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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The Underground Economy - Part 1 - The Hidden Tax

The underground economy is driving a hole in the collection of income taxes, social security, unemployment and Medicare to the tune of hundreds of billions of dollars annually within the United States. Yet significant questions remain about how big the problem is, as well as what should be done about it. Dire lessons can be learned from countries that have allowed tax evasion to become the norm. With estimates of rates ranging from 30-48% in countries like Greece, Italy and Romania, it isn’t surprising that the remaining tax base is completely insufficient to keep government afloat.

While the U.S. doesn't rise to that level (yet) when it comes to fraud as well as businesses evading obligations, including taxes, the gap raises a number of significant issues:

  • Failure by individual businesses, and in fact, large portions of entire industries, to appropriately report and pay taxes results in a revenue gap forcing cuts in services or other significant decisions. Studies show that most of the federal budget deficits in recent years would have been eliminated completely if income taxes were accurately reported and paid.
  •  When businesses don’t contribute to social security, unemployment or workers’ compensation coverage, as well as fail to follow health and safety rules, their employees are put at risk of bodily and financial harm. This can result in a long-term drag on other social services, exacerbating the financial gap noted above.
  • A business that fails to pay local, state and national taxes can gain a cost advantage of 30-40% over their honest counterparts. Within some industries that have tight margins, like restaurants or groceries, that can be the difference between continued operations and bankruptcy. In other industries, where there is bidding and price competition, ranging from construction to janitorial services, they undercut legitimate competitors and win the work.
  • As the title implies, there is another impact – the firms that don’t pay into these taxes and provide coverage like unemployment and workers’ compensation end up raising the tax rates for all the others. This “hidden tax” is something that states in the U.S. are starting to study and pay more attention to as they address the underground economy. One way to think of this is the same way that studies from the insurance industry show that fraud is increasing our auto insurance rates by 10% overall, and higher in some areas.

Within the U.S., the Internal Revenue Service (IRS) publishes a study on the “tax gap” every 5 years. The last study, released in 2012, showed a total gap of 16.9%. In other words, only 83.1% of income taxes were reported and paid. Not surprisingly, compliance rates were much higher among individuals that drew regular paychecks working for a separately-owned company that reported those to the government. The worst compliance rates were amongst self-employed businesses and farms.

During my time with the underground economy task force in Washington State, we utilized the methodology from the IRS to estimate our own gaps, just looking at business revenue and income taxes, unemployment and workers’ compensation. Net underreporting stood at $704 million annually, but we all knew this was underestimating the size and scope of the problem.

While some businesses actively evade compliance, others are unwittingly non-compliant due to the sheer complexity of the laws. Compounding this problem of non-compliance is the growth of the disruptive economy.  As companies that allow informal relationships and part-time business spread, like UberX and Airbnb, existing gaps widen significantly.  Some Uber drivers don't consider what they are doing to be "business" at all, and tend to fail to report that as income.  Added to that is potential fraud in failing to disclose their commercial business to their insurance companies, which would generate much higher rates, and the problem begins to compound.  These innovative developments in the new economy and  new customer interactions, driven by technology,  should be supported but laws, regulations, and education need to catch up quickly to prevent a rapid spread of the gray economy.

Recently, I had the opportunity to join a roundtable in the province of Ontario, Canada, that brought together regulators and public officials, the Chamber of Commerce, economists, and individuals that represent this disruptive economy, including the Canadian CEO of Airbnb.  The discussion was lively, and we came up with many opportunities to improve.  Hearing Airbnb say "please regulate us" was a breath of fresh air, and represented their desire to be good corporate citizens that just want clarity.

This background definitely leads to more questions – what are the problems, and where do they stem from? What can government do to help stem the tide? In the next two parts of this blog series, I will address those areas as well. In the meantime, please follow me on Twitter @CarlHammersburg and join the conversation.

 

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Student projections help teachers tailor instruction, improve student outcomes

Last week, I had lunch with a friend who I hadn’t seen in quite some time.  As I approached the table, I noticed my friend busily writing away in her journal.   With a fantastic smile, my friend shared that she was writing down her personal and professional goals for the upcoming year.  For her, along with many others, setting goals and working toward them helps her in being proactive as she welcomes 2015.

Sometimes we often remark, “If I had only known, I could have done things differently.”  As I drove home, I caught myself setting my goals for 2015 based on my reflections of the past two years rather than future possibilities.

What if we were able to set goals for our students knowing more than just what is said by other teachers?  What if we truly had an educator’s crystal ball to predict the best way to meet students where they are and get them to where they need to be?

In many states and districts across the nation, educational leaders and teachers now have the ability to see how their students are projected to score on both end of course assessments, as well as college readiness indicators. By applying predictive analytics, that data reveals the potential of students, along with their possible challenges, that will allow data-driven educators to better prepare their students for success.

When educators today design lessons, plan interventions, or foster growth opportunities and enrichment for their students they are no longer dealing with guesswork. They are dealing in actual goal setting. Educators can see where students may need additional help, and where they can succeed. Teachers are now setting real, tangible, individualized goals for their students.

With that in mind, I had in-depth conversations with two educators: an assistant principal from Pennsylvania and an instructional coach from North Carolina.

Corey Mosher, Assistant Principal of Athens High in Athens Area School District in Pennsylvania, notes that using student projection data in PVAAS was a game changer for his school. “I love PVAAS projection data,” shared Mosher in our conversation. Mosher led his faculty in becoming more aware of and comfortable with using data for school improvement. By using projection data, Mosher shared that Athens High is “using the projections data to ensure students are enrolled in the best courses.” Furthermore, he notes “projection data ensures that teachers have the best data available to hone in individual instruction offering early, targeted interventions.” Using student projection data has allowed for Athens High to improve student outcomes by ensuring proper course placement and early, effective remediation for students.

Dr. Theresa Melenas is an instructional coach and assistant principal for Sampson County Schools in North Carolina. She continues to work with teachers throughout the district on using student projection data. In addition to increasing the number of students enrolled in more rigorous math courses, she continues to work with teachers and school leaders to use projections to provide earlier interventions for students. “If we know a student shows an interest in a STEM field, we can use student projection data as early as middle school to provide them proper support for success in higher level mathematics courses.”

Both of these educators used student projections to better plan individualized instruction that ultimately improved student outcomes. As educators, we don’t have to guess anymore. We don’t have to base all of our instructional decisions on the information that is given to us after it has been filtered through a former teacher’s opinion. We can harness the power of authentic assessments, the power of technology, and the power of predictive analytics to actually steer and personalize the instruction of every student.

As I looked through the windshield on my way home, I was excited about what that means for every student in every school. An education that is built exactly for the needs of each student. And I am thrilled to see what 2015 has to offer.

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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 MentalHealth.gov, 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. http://archive.samhsa.gov/data/2k9/165/suicide.htm

[4] National Alliance on Mental Illness (2013) Mental Illness facts and numbers. http://www.nami.org/factsheets/mentalillness_factsheet.pdf

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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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