’s credibility against other statements.

An even more powerful reason to collect and solidify various versions is to avoid future attorney representation and fact-cident influencing. Worst-case scenario with lack of early employee statements is that an attorney gets to coach the employee into a tighter self-serving story later on.

When you suspect an accident is actually a fact-cident, don’t accept any aspect at face value. Put in the time to either confidently validate and pay the claim or justify heavy investments in defense.

As a bonus, from the big-picture perspective, this type of consistent diligence establishes a general no-nonsense workplace attitude and culture when it comes to workers compensation.

66 Red Flags in Work Comp Claims

This article started as another “Top Ten” list, but I quickly realized that, when looking for potential fraud or compensability issues in a workers’ compensation claim, there are many more than 10 red flags – I came up with 66. You can probably think of more. Please add them in the comments.

The following is a loosely organized list of red flags signaling potential fraud or abuse by a workers’ compensation claimant. Keep in mind that even if the claim has all of these red flags, this does not necessarily mean the claimant is committing fraud or that you have grounds to deny compensability of a claim. However, the presence of some of these red flags should cause you to investigate further. Also note that I am using the term “fraud” broadly to include general wrongdoing on the part of the claimant and not specifically referring to a legal cause of action.

Here we go:

  1. Late reporting – If an employee is really injured on the job, it is unlikely the employee will wait days or weeks to report the injury.
  2. The details of the accident are sketchy.
  3. The employee has difficulty recalling what happened.
  4. The employee changes the description of the accident when inconsistencies are pointed out.
  5. The nature of the injury is not consistent with the nature of the work done by the employee.
  6. The date, time or location of the accident is unknown or forgotten.
  7. The details of the accident are inconsistent with the employee job duties.
  8. The accident occurs in an area where the injured employee would not normally be.
  9. Fellow workers hear rumors circulating that the accident was not legitimate.
  10. The employee gives completely different versions of the accident to the employer, the adjuster and the doctor.
  11. The employee keeps modifying the story of what happened.
  12. The employee leaves out pertinent information.
  13. The details of the accident vary from medical report to medical report.
  14. There are no witnesses to the accident, and the employee normally works around other people.
  15. There are witnesses, but their version of the accident differs from the employee’s.
  16. The nature of the injury is unusual for the employee’s line of work.
  17. The employee’s co-workers express doubt that the accident occurred.
  18. The employee is disgruntled about some aspect of his job.
  19. The employee was demoted or passed over for a promotion.
  20. The employee is on the list to be laid off.
  21. The employee is on “positive improvement needed” status and is about to be terminated.
  22. The employee has had numerous prior employers.
  23. The “accident” occurs immediately before a strike, plant closing or the end of seasonal employment.
  24. The employee is a new hire.
  25. The accident occurs near the end of probationary period.
  26. The claimant is a seasonal worker.
  27. The employee has an early Monday morning accident before the supervisor or other employees see him on the job (meaning the accident might have occurred off the job over the weekend).
  28. The injured employee is not at home during the normal workday.
  29. The employee is always sleeping when the adjuster calls or cannot be disturbed.
  30. The employee’s family member is vague or noncommittal about when you can reach the employee.
  31. The employee uses the address of friends or family members and has no definite address or uses a Post Office box as an address.
  32. The employee’s spouse is not working and is drawing workers’ comp indemnity benefits, Social Security disability payments, welfare or unemployment insurance, and the employee wants the same lifestyle.
  33. The employee inquires about a settlement early in the claim process.
  34. The employee was having financial problems.
  35. The employee is nearing retirement age.
  36. The employee files for benefits in a state other than where the accident occurred.
  37. The employee fails to report other work income while drawing indemnity benefits.
  38. The employee took excessive time off just before the injury.
  39. The employee is in the middle of a divorce or other family disturbance.
  40. The Social Security number used by the employee belongs to someone else.
  41. The employee applies for Social Security benefits before the injury occurs.
  42. Income from workers’ comp, disability or other sources exceeds the employees prior after-tax income.
  43. The employee protests about returning to work and never seems to improve.
  44. All the injuries are subjective – pain without trauma, soft-tissue, emotional.
  45. The employee changes doctors frequently (“doctor shopping”) or changes doctors when released to return to work.
  46. The employee has excessive treatment for soft-tissue injuries.
  47. The medical treatment reported by the employee is different from the medical care stated in the medical reports.
  48. The nature of the medical treatment changes from one body part to another after the employee has been treating for a while.
  49. The employee misses medical appointments.
  50. The employee fails to show up for an independent medical examination.
  51. The employee refuses or delays diagnostic testing.
  52. There are whiteouts, corrections or erasures on medical forms submitted by the employee.
  53. Pain is exaggerated.
  54. Invalid or inconsistent effort is reported on the functional capacity evaluation.
  55. The employee has a history of multiple workers’ comp claims or reporting subjective claims of injury.
  56. The injury relates to a preexisting medical condition or health problem.
  57. The length of recovery is excessive for the nature of the injury.
  58. The employee who has been off work for a while has calluses on hands or grime under the fingernails.
  59. The medical reports reflect “muscular,” “tanned” or other adjectives that reflect that the employee is in good health.
  60. The employee is unable to work because of the injury but is seen painting her house, mowing the lawn, carrying heavy objects, etc.
  61. The employee has a high-risk hobby or does other activities involving considerable physical exertion.
  62. Surveillance reflects physical activity greater than what is reflected in the medical reports.
  63. The employee is unusually pushy to settle the workers’ comp claim.
  64. The employee has extensive medical knowledge but no training in the medical field, or uses extensive insurance terminology but has no work experience in the insurance field.
  65. The employee is a part of a group of employees using the same doctor and the same attorney for their workers’ comp injuries.
  66. The attorney’s letter of representation is the same day of the injury or even dated before the “injury.”

Please share your experiences in the comments!

Disclaimer: The information in this article does not constitute legal advice, nor is it intended to create an attorney-client relationship. Every situation is unique, and I encourage you to seek legal advice from a licensed attorney for your particular situation.

Chasing the Right Numbers on Claims

Managing a claims operation is challenging. There are so many moving parts, dynamics and procedures. Information comes gushing in like a fire hose, making it difficult for many companies to effectively assemble and organize it. It’s crucial to help claims divisions focus on the right numbers instead of chasing numbers that have no value.

Most claims leaders know that there are a few factors that affect the majority of claim outcomes. However, many times organizations will mistakenly target metrics “for metrics’ sake,” at the expense of common sense.

Traditionally, a claims supervisor or branch manager will receive metric targets from senior leadership. Unfortunately, the intent of these goals is skewed dramatically by the time they reach front-line personnel. For example, let’s take a company that wants to improve customer service by inspecting vehicle damage the same business day. While this is a noble idea and has the potential to increase customer satisfaction, branch level managers are often forced to abandon rational thinking to meet a specific “inspection metric” or quota. Managers will chase the numbers to obtain an inspection, often having staff appraisers take photos of damaged vehicles over fences or taking shortcuts in an attempt to meet requirements. This often leads to compromised accuracy and raises the question — “Does it really make sense?” It does to the manager who needs to meet goals and protect her job but does it truly increase customer satisfaction? Not necessarily.

Having a goal at the top doesn’t mean that the numbers will retain their true meaning by the time they get to the daily staff. It’s crucial to focus on figures that actually create better claim outcomes and customer experiences.

Here’s another example of how differing goals within a claims organization can skew overall results when managers are forced to manage to the wrong numbers:

Let’s say your insured damages another vehicle and that claimant decides to go through his own carrier for repairs. Now the carrier sends in a subrogation demand that includes excessive rental, overlapping operations, duplicate invoices and mathematical errors. Would it be a good idea to just pay what is being asked without reviewing for accuracy?

Well, for some insurers that don’t have the staffing or the expertise in the subrogation department, quite often an excessive demand like this might just be rubber-stamped. The subrogation department may be overseen by an individual who has been compartmentalized away from day-to-day claims. If this manager’s goals and metrics don’t include accuracy, he may just pay this overinflated demand.

Chasing the wrong numbers can give the misperception that the manager is achieving goals, but the best possible outcome wasn’t achieved.

So what’s the answer?

The key is matching numbers to desirable outcomes that make sense. Eliminate any metrics that provide little value and only serve to create busywork. With the wealth of data that companies are able to gather and analyze, the focus should be on information that has a direct impact on customer retention and quality service.

One must carefully focus on the right numbers to add value and help push the organization forward to achieving that ideal balance of client satisfaction and operational efficiency.