Lack of access to mental health care becoming a crisis

A lack of mental health professionals is pushing the US mental health system to a crisis point. And even if a psychiatrist commits someone to a facility, there may not be a bed for them. A startling 55 percent of all US counties have zero psychiatrists, psychologists or social workers to provide mental health services. [1] Meanwhile, the Treatment Advocacy Center (TAC) has tracked a 14% drop nationwide in the number of state psychiatric beds from 2005 to 2010.

These problems threaten access to timely and effective care for those with mental health needs.

In addition, the shortage of psychiatric beds and obstacles to care have been connected to increases in homelessness and incarcerations for mentally ill citizens. Aside from building more facilities or training more mental health professionals, what can be done to combat this problem? There is data available now in current systems to improve mental health access and get these people the care they need.

What are the mental health service needs?

Simply put, analyzing the right data can tell us where, what and how good the available mental health care services are. The use of advanced analytics can help tell us:

  • Where providers/facilities/services are currently located
  • What the current capacities are, and what future demand will be, using trend analysis
  • The quality of care based on actual outcomes

This is obviously valuable information in the effort to provide services to those struggling with mental health issues, but how else can it help? Policy changes have a tremendous effect on mental health services. Also, there are many enhancements mental health programs are considering, but it can be difficult for them to understand the cost vs reward. Analytics can help mental health organizations:

  • Ameliorate provider shortages caused by policy changes,
  • Develop provider/facility expansion incentive programs
  • Support telemedicine infrastructure initiatives that target underserved areas
  • Improve campaigns encouraging people to enter the mental health profession
  • Design patient/community mental health awareness campaigns

These are all potential benefits to a mature mental health data system. So what goes into that? Data from existing Medicaid/Medicare systems, hospital inpatient/outpatient systems, current and future Health Information Exchanges and current and future All Payer Claims Databases would be essential to the success of such a project, and significantly increase our analytic capability. The good news is, many states are in the process of making this a reality. For instance, most states have Medicaid claims data readily available and could pilot an analytic needs assessment just using that data.

Just having that information in hand could help the mentally ill gain access to quality care, and keep this crisis from growing. What is your state doing? Please share in the comments section.


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The enemy within: When trusted access turns into insider threat

Not all fraud originates from the dark underground of criminal enterprises. In fact, fraud could literally be right under our noses, or in the cubicle next to us.

The “Insider Threat” has many names – internal fraud, occupational fraud, even espionage – and uses authorized internal access and knowledge to carry out an illicit act.  While most offenders try to evade detection, insiders have an advantage where they can be privy to detection methods.  With this level of in-depth knowledge of an organization, the insider threat poses a different kind of threat than typical cyber adversaries, who rely on formalized methodologies to access, explore, and exploit a network environment.

When organizations vet prospective employees, contractors or partners to establish trustworthiness, the vast majority are genuinely honorable.  Outside of television shows such as “24” and “Homeland”, individuals rarely join an organization with the sole purpose of infiltrating a target.  You might wonder, if this is true, how can a person with trusted access suddenly “go rogue”?  Insider threats often evolve over time in response to personal challenges.  For example, financial hardships or monetary gain can motivate someone to exploit their access within their employer.  Non-obvious stressors such as personal relationships, workplace issues or ideological beliefs can also trigger an individual to misuse their trust.  The bottom line is that people change over time – their personal situations, beliefs or allegiances – resulting in risk to an organization’s critical or sensitive information.

The Federal Government has been exploring the concepts of continuous monitoring and continuous evaluation as a means to reduce incidents such as the Washington Navy Yard shooting and the Wikileaks scandal involving Army Private Manning.  For mature organizations who have had a focus on the insider threat for decades, these new concepts employ ongoing techniques to evaluate trustworthiness beyond the historically ad-hoc or periodic reviews.  A cornerstone of this approach is automated analytics.  Without automating processes and applying advanced analytics, organizations are at risk of falling behind amid massive amounts of information and overlooking a key indicator which might seem benign to the individual investigator.

Not all employees have access to highly sensitive data, so the need for such oversight is limited to those individuals who have access to an organization’s sensitive information.  Anyone dealing with personally identifiable information, such as information in tax records or social services, can put an agency at risk.  Luckily, organizations can begin to take steps to proactively safeguard their information and reputation today.

Like in cyber security, a first step toward reducing the insider threat risk is to understand where the sensitive data resides within an organization.  Further, agencies must then identify all individuals, employees, contractors, and third parties who have access to this information.  Defining the data and its access points are a critical first step in safeguarding these assets.  Next, an organization can employ a risk or fraud prevention program.  Like in other types of fraud, deterrence can reduce the possibility of a successful insider attack.   Agencies with strong prevention programs that are communicated throughout the entire organization can reduce their risk significantly.  However, the determined individual can easily evade such programs (such as FBI Agent Robert Hanssen), which is why organizations should complement their efforts with analytical detection methods to ensure a holistic, effective program.    When we consider the aftermath of Edward Snowden’s leaked national security data, whether you consider him a hero or traitor, risk prevention is far more appealing than the uncertainty of the adverse effect of one individual.  In Snowden’s case, we still haven’t even fully realized the impact of his actions.

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Is predictive analytics misguiding your fraud detection efforts?

When it comes to fraud detection and risk mitigation, predictive modeling has earned a reputation as the “heavy hitter” in the realm of data analytics.  As our celebration of International Fraud Awareness Week continues, I would challenge our readers to ask themselves this question, “Is the reliance upon predictive analytics misguiding our ability to detect fraud? “

I did not come here to bury predictive modeling, of course. It’s superior to a rules-based approach since it moves away from mere intuition to data-driven decision making.  Predictive modeling is highly effective in assessing risk since a predicted probability score helps scale risk from “somewhat likely” to “highly likely”. With that information, investigators can focus valuable resources on those events “most likely” to be truly fraudulent or non-compliant.  And as seen by various Federal and State agencies, predictive modeling has had great impact when it comes to identifying and preventing fraud.

For instance, Los Angeles County Department of Public Social Services used predictive models to combat child care benefits fraud. LA County was able to identify organized conspiracy groups much earlier, significantly reducing the duration of fraudulent activities. LA County mapped out a network of participants and providers that visually displayed their relationships. They looked at whether any given small network fit into a larger scheme of networks, in which participants are in collusion with other child care providers. They identified strong central nodes and, in one case, found a child care provider serving many nodes of participants colluding in fraudulent activities.

That is all great, but with that said, since predictive models are built on known targeted historical events (i.e., known fraud), they can only predict well when scoring new data with similar relationships in place.  As patterns of behavior change, predictive models become outdated very quickly and are not able to pick up on those new behaviors unless they are retrained with examples of what the “new behavior” looks like.

With this limitation in mind, I challenge readers to ask to what extent do we become reliant on outdated predictive models? Do we have the data to retrain those models?  If not, does it make more sense to focus more effort on finding the “unknown unknowns?”

Imagine instead the ability to identify relationships without knowing which rule to write (rules-based) or relying upon known historical representations of what is bad (predictive modeling).  These relationships would instead surface through contextual linkages and network patterns within the data.  These type of analytics (network analytics or “entity link analysis”) would provide the contextual clues for detecting fraud by revealing changes in baseline behaviors, peer groups and other monitored entities and not rely upon known historical evidence of something bad.

Social network analysis of disability claims fraud scheme (click to expand)

Social network analysis of disability claims fraud scheme (click to expand)

Outputs from the contextual linkages such as measures of centrality (for example, closeness and proximity), and changes in these measures over time, can then be used as inputs to help an existing model do a better job in detecting anomalies. They can also help a new model establish behavioral trends, and spot anomalous behaviors as compared to the behaviors of one’s peers.  In other words, instead of using known historical evidence of fraud to train models, “membership” in a network becomes the target that is being modeled and someone is scored based on the extent they are moving “away” or “closer” to a type of behavior like collusion.

So in terms of fraud awareness, to what extent have you become reliant on predictive models while fraud behavior inevitably changes over time?  And to what extent can you explore the territory of the “unknown unknowns”?  How can you further enable your predictive models to detect new trends and open up new ways to predict fraud?

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Data Mining: A Medicaid Fraud Control Unit's best weapon in the fight against health care fraud

On a cold and wet December morning in 2008, at approximately 1:30 AM, I pulled into the parking lot of an abandoned supermarket in Arlington, TX.  With sleet pelleting my windshield, I saw three additional sets of headlights enter into the parking lot from different directions.  All three cars converged on mine…lights blacked out…trunk lids popped open…and I knew it was ‘go’ time.

In each of our trunks were a collection of shotguns, assault rifles, road flares (because every cop has road flares), evidence bags, zip ties (they work better than handcuffs in rapid response situations), Kevlar and ceramic plated tactical vests, hollow point ammunition, Taser, pepper spray (3 million Scoville Heat Units – ouch!), ASP baton and handcuffs. Last, but not least, I had my .40 cal Smith & Wesson Military and Police Semi-Automatic handgun with Trijicon night sights.

As we say in Texas, we were loaded for bear!  We had everything a team of highly trained law enforcement professionals could possibly need to execute the search and arrest warrant for the…um...gentleman that we had probable cause to believe had committed health care fraud.

Yes, you read that last part correctly, health care fraud!

While health care fraud cannot compete with the Hollywood-style glamorization of guns, drugs and murder, it is not any less important of a crime to investigate, nor does it have any less dangerous perpetrators. With annual losses to Medicare fraud estimated as high as $250 billion, the United States is extremely vulnerable to groups like the Russian Mafia and Nigerian Mafia, to name a few.

Mafias…in health care fraud?  Yes, and they protect their revenue flows from health care fraud just as violently as they do from other revenue sources.

So, which of the weapons mentioned in our trunks above are best served to combat this threat to, not only our health care delivery system, but also our economy?  Is it all of those ‘cool toys’ I got to play with in my former law enforcement life?

Not even close. While guns and gadgets will always have their place in law enforcement, I would argue that data mining is the best, but least emphasized, weapon in the proverbial trunk of law enforcement professionals.

Since 1978, Medicaid Fraud Control Units (MFCUs, the largest group of state law enforcement professionals fighting health care fraud in the US), have not been allowed to use federal matching funds for the purpose of data mining.

Here is an excerpt from the Federal Register:

Federal regulations at 42 CFR 1007.19 specify that State MFCUs are prohibited from using Federal matching funds to conduct ‘‘efforts to identify situations in which a question of fraud may exist, including the screening of claims, analysis of patterns of practice, or routine verification with beneficiaries of whether services billed by providers were actually received.’’

Historically, State Medicaid Agencies have performed the data mining function and then fed the MFCU leads that rose to the level of a credible allegation of fraud.  With the modernization of technology and a change in law on June 17, 2013, MFCUs are now benefiting from the ability to apply their federal share of dollars towards data mining activities.

While some MFCUs are content with being given a fish by the State Medicaid Agency, other MFCUs are wading into the Big Data waters and seeking assistance from companies like SAS so they can learn how to fish.

I imagine at some point, data mining will be as common to tomorrow’s MFCU investigators as Kevlar and blue steel were to my generation of crime fighters.

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A prescription for fraud: The hidden connection between drug abuse and fraud

All of us are familiar with common fraud types. Insurance fraud, credit card fraud, identity theft, and tax evasion are among the most recognized areas.  But, there are many other fraud types that have a big impact on our daily lives, yet receive little attention in the media and among fraud fighters.

Prescription drug fraud is one such area.  Don’t know much about it?  The extent of the problem will shock you.

Prescription drug abuse has reached epidemic proportions in the United States.  In 2012, over 41,500 people died from drug overdoses.  60% of these deaths are specifically because of prescription drugs, not just illicit drugs.

Let’s put those figures into perspective.  5 people die every hour in the US because of drug overdoses.  Think traffic accidents kill more people each year?  You’re wrong.  What about gun violence?  Wrong again.  Drug overdoses are now the leading cause of accidental death in the US.

Fraud fighters are a skeptical bunch.  “So," you might ask, “What in the world does this have to do with fraud?”

As it turns out, there’s a strong connection.

The federal government has put tight restrictions on the ability to get certain types of prescription drugs – especially those that have high potential for addiction or abuse.  This is good public policy.  But, those restrictions have created a black market for drugs such as oxycodone, morphine and valium.

If the history of economics has taught us anything, it's that a black market creates fertile ground for fraud.

Doctor shopping is a great example of the fraudulent behavior that is unintentionally created by restricting access to prescription drugs.  A “doctor shopper” is a patient who sees multiple physicians in a short period of time, all in an attempt to get prescriptions for a pain killer (or other controlled substance).  The patient fills the scripts, then uses them to feed a drug addiction or sells them on the street.  How is this fraud?  The patient is lying about his or her condition to get the prescriptions.  Likely, the patient is also using health insurance to pay for both the doctor’s visit and the drugs.  It’s a classic case of fraud. 

Just ask Jennifer and Bryce Charpentier.  The San Diego couple was arrested earlier this year, based in part on evidence of doctor shopping.  Between the two, they obtained 150 prescriptions for drugs like hydrocodone from 13 doctors while using at least 17 different pharmacies – all in a two year period.  The ironic part of the story is that both Jennifer and Bryce are police officers!

Some fraud schemes have become highly sophisticated.  One example is a drug diversion scheme,  in which a group of doctors, patients, and pharmacists collude to write and fill prescriptions for drugs that have significant “street value.”  This network will sell the drugs on the black market, then split the ill-gotten gains.

Some folks may dismiss prescription drug fraud as just another form of healthcare fraud.  That’s a big mistake.

The costs of prescription drug fraud go well beyond the losses by private insurers and government healthcare programs.

The best estimate?  $55.7 billion.  Each and every year.  That estimate is the total costs from lost productivity, treatment for addiction, and criminal justice efforts -- and is in addition to the losses experienced by insurers and government.

Oh… and remember those stats on the number of deaths from prescription drugs each year? Add it all up, and you’ve got one heck of a problem… mixing healthcare fraud with public health, medicine with law enforcement.

The great news in all of this is that government is quietly tackling this pressing public issue.

State governments have created Prescription Drug Monitoring Programs (PDMPs).  These programs require that pharmacies report prescriptions filled for controlled substances on a daily or weekly basis.  Any pharmacy operating in the state (including mail order pharmacies) must submit data on the prescription, including prescriber information and patient information.

States place this prescription data into a consolidated database.  Some have gone so far as to make it mandatory for doctors to consult the database before writing a prescription for a controlled substance.

The most innovative states – like New York and California – are beginning to harness the power of their PDMP databases quite effectively.  For instance, those states are designing programs that proactively identify doctors who have unusual prescribing patterns, or pharmacies that dispense suspicious volumes of controlled substances.  Importantly, they are using analytics to pinpoint such behaviors, while allowing the vast majority of “good” doctors and pharmacists to continue to care for patients without interference.

With continued investment, PDMP’s will be a potent weapon in fighting this often-overlooked area of fraud.  And, if states start to link with law enforcement data on illicit drugs like heroin, we’ll see an even bigger impact on drug abuse and fraud.

Isn’t it great to see a government program that has such a positive impact on the lives of its citizens?

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The Many Faces of Unemployment Fraud

Is fraud like a snowflake, and every one is unique? Not really.  There is, however, an increasing number of methods and schemes that show up and expand the range of issues we need to look for every day.  To celebrate International Fraud Awareness Week this year, I'm going to spend some time diving deeper into Unemployment Insurance fraud and abuse.  With federal estimates that improper payments in unemployment sit at 9.3%, or $6.2 billion annually, and tax evasion likely equaling that, this is a problem worth our attention.


Old School

Working while drawing - A worker is laid off, and legitimately earning unemployment for a period of time, but has returned to work and failed to report that and cease drawing benefits.  New Hire Registers at the state and federal level are one of the easiest tools to help prevent this, as evidenced by New York State's recent press release showing they saved $409 million just from matching claims versus New Hire.

Faking job search - There are requirements to keep receiving unemployment benefits, ranging from being "available for work" (aka, not on vacation in Hawaii!) to job search requirements.  In this scheme, the claimant is lying or faking their compliance with one or more of those requirements.

Collusion with employer - The employee wasn't really laid off at all, or goes back to work at the same employer under the table.  Typically, this involves splitting the value of the benefits, or a lowering of the wages paid while the unemployment is also being collected.

New School

Identity theft schemes - These are spreading as a method of using stolen identities, often because it takes much longer for the victim to become aware, as it won't appear on a credit report.  There are many different variations, and some of the more interesting ones have involved theft of identities of prisoners to file claims, or the case that showed up in Florida where many identities stolen were those of other state employees.

Fake businesses - This is an intriguing approach as it actually involves paying taxes that aren't owed.  Businesses are formed solely for the purpose of filing fake reports of employment and wages paid for workers that will subsequently be "laid off" and file for unemployment.  The benefits gained will exceed the taxes paid, and the business will go under.


Many people fail to think about the other side of unemployment fraud.  Businesses owe unemployment tax for their workers, and are motivated at times to undertake schemes of tax evasion or fraud.

Old School

Cash payment under the table- This truly is the oldest scheme in this particular book.  Workers are simply paid off the books, and none of the employment is reported.  The variations range from businesses that are avoiding virtually all laws and licensing requirements, to ones that are attempting to appear legitimate, and have registered.  Some of the latter also report a portion of their employees to further the image of legitimacy.

Shaving wages - Similar to the scheme of keeping workers off the books, in this approach, the workers are all reported but their wage rates are reported at a lower rate than what employees were actually paid.  At times, this is used in conjunction with keeping a portion of the workforce off the books completely.

New School

SUTA-dumping - Businesses that have experienced tax rate increases based on layoffs are incentivized to form "new" business entities to take over operations with a lower tax rate, which is against the law if they fail to declare it.  SUTA (State Unemployment Tax) dumping involves that illegal transfer of employees and tax reporting between two or more entities to lower the tax rate.

Independent contractors - This issue has been around for many years, but it wasn't very prevalent when I was doing tax compliance activities 20 years back or more.  Now, all industries are seeing an increase in the misuse of independent contractor designations for individuals that are nothing more than an hourly or piece-work employee.  While especially prevalent in industries like construction, landscaping, janitorial and delivery, I've seen surprising cases where restaurants claimed their waiters and waitresses are independent.

Manipulation of employee leasing/PEOs - Professional Employee Organizations (PEOs) and employee leasing companies provide ways to have a workforce level that can easily ramp up or down, and offload administrative elements of employment, from hiring and HR to payroll and tax withholding.  However, this relationship has been manipulated to avoid the costs of layoffs and avoid increases in tax rates for organizations that lay off employees.


This is just the tip of the iceberg in terms of the schemes and issues, which are multiplying annually.  Proper use of data-sharing and analytics can significantly help the issues on both the claims and tax sides of the house.  Cuts in staffing have hit unemployment agencies hard, affecting program integrity efforts.  Ensuring that they are using data from New Hire registers, incarceration data, and matching with other programs ranging from workers' compensation to income and sales tax can dramatically impact results.  Shifting from approaches that rely almost exclusively on complex rules and utilizing analytics, ranging from predictive models on past cases to peer comparisons for anomalies, can reduce false positives and increase detection of complex schemes.

May you enjoy your International Fraud Awareness Week 2014, and help spread the word.  Check out what the SAS Security Intelligence team has planned for the rest of the week. Next time, I'm going to take a look at cyber threats and what can be done to protect businesses and government.  In the meantime, follow me on Twitter @CarlHammersburg


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It’s beginning to look a lot like International Fraud Awareness Week

For most people, this time of year means celebrating cherished, personal traditions… helping those less fortunate…flocking to stores in droves…the company holiday party…

For the SAS Security Intelligence team, it means identity theft…benefits fraud…unemployment insurance fraud...insider threats. Why? Because next week is International Fraud Awareness Week! And we’re celebrating by going on a blogging binge.

Everyday next week, Monday to Friday, a member of the team will write about a particular fraud topic. Take a look at the line-up below.

Carl Hammersburg is like the kid that can’t wait to open his presents so he peaks under wrapping paper and whines until he gets one present early. We let him have two. He posted two fraud blogs already this week! Check out his round-up of fraud news and his take on the Medicaid fraud epidemic.

Enjoy those then come back on Monday for more great fraud content. You can also follow the conversation all week on Twitter through #FraudWeek.

Monday:              Still chomping at the bit, Carl will tell us about schemes in unemployment insurance claims and tax evasion.

Tuesday:            Shaun Barry writes about the hidden connection between prescription drug abuse and fraud.

Wednesday:     Ricky Sluder will discuss data mining as a valuable weapon in a Medicaid Fraud Control Unit’s fight against health care fraud.

Thursday:         John Stultz questions whether the popular demand for “predictive analytics” misguides our efforts to detect fraud.

Friday:                 Jen Dunham will tell us about “The enemy within: When trusted access turns into insider threat”

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The Medicaid Fraud Epidemic - Can We Still Save the Patient?

There are times when I harken back to the classic television show M.A.S.H.  For those of you too young to remember, the story centered around a mobile Army surgical hospital in the midst of the conflict in the Korean peninsula.  While they weren't the first people to see the patient, the unit served as the front line for stabilization and surgery.  Often, conflict flooded them with patients, and triage - quickly determining who could be saved and who couldn't be, along with the order of treatment, became critical.

We're facing the same triage decision with the Medicaid and Medicare programs here in the United States.  Collectively, Medicaid and Medicare fraud, waste and improper payments are epidemic. While a Health and Human Services study on improper payments last year reflects a drop to 5.8% improper or fraudulent Medicaid payments for a total of $14.4 billion, there are still many cases hitting the news where a single fraudulent network is responsible for $100-200 million or more in fraud.  Meanwhile, Medicaid expansion is moving ahead in most states, and while the feds are pushing for better payment integrity, and providing grant funding, so much of the underlying staffing and structure is weak. The task is huge for most states.

Let's start with some good news.  Some states are using the changes afoot to ensure a focus on payment accuracy and fraud prevention.  Kentucky, for example, used the implementation of a state health exchange to layer on analytics as a proactive defense across multiple benefits programs, including Medicaid, SNAP (food stamps) and TANF (welfare to work).  At least six states have put out a public request for information (RFI) to gather data from vendors on payment integrity and fraud prevention solutions in the last eight months.  An RFI is often a precursor to acquiring funding or submitting a grant application to engage in a project.  Multiple states are  modernizing core billing and payment processing systems and ancillary functions to improve payment integrity, and they are looking at solutions that can intervene pre-payment. This would change the traditional "pay and chase" game that comes up in normal audit and fraud processes.  In addition, many states are updating and upgrading core claims processing systems for the first time in years, and every one of those has included analytics for analysis of cost drivers, outliers and fraud.

Now let's look at the flip side - even the absolute basics aren't working at times.  For example, there is a Medicaid Interstate Match program, which simply matches recipients across multiple states to ensure they aren't receiving Medicaid benefits in multiple locations.  The feds set this up, and they also mandated participation, yet 14 states didn't submit any data last year, according to the Office of the Inspector General (OIG) of Health and Human Services.  Even worse, a match between Medicaid and Medicare, known as Medi-Medi, had only 19 states participate, according to the same OIG report.

While I started off saying this is a case of triage, at the end, we realize that this patient needs to be saved.  They may need changes, but bringing systems up to date, implementing modern analytics and staffing appropriately to handle caseloads can stem the bleeding and get them back in action.

What are your thoughts? Positive trends that you are seeing in these programs? Please give your feedback, and in the meantime, follow me on Twitter @CarlHammersburg

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Fraud is in the Air, Everywhere I Look Around

It's time for a fall fraud roundup. Bad deeds swirl around like so many dry leaves, and I'd like to highlight a few of them this week.

  • It can happen anywhere, even in sports, and no, I'm not picking on those shoplifting Dallas Cowboys here. An MLS referee was suspended for workers' compensation fraud for taking $14,000 from the New York State Workers Compensation Board while, you know, working.  That just happens to be one of the no-no's within a workers' comp system.  I think being televised regularly might make this case a bit of a slam dunk.
  • However, we're still much better off than workers' compensation in Australia, where $273 million was paid out in Victoria State alone last year for mental/stress claims.  As if the fraud wasn't hard enough to root out already, I can just imagine every bad employee claiming they are "stressed" because the boss is actually on them to work.  There are serious issues with discrimination, bullying, racism and sexism within workplaces, and laws to prevent them as well as opportunities to sue for them.  The workers' comp systems were never built to contemplate those kinds of cases or caseloads.  With the stress injuries being filed by government workers at 5 times the rate of the private sector, and claims costing nearly $90,000 each (higher than physical injuries!), this is guaranteed to be a Pandora's box.

...In a first, I'm interrupting my own blog with breaking news.  In a "you can't make this stuff up" moment, I was just interrupted while writing this entry by a fake IRS collection call.  The automated voice, supposedly from agent "Linda" wanted me to know I owe thousands of dollars, and they are about ready to seize my assets, levy my wages and such unless I call right back.  While I appreciate the D.C. area code...nice touch...don't they know what I do for a living?  Analytics could have let them know mine wasn't the number to call.... Back to our regularly scheduled fraud roundup.

Employee misclassification and the underground economy

The issue of employee misclassification and the underground economy has been gaining traction again this year.  This involves businesses that falsely claim their employees are independent contractors, or pay some or all of them off the books to avoid compliance with the law.  Not just one or two here, but unemployment, social security, federal income tax withholding, and workers' compensation just for a start.  Often thrown in are violations of overtime or minimum wage laws, and failing to follow most or all safety laws.  At times, this is the desired state by the employee, who is also trying to defraud the government by not paying taxes, or, often avoiding child support or similar obligations.  Other times, they are the victims, unable or unwilling to speak up for fear of the loss of their job, or other retribution if they are an undocumented immigrant.

  • The Little Hoover Commission in California has conducted a series of hearings on the underground economy this year, with coverage ranging from the employee misclassification I mentioned to counterfeit goods and theft of intellectual property.  Not surprisingly, with the movie industry largely based in California, and Silicon Valley being the primary technology hub in the United States, issues that impact their bottom line will be front and center.  I had the opportunity to present written and oral testimony to the commission earlier this year, and they recently followed up to gather additional information.  No timeline on the final report and recommendations that might impact legislation, but stay tuned. I will let you know what comes out of it.
  • A great series from McClatchy and ProPublica really dove into the issue of employee misclassification recently as well.  Focused on the construction industry, which I know from my time fighting this issue is one of the worst, it is appropriately titled "Contract to Cheat" .  It just won the October Sidney award from the Sidney Hillman Foundation for the excellent journalism.  There are a series of related articles focusing on 7 specific states - California, Florida, Illinois, Missouri, New York, North Carolina, South Carolina and Texas, where in depth studies, boots on the ground investigative journalism and interviews helped dive deeper into certain aspects.

On the website that pulls all of this together, McClatchy has even included an interactive section with the commonly used questions utilized by government agencies to determine if someone is an employee, or potentially misclassified.  When I worked for the government in Washington State, we worked hard to put similar materials in the hands of the public, and I applaud them for their work.  One of the key reporters in this endeavor, Mandy Locke, who writes for the News & Observer in Raleigh, North Carolina, is someone I've had the opportunity to work with a couple of times previously for stories that became part of the background for this effort.  Her excellence appears to be shared amongst the other contributors.

Utilizing a combination of public data and interviews, the series attempted to quantify misclassification across 12 sub-categories of the construction industry for multiple states.  The numbers don't surprise me.  Texas topped the list at 37.7% of workers misclassified, nearly 317,000 people in total.  The home state of SAS, North Carolina, came in at 35.2%, and more than 102,000.  (I'm pleased that SAS is the opposite - even the groundskeepers on campus are employed directly by SAS!)  What does all of this add up to in terms of the tax dodge that falls on the rest of us from this fraud? About $500 million in North Carolina, and a staggering $1.2 billion in Texas!

Piecemeal analytics isn't enough

While I've seen some increased utilization of analytics to assist with this problem, it is small and piecemeal.  Unemployment insurance programs are often much more concerned around claims fraud, which is a real and significant problem, but except for cases when identity theft or false businesses are involved, is small dollars.  Workers' compensation enforcement is often staffed so minimally, they have little opportunity to enforce the law, and have few tools to do so, except in places like Washington State.  Wage enforcement is often in the same situation.  However, when looking at the issue more comprehensively, data sharing and cross-agency analytics utilizing a tool like the SAS Fraud Framework cam make a huge difference.  North Carolina is taking a leadership role in that approach, and hopefully that will begin to pay off in reversing the alarming stats above.  Looking at the comprehensive losses to the state ensures that is isn't a program by program decision.

So, with all that aired out, I'll be moving on to other subjects soon.  In the meantime, follow me on Twitter @CarlHammersburg



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An analytic approach to improving mental health access, quality and costs

Part 1: The challenge and the opportunity

Mental illness continues to profoundly affect the nation’s population and, for the most part, remains greatly under analyzed.  This is the first entry in a series about the mental health problem in the US, and how an analytic approach can improve care for the mentally ill and reduce the associated costs.

In 20121, an estimated 20%, or approximately 44 million, of adults aged 18 or older were living with a mental illness in the US. This does not include substance abuse related numbers which would drive the total higher.

The numbers do not differ much for children. A recent study from the CDC indicates that somewhere between 13-20% of all children living in the US had experienced a mental disorder in the previous year.  Within this mental health population only 38% of all adults, and less than 20% of all children/adolescents, are treated for their mental illness2.

Those who go untreated for mental illness bear a cost to themselves and potentially the rest of society. They maintain a high risk for a variety of unhealthy/unsafe behaviors such as suicide, alcohol/drug abuse, violent/self-destructive behavior and increased potential to be homeless or incarcerated.

The financial impact is evident. As of 2008, an estimated $60 billion was spent on mental health compared to $35 billion in 19963.  In addition, US employers every year lose almost $22 billion dollars from decline in production due to mental illness4. That number is a direct result of the estimated 217 million work days which are lost/partially lost from mental illness5.

The social impact further affects family/friends/work/school/public services and while much has been said in the news about violent crime and the mentally ill, in actuality about 3-5% of violent crime is attributed to someone with severe mental illness. In fact, severely mentally ill people are almost 10 times more likely to be a victim of violent crime than the rest of the population6.

While successful initiatives have been launched to create awareness and address mental illness in a more comprehensive manner, much more can be done to support and affect positive change across the entire mental health spectrum.  Integrating readily available data from clinical, claim and other government/commercial/social systems can lay a foundation for deriving valuable insight and target mental health initiatives surrounding:

  • Access to Care
  • Patient Coordination & Quality of care
  • Cost Containment

Applying advanced data management, analytics and visualization technology to this data could lead to prodigious care & payment delivery enhancements in the mental health areas listed above.

Ultimately, an analytic approach can lead to better outcomes not just in mental health but also in physical health and social services as well.  I’ll delve into access to care in Part 2.

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