Imprisoned for tax fraud, the Phantom Preparer tells his story

Tax fraud…5-7 years in prison. I thought I had it all figured out. The government wades through millions of returns and tries to issue refunds quickly. Sure, I filed over a thousand for my clients, but that’s just a blip on their radar. I never thought I’d get caught. But I did, and as the evidence was presented at my trial it became clear that I underestimated the Department of Revenue, and their technology.

Since I’m already enjoying the hospitality of the state prison system, I may as well tell you my story. I’m the Phantom Preparer. I hatched what I thought was a low risk tax fraud scheme that netted me over a hundred thousand dollars. After two months bunking with a cell mate who bathes infrequently and talks to spiders, I can tell you…it wasn’t worth it.

It's a simple scheme. I would claim fake credits or large (overstated) deductions for my clients to get them huge refunds. I would take a cut, and everyone was happy. Maybe they knew something was fishy, maybe not. They sure didn’t complain about the windfall. And as word got around that I could score big refund checks, my clientele grew.

The problem is, more clients equals more data. And that’s like candy to sophisticated fraud analytics. As exhibit after exhibit was trotted out by the prosecutor, I got pretty sick of seeing that SAS logo pop up. SAS Analytics uncovered patterns that led investigators to my door.

Upon further review, that does look a little suspicious.

Upon further review, that does look a little suspicious.

I submitted more than 1,400 returns, all online, over three years. I never filled out the Preparer Tax Identification Number field, thus my secret identity as the Phantom Preparer. I figured with all the returns flooding in, who’s gonna notice? Right… Unfortunately, all those empty fields create a pattern.

You know what else is a red flag for DoR investigators? When someone goes from years of claiming little to no deductions to claiming more than $45,000.  More than 90% of my filed returns claimed refunds. In retrospect, I may have gotten a bit greedy. $10,000 in interest expenses? Sure! $12,000 in job-related expenses? OK!

I didn’t think they’d compare past years to the current claims. It just seemed like too much information to wade through. Not so much, I guess.

Also, all those returns were filed online from the same IP address. Mine. Whoops.

Not only that, at my trial they showed something called “geoboxing”. Basically, it was my house on an aerial map…surrounded by a cluster of the addresses on the fraudulent claims, and wouldn’t you know it? I was at the intersection of Tax Fraud Lane and Up The River Court. Lots of my neighbors were clients.

You can see my house from here!

You can see my house from here!

Unfortunately, investigators could also see a dozen of my clients' houses.

Unfortunately, investigators could also see a dozen of my clients' houses.

 

 

 

 

 

 

 

 

When the prosecutor was building their case, they even went back and matched the timing of the filings to my calendar. So, say I met with Bill Smith at 1:00pm on Feb. 20. Bill Smith’s return was filed an hour later. By itself, not a problem, but when that same pattern repeats over hundreds of meetings and fraudulent filings…uh-oh.

So, the evidence was overwhelming and I copped a plea. With good behavior I may be out in four years. I guess I’m not the Phantom Preparer anymore. I’m just Prisoner #4976113.

 

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Someone's trying to steal your tax refund... better call the math geeks!

Judging by the spike in media coverage of tax fraud, one might think accountants have suddenly been inspired by Breaking Bad re-runs, and turned en masse to lives of crime.

Umm… no. But, there are two good reasons for all the attention.

Source: 401(k) 2012, https://creativecommons.org/licenses/by-sa/2.0/legalcode

Source: 401(k) 2012, https://creativecommons.org/licenses/by-sa/2.0/legalcode

One reason is because of a new law – the Foreign Account Tax Compliance Act. This law requires non-US banks to disclose financial information to the IRS for accounts held by US citizens.

Why the fuss over an arcane-sounding law? The IRS is cracking down on wealthy citizens who hide their income in off-shore accounts. And they’ve had some high-profile successes. Take the case of Credit Suisse, a Swiss bank headquartered in Zurich. They were fined $2.6 billion for helping Americans to evade US tax obligations.

That makes for good headlines. But, it doesn’t fully explain the abundance of press coverage.

The other reason for the media attention is something that impacts most Americans directly. Fraudsters are plotting and scheming to steal your tax refund.

Huh?! How can someone steal your refund, when it is deposited directly into your bank account? Here’s how the scheme works:

  • A fraudster steals information about you. They don’t need a lot of information – just a few data points like name and Social Security number. If they are good, the fraudster might find out your employer’s name, too.
  • Next, the fraudster creates a fake tax return. He makes up an income amount, maybe even adds a few deductions or credits. He also creates a fake W2 that makes it seem like you had taxes withheld from your paycheck throughout the year.
  • Magically, the fake return always ends up with a refund balance.
  • Finally – and the key to the whole scheme – the fraudster submits the return electronically, and asks for a direct deposit of the refund. He provides a bank routing number for an account he controls. In a few days, the money gets deposited. The fraudster withdraws the money and disappears.

You’ve just been scammed.

No one realizes there’s a problem, until you try to submit your valid tax return. Your return gets rejected, and you are left to fight to get your (valid) refund.

This scheme is known as Stolen Identity Refund Fraud – or SIRF. It is plaguing taxpayers and governments across the US.

Government officials have battled tax refund fraud for many years. What’s different now is that a mix of technology, globalization, and identity theft are making it much easier for the bad guys to cheat the system.

Innovative government leaders are not sitting idly as this problem grows. Several innovative states are revolutionizing the fraud-fighting tools they use to tackle this challenge.

The Department of Revenue in a southern state uses highly sophisticated analytics to catch tax cheats. Their analytical models are similar to what banks and credit card companies are using to stop financial fraud – techniques such as anomaly detection, predictive modeling, and link analysis.

When a new tax return is filed, this state looks to see if there is an unusual change in circumstances from your previous returns. Same address? Same employer? Same general financial circumstance? Your return is assigned a low risk score and processed immediately. That’s the easy part.

The hard part comes when there are differences. About 12% of Americans move to a new address in a given year. The average American stays at a given job for only 4.4 years. It’s perfectly normal for people to get promotions and raises… get married… have children… all events that can have a material impact on what your report on your tax return.

Analytics make these hard decisions easier. This state is able to figure out which changes are part of everyday, normal life… and which ones are unusual and may indicate someone is trying to steal your refund.

Suddenly stopped claiming your 3 kids as deductions? That’s odd. Reporting wages from a company that is no longer in business? Seems strange. Your new bank routing number is for a bank based in Nigeria? Or Russia? A return with these types of anomalies is assigned a high risk score. Some such returns are rejected automatically. For others, the Department of Revenue contacts the taxpayer to see if the return is valid.

Analytics are helping protect taxpayers from SIRF. And, they are helping speed up payment of valid refunds. That’s a classic case of good government at work. Every state should harness the power of math to protect and serve its citizens.

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Analytics can ease the burden on social workers & help protect kids

March is national Social Work Month and this year the National Association of Social Workers celebrates its 60th anniversary of facilitating positive social changes and improving the lives of individuals and families.

Social work is a profession that considers the needs of others every day. Individuals who dedicate themselves to a career in social work focus their time and effort on the quality of life and well-being of families, children and others in need. Social workers support society’s most vulnerable during times of crisis, poverty, abuse, mental and physical disability, and social injustice. It is a noble profession that is often overlooked and under-appreciated.

Many data sources can help improve outcomes for kids (click to enlarge)

Many data sources can help improve outcomes for kids (click to enlarge)

Case workers across our country, specifically those working with child abuse and neglect cases, often face significant challenges in protecting the children in their care. The headlines are tragic and the statistics are startling. 3 million reports of child abuse and neglect are filed in the US every year – nearly a report every 10 seconds and more than four children die every day as a result of child abuse.

Case workers are confronted with overwhelming caseloads and limited access to critical information about the children in their care. With limited resources, it becomes difficult to monitor ever-changing circumstances related to a case and to proactively identify changes in a child’s risk.   Case workers need better tools and more timely access to information to help them assess children and family situations and make decisions based on comprehensive information.

A key focus area of the SAS State and Local Government Practice this year is a commitment to improving positive outcomes for children throughout the nation. We are focusing technology efforts on minimizing negative outcomes and maximizing positive outcomes for children.

SAS technology integrates data from a variety of sources to consolidate information about a child and identify key relationships in the child’s environment. This produces an overall risk score for the child. Those data sources are monitored for changes that might affect a child’s risk score such as extended absences from school, criminal history of someone in the household, or changes in access to key social services.

The goal is to put critical insights into the hands of case workers – providing them with ready access to timely, reliable, and actionable information. It reduces the time case workers need to spend gathering data and enables more time interacting with children and their families. Organization leadership gains better insight into their case portfolio enabling better triage, assignment and resource allocation to improve the management and outcomes of their child services cases.

Social workers who protect our most vulnerable face enormous challenges every day. SAS is committed to supporting their efforts in improving the lives of these children.

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Better government data sharing reduces costs, improves services

For Parent’s Weekend this year, I needed to choose a restaurant for dinner in my son’s college town. Our extended family was attending the college football game and spending the weekend with our son. Before making my decision, I searched the internet for all the restaurants located within a reasonable distance from where we were staying.

I read reviews to see what other people thought of the quality of the food and service. I visited each website and scanned the menus to be sure there were choices that would suit the tastes of some of our “more selective” family members. And finally, I checked for reservation availability for our party.

All this information just to decide where to eat dinner!

Information is everywhere. As consumers we want as much information as possible to make all kinds of decisions.

Governing Big Data white paper

As citizens, we would like to think that our government takes the same approach – using all information available to make the best decisions for all types of government operations. Unfortunately, in many cases the operational, tactical and strategic decisions made in government are not based on all of the information that might be pertinent to the situation.

Government is no newcomer to Big Data – governments have been collecting and generating vast amounts of data for years. Unfortunately, government information systems have historically been built in silos where data from one government function is held separate from data for other government functions.

Through the years, a culture of data ownership rather than data stewardship has evolved. The perceived obligation to control and protect an organization’s data has impeded the ability and willingness to share information across the government enterprise – even when these data assets are critical to improved decisions and services to the public.

In the instances where data is being shared, it is often duplicated, transmitted, interpreted and stored multiple times – oftentimes undermining the reliability, consistency, timeliness, and security of the data. The result is redundant data and inefficient decision making.

The good news is that government is increasingly focused on evidence based decisions and using data in new and better ways. The vision and strategy for enterprise data sharing and analytics can help government ensure that data is used as a strategic asset to improve business decisions. Better government data sharing and use of data can help:

  • Improve government efficiencies by reducing fraud, waste, and abuse
  • Better manage assets, identify and forecast trends and costs of major government expenditures like healthcare and education;
  • Improve citizen services by gaining more complete information about their needs and streamlining their interactions with government;
  • Reduce costs through improved workflows, effective compliance efforts and avoidance of redundant systems and data stores.

To better understand the challenges and value of enterprise analytics, join us for Governing magazine’s Big Data Driving Big Results across the Government Enterprise webinar on April 2, 2014.

The webinar will explore the challenges and opportunities associated with improved government data sharing and enterprise analytics and will focus on:

  • Strategies to identify and unlock the benefits of information sharing within and between agencies
  • How the state of Michigan deployed Big Data to drive hundreds of thousands of dollars in cost savings and provide better service to constituents
  • Best practices to analyze and share data from case studies in North Carolina and Michigan
  • A new approach to managing data independently from IT

Governing magazine’s white paper Managing Data across the Government Enterprise: A Resource Guide for Integrating Data to Support Analytics-Based Decision-Making provides additional background information and case studies exploring the topic of enterprise analytics.

 

 

 

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Using analytics to build pathways from K-12 to careers

42-50717757Public educators have increasingly been told to produce the “workforce of the future.” States are striving for alignment between what students learn and the jobs that ultimately will be available to them. This alignment is critical for students so they have the right skills and knowledge to excel at college and/or the vocation of their choice. It is equally important for the economic vitality of our communities to minimize unemployment, turnover, and reduce training costs for businesses.

Achieving education-workforce alignment requires coordination across many disparate organizations, including K-12, Higher Ed, government agencies, business and industry, all of which have many moving parts. It requires having a factual base to ground conversations regarding: what has happened; what is happening; and what is most likely to happen next?

Many agree with this notion at a high-level, but what does it look like on the ground? An expert panel will discuss how they put these ideas into practice at the SxSWEdu conference in Austin, TX. This panel will present perspectives from state education agencies (SEAs), a school district, and a higher education system on how analytics and data visualization are used to help educators (at all levels) better support students on the path to college and career.

First, Layla Bonnot from the Council of Chief State School Officers (CCSSO) will share insights gleaned from working with SEAs across the country on CCSSO’s Education Information Management Advisory Consortium. Most states have used federal grant funding and invested local dollars in building State Longitudinal Data Systems (SLDSs) that span Pre-K through college, and even into workforce data. SLDSs are expected to answer research questions, evaluate programs, and drive policy decisions. Given the large investment in these SLDSs, the stakes have never been higher to begin using these data for education-workforce alignment. Some states have struggled, while others have made notable progress. Layla Bonnot will highlight common success factors in states leveraging data and analytics to inform these policy advancements.

Next, Lubbock Independent School District’s Associate Superintendent, Doyle Vogler, will provide a K-12 perspective. Lubbock ISD is an urban district in West Texas serving almost 30,000 students. The district has experienced sizeable demographic shifts over the last 10 years, and consequently, has had to devote additional resources to meet the diverse needs of its changing student population. Mr. Vogler will discuss how Lubbock’s educators use predictive analytics and data visualization to assess the academic preparedness of incoming students and implement targeted supports to build the college and the workplace pipelines.

Lastly, University of Louisiana System’s President, Dr. Sandra Woodley, will offer the postsecondary perspective on workplace preparation. Job growth is expected to accelerate sharply in Louisiana over the next 10 years, which is welcome news, but new jobs will require highly-trained and educated workers—many more than the public education system can currently provide. Dr. Woodley will share the strategic framework used by her team to focus their work and discuss how they built a coalition of legislators, government agencies and businesses to support the development of a ‘talent marketplace’ to arm students with better information about job prerequisites and opportunities. Her team also created an ‘analytical hub’ to provide administrators and policy makers dynamic visualizations on the flow of students, dollars and outcomes.

These three speakers will address challenges and learnings around the introduction and use of analytics to support educators. They will identify keys to successful collaboration among organizations with similar college and career readiness missions. And they will explore how analytics can be used by educators to better serve students through targeted supports. If you are able to attend the innovative SxSWEdu conference, you will not want to miss this session. If you cannot make it, I will post a recording of this session as a follow-up on this blog. Come back to check it out!

UPDATE: The recorded session is now available!

Also be sure check out our friends in SAS Curriculum Pathways' who are participating in panel discussions at SXSWedu.

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Not in my Backyard! North Carolina is Tackling Government Fraud

Many states are starting to crack down on the serious abuses of government programs, cutting down on outright fraud as well as reducing abuses and errors.  I wanted to highlight one of those, now that they've been on this path for a few years.

North Carolina, where SAS is headquartered, is taking an enterprise approach to analytics, with fraud prevention a key plank in their approach.  Towards this end, they've formed a Government Data Analytics Center, known as GDAC.  GDAC offers a centralized repository that has pulled in data from many different sources in order to help analytics and fraud prevention across many programs and agencies.  Included data ranges from incorporation data from the Secretary of State to driver and vehicle licensing data from the DMV, workers' compensation coverage to unemployment claims and tax filings.  Use of data is restricted to appropriate programs based on the law and data sharing controls.

So what is this approach doing for the citizens of North Carolina?  It's helping to target those businesses that are taking advantage of systems by filing false claims, or avoiding business obligations, undercutting legitimate competitors.  While all the results aren't in yet, this approach, built on the underpinnings of the SAS Fraud Framework, has already started to pay off and gain some positive media attention for the state.

In recent years, the issues of employee misclassification and the underground economy have resulted in law changes, task forces and media attention in North Carolina.  It's also a subject I've covered in recent posts.  The North Carolina Industrial Commission (NCIC) has responded, using GDAC to target businesses that illegally cancel their workers' compensation policies, while continuing to employ workers.  Their system is known as Noncompliant Employer Tracking System, or NETS.  Starting with initial sweeps in counties to target employers, they jumped out to a good start last summer, as covered here, and continue to expand efforts and results.

One of NCIC's partners in these efforts is the Department of Commerce's Division of Employment Security (DES).  DES oversees the unemployment insurance program for North Carolina, and is actively tackling both claims and tax fraud.  An interesting new twist to unemployment fraud that involves both of those areas is fake businesses set up solely to drive false unemployment claims through, which I addressed recently in a post that touched on schemes of unemployment fraud.

DES has been using GDAC and focused investigations to take down 105 fake employers statewide, with 672 fake unemployment claims associated.  Some of the vacant businesses were using vacant lots as their address, and one even used the local television station!  Far be it from me to say, but that may not be the best way to stay under the radar.

This operation is paying off big.  Savings from prevented payments alone hit $5.2 million.  An additional $2.5 million in fraudulent payments is targeted for recovery.  Beyond that, I expect a series of criminal charges to follow.

This approach works, and preventing payments from going out the door is the best way to stop fraud against government and the citizens and business that pay taxes.  Kudos to North Carolina, and kudos to everyone working on GDAC and at the agencies using it to improve results for citizens.  They are speaking up loud and clear and saying "Not in my backyard!".

Want to join the conversation? Add a comment.  Care to follow other fraud news? Follow me on Twitter @carlhammersburg

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Analytics making a difference in tax fraud: Kentucky protects taxpayer money, uncovers fraud schemes

According to a 2012 report, it was estimated that over the next five years the US Internal Revenue Service (IRS) will issue more than $20 billion in potentially fraudulent tax refunds. Figures like this do little to boost taxpayers’ confidence in our nation’s tax system.

And tax fraud is not just an IRS issue. States coast to coast are facing similar circumstances. Plagued with identity theft, shrinking budgets and heightened pressure to pay returns quickly, states are having a difficult time cracking down on tax fraud offenders.

Kentucky is stepping up efforts to improve detection of individual income tax compliance issues and save taxpayers’ money, with help from SAS.

“Our job is not just to collect taxes, but to get taxpayers the money they are owed as quickly as possible. The increasing amount of fraud and abuse puts an even greater responsibility on us to be diligent in our efforts. Analytics helps with that,” said Mack Gillim, Executive Director of the Office of Processing and Enforcement for the Kentucky Department of Revenue.

Tax returns that come into the Commonwealth are scored on a nightly basis and any potential issues are flagged for examiners to review the following day. This quick turnaround process is key to the Commonwealth team as they try to get refund checks out to their taxpayers within 14 days.

The department is committed to serving citizens promptly, but that expedience makes it even more critical to be able to quickly spot suspicious returns. Diligent examiners come in first thing in the morning, assess the flagged returns and immediately start investigations.

Using SAS hosted fraud-fighting technology, the Commonwealth’s Department of Revenue has uncovered several fraud schemes. It’s a constant battle for government agencies to keep up with the evolving schemes of dedicated fraudsters. I can’t share all the red flags and anomalies KY DoR looks for as it could help fraudsters adjust their schemes. I can, however, tell you that the metrics have been invaluable in identifying trends that otherwise may have gone unnoticed for a much longer period of time.

As responsible stewards of taxpayer money, the Kentucky Department of Revenue’s use of analytics is making a difference in their vigilant stand against fraud.

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Wanna solve the US budget deficit? Fix fraud!

Here’s a great way to kill a conversation at the next cocktail party you attend.  Start talking about the US budget deficit.

You remember the deficit, right? It’s the difference between what the government collects and what it spends. In 2014, the US federal government spent $488 billion more than what it collected in revenues. In the last three months alone, we have racked up a deficit of $175 billion.

Nobody seems to want to talk about the deficit. It’s boring. It’s old news. And that’s not just the reaction from people you meet at cocktail parties. President Obama has yet to propose a balanced budget. Congress is no better. Senator Mitch McConnell has recently talked about “belt-tightening”, but he has refused to commit to achieve a balanced budget over the next 10 years.

Why should you care? A few weeks ago, the cumulative deficit – also known as the national debt – hit $18 trillion. That means every American owes about $56,000 to pay off our collective debt.  Yep, that’s right. You are on the hook for $56,000. So is your mother… and your two year old niece… and don’t forget about your great aunt Matilda.

“OK, I get it,” you say. “But what can we do to fix the problem? The political process in Washington is so broken that no one can agree on what to do!”

Here’s a simple solution for eliminating the deficit – stop fraud in federal programs.

Wait. That’s it? Fix fraud, and we fix one of the biggest political challenges of our era? You’ve got to be kidding!

Nope. A closer look at the numbers reveals a solution that is hidden in plain sight.

Let’s start with the revenue side of the equation. By its own estimates, the IRS calculates that it fails to collect $385 billion each year in tax revenues. This “tax gap” comes from taxpayers who fail to file a return, report less income than earned, and exaggerate deductions and credits. There is even a small group of true delinquents – taxpayers who are unable or unwilling to pay what they owe.

Surprisingly, these estimates do not include a very significant source of revenue leakage – the underground economy. Tips. Cash payments. Paying employees “off the books”. Bartering. The IRS has no reliable way to estimate the size of the black market. So, they find it impossible to get a handle on how much tax revenue is lost each year.

On the expenditure side, the Office of Management and Budget tracks the estimated fraud rate for major federal programs. Their most recent estimate is that federal programs make $99.7 billion in payouts that they should not have. These improper payments come from Medicare, Medicaid, Social Security, and various social benefit programs.

Let’s add up all the numbers to get a complete picture. We’ve got a deficit of $488 billion. Fixing the $385 billion tax gap lowers that number to $103 billion. Stopping the $99.7 billion in improper payments lowers it even further to $3.3 billion. So we’re getting tantalizingly close, but a small deficit remains.

Here’s where the underground economy comes into play. The US economy generates about $17.5 trillion in economic activity every year. Let’s assume an underground economy of 1% of that total – or $175 billion (a very conservative estimate, by the way). Let’s also assume an effective tax rate of 15% on that $175 billion. The result is $26 billion in additional tax revenues, or enough to give us a slight surplus.

And that, my friends, is how fixing fraud can solve one of the greatest political challenges of our generation.

Now let’s get to work convincing government leaders to take decisive action in pursuing fraud and improper payments.

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The Underground Economy - Part 2 - A Growing Problem

In Part 1 of this blog series, I touched on the problems created by the underground economy, and framed the size and scope. But important questions remain about the types of businesses that are part of the underground economy, how they do it, and what the driving forces behind this wave of fraud are.

Growing Acceptance

Studies from various sources show that some amount of tax evasion and fraud to be growing in acceptance amongst the general population. At an example, 1 in 5 surveyed in the U.S. now find some form of insurance fraud acceptable. With that trend, it becomes much easier for a business owner to rationalize their actions in the underground economy as part of “the new norm” and completely acceptable, even if not lawful.

Where’s the Problem

While the exact nature of the underground economy ebbs and flows, and will vary from location to location, there are a number of industries that have already shown to be rather susceptible. They tend to fall into a number of categories:

Other industries vary by geography and presence of natural resources. Agriculture is extremely prone to under-reporting and misclassification, and logging and related work is a significant issue in some provinces in Canada and states in the U.S.

Technology and Mobile Economy

There are a wide range of forces driving the move towards the twin issues of the underground economy and employee misclassification. Amongst those are the growth of the mobile economy and technology. As transactions with businesses increasingly shift from actual physical locations and land line phones to mobile, Internet and e-mail, there is less ability for transactions to be tracked and it is more difficult to identify the true size and nature of a business and its employment.

Methods such as using “zappers”, which erase a portion of completed transactions from sales logs, allow them to keep false books and records that more easily appear legitimate in an audit.

While decades ago, many workers could expect to stay with the same company for most or all of their lives, the concept of “employment”, especially for Millennials has increasingly become mobile and in rapid flux. It makes it very easy to designate an employee illegally as an “independent contractor", exempt from many tax payments and coverage for unemployment and workers’ comp.

Disruptive and Informal Economy

Craigslist has long been a great facilitator of the underground economy. While eBay makes some efforts to police things, the line between an individual selling off some of their used clothes and a full business is a tough one for them to address. The uproar is growing louder in many countries around websites and apps like Uber, Airbnb, VRBO and many others, as they represent easy person-to-person and business-to-business transactions that typically avoid much government scrutiny. At times, the hammer is falling hard, driving them out of business in some localities or countries altogether.

In Part 3 of this series, I will discuss some of the best ways to address the growing underground economy. In the meantime, please add your comments, or follow me and join the conversation on Twitter @CarlHammersburg.

 

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How analytics could defray the immense financial impact of mental illness

The physical and social costs of untreated mental illness are significant and have been discussed in detail in previous posts. Now let’s talk about the immense financial costs, then I’ll wrap up the series with a conclusion. The financial costs cover a broad cross-section of society, including government services and the private sector. Here are some startling numbers from the American Hospital Association:

  • Mental health care services in 2008 were almost $60 billion
  • Each year US business concedes almost $22 billion in lost/partially lost work days
  • Canadian research looking at acute care hospital readmissions within a year after discharge found those with mental illness were readmitted 37% and those without mental illness 27%
  • Monthly healthcare costs for patients with chronic medical conditions and depression are 65% higher than patients with chronic medical conditions and no depression

And the National Alliance on Mental Illness states:

  • 6 million Emergency Room in visits in 2007 were mental illness related
  • Incarcerated mentally ill cost an estimated $9 Billion per year
  • Medicaid is the single largest payer of mental health services

Many of the numbers above are staggering, as are the costs associated with treatment. Again, from NAMI:

  • Average per year cost of mental health treatment for an Adult - $1,551
    • Average per year cost of incarceration of an adult – Federal: $28,893.40, Community: $26,163., according to the Bureau of Prisons
  • Each $1 in mental health treatment = $3.68 savings in hospitalizations/criminal activity
  • 69% of adults return to employment following treatment
  • 50,000+ private industry jobs are from Community mental health and substance abuse agencies
  • Physical health services for Medicaid beneficiaries with mental illness is 32% more than those without[1]

What makes this issue of particular urgency is that overall public spending on mental health services dropped $4.35 billion from 2009-2012[2]. While funding is starting to be slowly restored across the nation it will take some time, perhaps several years, before we are back to prior levels of funding. During this time it is imperative to continue to investigate ways in which the cost effects of mental health can be better contained. Applying advanced data management and analytic technology to currently available data can help both providers and system managers better understand cost-effective, evidence-based treatment programs, social service programs/services, payment reform, and much more. In fact, data from many of the systems discussed in my post on quality of care can prove to be equally valuable for financial cost analysis and enable change to payment and service delivery.

For instance, providers could analyze and forecast costs for mental health services provided to Medicaid/Medicare/Commercial Payer (via APCD and/or other mental health public data initiatives) populations. This would be useful for:

  • Comparing cost effective evidence-based mental health treatments in private/public settings
  • Better understanding mental health-related “super utilizers” (e.g. double digit ER visits per year)
  • Better understanding of dual eligible population and cost attribution
  • Analyzing cost effectiveness of social service programs on mental health
  • Analyzing geographic/demographic/provider cost variation
  • Better understanding Medicaid managed care costs
  • Better understanding cost data for rate review activities
  • Detecting potential cases of fraud/waste/abuse

The benefits of integrating those systems also include:

  • Promoting payment models which encourage collaboration and quality of care such as Episodes of Care
  • Comparing benefits of various incentive-based payment models
  • Calculating payments that cover all treatment in a specific Episode of Care, including acute or ambulatory settings
  • Attributing clinical services associated with a specific Episode of Care to appropriate clinician
  • Calculating and understanding avoidable complications associated with an Episode of Care

Conclusion

The impact of mental illness on individuals and our society as a whole is both costly and tragic. Given the limited resources available to address these needs, it is essential that we have a data driven approach to support the allocation of these resources.   Medicaid (the largest payer for mental health services), provides a fertile ground for the use of advance data analytics as a system management tool.

The Center for Medicaid Services has a strong push towards integrated eligibility (which would include many other social services key to supporting those who are untreated), quality improvement and co-ordination of care, payment reform and modernization initiatives. As a result, Medicaid is well positioned to drive change in the mental health arena. With Medicaid enhancements there is also great potential for a trickle-down effect to the rest of the health system as the mentally ill who are in Medicaid could receive a more effective model of treatment, based on analytic support.

This group may transition eventually into commercial healthcare having the benefit of more established and effective treatment plans. As we have seen, the use of advanced data management and analytic/visualization technology can be a unique driver in enabling mental health treatment stakeholders to effectively identify mental health risk factors, model the right blend of effective services to stimulate positive change, determine efficacy of new evidence-based treatments, identify future trends, analyze costs and new ways to improve access to care, enhance co-ordination of care efforts and more. Ultimately, we can achieve change in these areas and positively influence physical, mental and social outcomes with the expanded use of data management and analytics.

[1] Medicaid Institute at United Hospital Fund, “New York Medicaid Beneficiaries with Mental Health and Substance Abuse Conditions” (2011)

[2] National Alliance on Mental Illness, “Medicaid Expansion and Mental Health Care” (May 2013)

 

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