The promise of integrated care for mental illness prevention and treatment

The impact of mental illness on individuals, families, the health system and even the economy is broad and significant. In this, the latest post in my mental health series, I’d like to talk about what can be done to help. Prevention and early detection are just parts of an integrated approach to care. A common approach to integrated care, when it comes to mental health, is combining mental health and primary care information and services. As I mentioned in my previous post, a person’s mental health has a tremendous effect on their physical well-being, and vice versa.

Cherokee Health Systems in Tennessee has integrated mental health and primary care services for over 25 years, resulting in enhanced patient satisfaction, enhanced quality of care, reductions of inpatient admissions, reduced ER visits, and increased primary care visits[1]. Studies in acute care have yielded positive results as well. Eight psychiatric hospitals in Florida reduced preventable readmissions from 17.7% to 10% by collaborating and coordinating hospital care and post-discharge care[2].

There are other approaches to integrated care taking off around the country. Medicare/Medicaid (at both the federal and state levels), private payers and many healthcare systems have looked to advance integrated care through delivery models such as Accountable Care Organizations, Managed Care Plans and Patient Centered Medical Homes. In addition, there are many efforts to prevent and treat serious and long term mental illness, such as:

  • Medicaid’s mandated EPSDT screening for early signs of mental illness
  • Federally supported demonstration waivers (which may include provisions for mental health enhancements/demonstrations)
  • Expansion of covered mental health services through private payers
  • Affordable Care Act mandated mental health quality reporting
  • The Mental Health Parity and Addiction Equity Act
  • State/regional/community health and human service programs
  • The efforts of various non-profit organizations

However, many of these initiatives to integrate care and implement successful prevention/early treatment depend on the valuable insight which exists in clinical EMR systems, administrative claims payment systems and many other government/non-government data systems. Integrating, enriching, analyzing and visualizing this ocean of data can help improve mental and physical health care, and ultimately provide better consumer and system outcomes. Broader adoption of advanced data management, analytics and data visualization technology will provide novel and more targeted, insightful approaches to looking at data. Data exists from a variety of areas such as:

  • Medicaid/Medicare Systems
  • Commercial Claim Data (potentially All Payer Claims Databases including Medicaid/Medicare)
  • Health Information Exchanges
  • Ambulatory/Acute/Post-Acute EHR Systems
  • Child/Youth/Family/Elderly/Disabled/Social Service systems
  • Education Systems
  • Welfare Management Systems
  • Juvenile Justice/Criminal Justice Systems
  • Social platforms (Facebook/Twitter/ etc.)

Linking the above data sources can provide a single, 360 degree view of all the health and social services provided to a patient and assist in the co-ordination/integration of care efforts. In doing so, the collective analytics can be operationalized in a number of ways to support the prevention/treatment of those with diagnosed/undiagnosed mental illness. Potential areas of analytic support include:

  • Enhancing coordination of care efforts that allow service providers and all stakeholders’ rapid access to a single source of data to analyze and coordinate treatment options in a collaborative environment
  • Risk adjustment of mental illness contributing factors through the mining of historical data and assigning individual services/behaviors/treatments different levels of risk based on positive or negative outcomes. Using an integrated data approach, risk factors need not be limited to one set of data, but instead can identify risk factors which are clinical and/or socio-economic in nature
  • Assisting prevention efforts by using the above risk adjusted data to create analytic models of clinical/social services/behaviors/treatments/other contributing variables which historically yield the most desirable/undesirable outcomes. Then, target prevention campaigns to identify the factors which could possibly yield negative outcomes.
  • Enhancing prevention/quality of care by predicting statistical likelihood of future outcomes and performing what-if scenario analysis to determine how specific clinical/social services/behaviors/treatments/other causal variables may affect future outcomes
  • Improving quality of care by analyzing the effectiveness of various treatment plans in order to replicate positive outcomes
  • Determining which service providers are providing the most/least desirable outcomes and understanding the causal factors
  • Increasing understanding of variables outside of the traditional health data realm that affect mental illness
  • Visualizing all of the data (separately or unified) in a single point of view environment
  • Attributing providers to episodes of care to better measure cost and quality

With these possibilities before us, what can we do to make them a more common reality? In the next section I’ll lay out why whatever investment it takes is worth it, and might even save us money.

[1] Agency for Healthcare Research and Quality, Research Activities No. 377, “Experts call for integrating mental health into primary care” (January 2012)

[2] American Hospital Association, TrendWatch, “Bringing Behavioral Health into the Care Continuum: Opportunities to Improve Quality, Costs and Outcomes” (January 2012)


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Early identification and treatment of mental illness is critical

Last week I discussed factors that threaten access to mental health care. However, better access to care doesn’t always mean better quality of care. Overworked mental health professionals and overcrowded ERs are forced to expend efforts and limited resources where they have the most impact.  This forces compromises in care. We must investigate ways to enhance coordination and quality of care for the mentally ill.

Prevention/Early Treatment in Children/Young Adults

While severe and persistent mental illness has tremendous effects across all segments of the population, it is within the child/adolescent group where clinical advancements in prevention/early treatment have the potential to generate significant long term benefits. According to the National Alliance on Mental Illness (NAMI):

  • 50% of all chronic mental illness begins by age 14 and 75% by age 24.
  • 70% of those who are in the juvenile justice system suffer from mental illness.
  • More than 50% of students aged 14 and older who have a mental illness, and are involved in special education, drop out of school - the highest dropout rate of any disability group.

In addition, the National Institute of Mental Health reports that suicide of those aged 15-24 represents the third leading cause of death for that age group.

In addition, many of those with severe mental illness often suffer from chronic physical ailments which are exacerbated by the presence of untreated mental illness. This can lead to higher long term treatment costs and the possibility of conditions becoming resistant to treatment. These combined factors contribute to a shocking result.

People with a serious and persistent mental illness have a life expectancy 25 years shorter than the non-mentally ill population.[1]

It’s important to get in front of the problem as early as possible. According to, studies show that positively supporting the social and emotional well-being of children & adolescents also leads to improved education outcomes, reduced rates of crime and teen pregnancy[2], increased productivity and quality of family life, and even more robust economies.

Prevention/Early Treatment in Adults

Early detection and treatment within the adult mental health population also is key to enhancing quality of care for that population. The consequences of untreated adults parallel those of children and adolescents, but the risk also extends beyond the effects on the individual and their family and friends.

These populations are in many cases now parents, leaders, educators and members of the workforce that, when left untreated, can impact their social and professional environments. Untreated adult mental illness negatively affects the productivity inside the work place. Untreated adults who are also suffering from co-occurring physical conditions such as diabetes can generate unnecessary hospital admissions and health care costs due to their inability to effectively manage their physical conditions. With such a high number of adults with an undiagnosed mental illness, it is important to identify and understand the risk factors as early as possible, implement evidence-based treatment plans, and target public outreach/education campaigns for this population. Evidenced-based treatment has proven successful in helping mentally ill patients function better in their daily lives and relationships with people.

Further complicating matters, studies suggest that mentally ill people are going long periods of time without seeking treatment. NAMI indicates that the median time between onset of mental health symptoms and treatment is nearly 10 years. This kind of gap creates challenges to successful treatment and raises the possibility of other physical/sociological problems, as previously mentioned. Mentally ill adults often find themselves  in the direst of situations. An estimated 1.1 million attempted suicide[3], approximately 46% of the homeless population live with mental illness and/or substance abuse issues, and 20-21% of state/local prisoners have a history of mental illness[4].

Even in cases where mental illness is diagnosed, the use of data and associated analytic tools can prove to be extremely valuable in sustaining effective treatment plans over time. In my next post, I’ll discuss how integrating data from a variety of systems, and applying analytics, can help with prevention, detection and treatment of mental illness. If you missed the previous two entries in this series, please check out my posts on an analytic approach to improving mental health, and access to care.

[1] National Association of State Mental Health Program Directors. (October 2006). Morbidity and Mortality in People with Serious Mental Illness.

[2] Vigod SN, Dennis CL, Kurdyak PA, Cairney J, Guttmann A, Taylor VH. Fertility rate trends among adolescent girls with major mental illness: a Population-based study. Pediatrics. 2014 Mar;133(3):e585-91

[3] National Survey on Drug Use and Health (2009) Suicidal Thoughts and Behaviors Among Adults.

[4] National Alliance on Mental Illness (2013) Mental Illness facts and numbers.

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Study reveals little impact of student teachers on value-added measures (with exceptions)

As teachers across 35+ states are evaluated, and sometimes compensated, in part by the academic growth of their students, there may be an unintended consequence. Teachers may question whether to accept student teachers, in fear of the student teacher bringing down their value-added estimate(s) and overall evaluation rating.

How can we grow our teaching pool if we are not opening our classrooms to these future teachers? At the same time, how can we expect our teachers to risk lower ratings during this training?

When I was in the classroom, I loved working with future teachers. From providing time for field observations to semester long student teaching, I wanted my classroom to be an open door for those joining the profession. I have noticed a decline in those who open their classroom doors and experiences to teacher preparation candidates, and I understand the concerns. I asked a fellow teacher why she wasn’t opening her classroom doors. Her answer? “It is too high stakes now.”

A recent pilot study commissioned by the Tennessee Higher Education Commission, Tennessee Department of Education and 10 Tennessee school systems analyzed what, if any, impact student teachers had on a teacher’s effectiveness.

Looking across 10 districts, the study compared teacher value-added reports for teachers who did have a student teacher in the classroom to their reports when these same teachers did not have a student-teacher. The data were restricted to teachers who supervised a student teacher in at least one of the three academic school years ending in the spring of 2009, 2010 or 2011 but who also did not supervise a student teacher in at least one other of those years. The analysis compared adjacent years when possible.

This pilot study had two key preliminary findings:

  • For most teachers, there was not a statistically significant difference between the two settings in the licensed teacher’s value-added report. Specifically, student teachers have very little impact on the value-added measure of licensed teachers when they are average or high-performing teachers.
  • However, teachers with a history of low performance had lower teacher value-added measures when supervising student teachers, particularly in Mathematics and Science.

While further analysis is required to draw more definite conclusions, these preliminary results are informative and merit additional exploration. Victoria Harpool, First to the Top Coordinator at the Tennessee Higher Education Commission, notes, “Serving as a mentor to future teachers ensures our students have the most effective beginning teachers possible and provides the mentor the opportunity to reflect on their own practice as they continue to refine skills.” This research can begin to serve as a policy guide for student teacher placement.

As the profession struggles with recruitment, it is my hope that this information will encourage you to continue your work with the future teachers. Remember your cooperating teacher? Make him or her proud that you continue the cycle of growing great teachers.


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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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