Tag Archives: healthcare system

Why Your Doctor Is Never on Time

Why is it that every time I go to a doctor, I am given an appointment for a precise time, and then every single time the doctor shows up at least 20 minutes late? Does the healthcare system hate me? Do doctors not want to fix the problem? Or are they just simply incompetent?

To dig deeper into the question, we at LeanTaaS dove into the operations of more than 50 healthcare providers this past year. We looked at resource utilization profiles at three different types of clinics – cancer infusion treatment, oncology and hematology – to understand the problem and how best to solve it.

The truth is that most healthcare providers have the patient’s interest at heart and are trying their level best. However, “optimal patient slotting” is a lot more complex than might appear on the surface – in fact, it is “googol-sized” in complexity. The good news is it’s a problem solvable with advanced data science; the sobering news is it MUST be solved if we are to handle the incoming onslaught of an increasing, aging patient population all carrying affordable insurance over the next 20 years.

The Doctor Will Be Right With You. NOT.

There are few things I take for granted in life, and waiting to see a doctor is one of them. The average wait time for a routine visit to a physician is 24 minutes. I am sure I am not the only one who has sat in a doctor’s waiting room thinking, “You said you would see me at 3:00 p.m. – why am I being called at 3:24? This happens every time; I bet you could have predicted it. So, why didn’t you just ask me to come at 3:24 instead?”

A Press Ganey study of 2.3 million patients at 10,000 sites nationwide found that a five-minute wait can drop patient satisfaction by 5%, a 10-minute wait by 10% and more than 10 minutes by 20%.

Source: http://www.pressganey.com/

 

That 24-minute stat is, in fact, not so bad compared with anyone who has had to get an infusion (chemo) treatment, visit a diabetes clinic, prepare for surgery or see just about any specialist. Those wait times can be hours.

Just visit any hospital or infusion center waiting room, and you will see the line of patients who have brought books, games and loved ones along to pass that agonizing wait time before the doctor sees them.

I spent the past year researching this problem and saw for myself just how overworked and harried nurses and doctors operating across the healthcare system are. I spoke to several nurses who have had days they were not able to take a single bathroom break. Clinics routinely keep a “missed meal metric” – how often nurses miss lunch breaks – and most of the ones I spoke to ring that bell loudly every day. I even heard of stories of nurses suing hospitals for having to go a whole day without breaks or meals.

The fact is that long patient wait times are terrible for hospitals, too. Long wait times are symptomatic of chronically inefficient “patient flow” through the system, and that has serious negative impact on the hospital’s economic bottom line and social responsibility:

  • Lower Access and Revenue: A natural corollary to long patient wait times is that the hospital sees fewer patients than it possibly could each day. The Medical Group Management Association found that the average utilization of operating rooms at large hospitals in 2013 was only 53%. Fewer patients served directly implies reduced access to care, lower revenues and higher unit costs.
  • Rising Labor Costs and Declining Nurse Satisfaction: Nurses are an expensive and scarce skill set. Because of the “peaks and valleys” caused by inefficient scheduling during the day, hospitals have to staff for the “peak” and simultaneously experience periods of low activity while still needing significant overtime hours from nurses.

Hospital leaders know this well. Every administrator I spoke to in my research – CEO / CAO / CNO – has some kind of transformation effort going on internally to improve patient flow – “lean” teams, 6-sigma teams, rules for how to schedule patients when they call into various clinics and so on. Leaders know that if patients could be scheduled perfectly and doctors could see them on time, the resulting “smoothing of patient flow” throughout the system would make their facilities, staff and the bottom line much better off.

The Real Reason

It’s not for a lack of motivation that the system is broken. It’s just a complex math problem.

The system is broken because hospitals are using a calculator, standard electronic health record (EHR) templates and a whiteboard to solve a math problem that needs a cluster of servers and data scientists to crunch.

To illustrate why scheduling is such a complex problem, let’s take the case of a mid-sized infusion (chemo) treatment center I studied during my research.

This infusion center has 33 chairs and sees approximately 70 patients a day. Infusion treatments come in different lengths (e.g., 1-2 hours, 3-4 hours and 5-plus hours long), and the typical daily mix of patients for these three types are 35 patients, 25 patients and 10 patients, respectively. The center schedules patients every 15 minutes starting at 8:00 a.m. with the last appointment offered at 5:30 p.m. So there are 39 possible starting times: 8:00 a.m., 8:15 a.m., 8:30 a.m., etc, ending at 5:30 p.m. The center can accommodate three simultaneous starts because of the nursing workload of getting a patient situated, the IV connected, etc. That makes a total of 39*3 = 117 potential “appointment start slots.”

That may not seem like a lot, but it results in 2.6 times 10 to the 61st power possible ways to schedule a typical, 70-patient day. (I’ll save you the math.) That’s 26 million million million million million million million million million million possibilities.

And that number is just the start. Now add in the reality of a hospital – some days nurse schedules are different from others, the pattern of demand for infusion services varies widely by day of week, doctors’ schedules are uneven across the week, special occurrences like clinical trials or changes in staff need to be considered and so on. You are looking at a problem that you can’t solve with simple heuristics and rules of thumb.

How Today’s “Patient-Centric” Scheduling Often Works – and Backfires

Very few hospitals I spoke to understand or consider this math. Rather, in trying to “make the patient happy,” most providers have been trained to use a “first come, first served” approach to booking appointments. Sometimes, providers use rules of thumb based on their knowledge of busy times of day or week, e.g., start long appointments in the morning and shorter ones later.

If hospitals were scheduling patients for one chair, one nurse and the same treatment type, some simple rules could work. But reality is a lot more complicated – the right schedule would need to consider varying treatment times across patients, include multiple treatment rooms/chairs, varying staff schedules, lab result availability and so on. Without sophisticated tools, there is an almost zero chance a scheduler can arrange appointments so treatment durations fall like Tetris blocks that align perfectly over the course of the day, and seamlessly absorb patients as they arrive, orchestrating doctor, nurse and room availability, while accounting for all the other constraints of the operation.

In effect, hospitals are scheduling “blind,” not taking into account the effect of appointments already scheduled before, during or soon after the slot being allotted on a first-come basis. Schedule currently is like adding traffic to rush hour and almost always results in a “triangle shaped utilization curve” – massive peaks in the middle of the day and low utilization on either side.

Typical utilization in an infusion treatment center with 63 chairs

 

Each of the 50 hospitals I spoke to identified precisely with this utilization curve. In fact, they identify with “the midday rush and slower mornings and evenings” so well that they have given them affectionate names – one called it their “Mount Everest,” another “Mount Rainier.”

From a cancer center’s standpoint, this chair utilization curve has several issues even beyond long patient wait times:

  • The center can only see a fraction of patients it could have with a “flatter” utilization curve.
  • Nurse scheduling has to be done for the peak, and the treatment center typically deals with lots of overtime issues.
  • Nurses find it hard to take lunch breaks because of the midday peak, while half the time the chairs are empty.
  • On any day, given the number of interdependent moving parts, a small perturbation to the system (e.g., a patient’s labs are late, another patient didn’t arrive on time) creates a domino effect, further exacerbating delays, not unlike a fender bender in rush hour traffic that delays everyone for hours.

In effect, when hospitals think they are scheduling in patient-centric ways, they are doing exactly the opposite.

They are promising patients what they cannot deliver – instead of giving the patient that 10:00 a.m. Wednesday appointment, an 11:40 a.m. appointment may have been much better for the patient and the whole system.

As we will see, the patient could have had a 70% shorter wait time, the hospital could have seen 20% more patients that week, every nurse could have taken a lunch break every day and a lot less (if any) overtime would have been required.

So How Do You Solve This “Googol-Sized Patient Slotting” Problem?

The solution lies in data science and mathematics, using inspiration from lean manufacturing practices pioneered by Toyota decades ago, such as push-pull models, production leveling, reducing waste and just-in-time production.

In mathematical terms, it means taking those 10^61 possibilities and imposing the right set of “constraints” – demand patterns, staffing schedules, desired breaks and whatever is unique to the hospital’s specific situation – to come up with a much tighter set of possible patient arrangements that solve for maximizing the utilization of hospital resources and therefore the number of patients seen.

In the case of the infusion center, the algorithm optimizes utilization of infusion chairs, making sure they are occupied uniformly for as much of the day as possible as opposed to the “peaks and valleys” in Figure 3. In essence, “rearranging the way the Tetris blocks (patients) come in” so they appear in the exact order they can be met by a nurse, prepped and readied for a doctor whose schedule has been incorporated into the algorithm.

The first step in doing this is mining the pattern of prior appointments to develop a realistic estimate of the volume and mix of appointment types for each day of the week.

The second step is imposing the real operational constraints in the clinic (e.g., the hours of operation, doctor and nurse schedules, the number of chairs, various “rules” that depend on clinic schedules, as well as patient-centric policies such as that treatments longer than four hours should be assigned to a bed and not a chair).

Finally, constraint-based optimization techniques can be applied to create an optimal pattern of “slots,” which reflect the number of “appointment starts” of each duration.

In the case of the infusion center, that means how many one-hour duration, three-hour duration and five-hour duration slots can be made available at each appointment time (i.e. 7:00 a,m., 7:15 a.m., 7:30 a.m. and so on).

Optimized shape of utilization curve for the same center as in Figure 1. 20% lower peak, much smoother utilization of resources, significant capacity freed

 

Doing this optimally results in moving the chair utilization graph from the “triangle that peaks somewhere between 11:00 a.m. and 2:00 p.m.” in Figure 3 to a “trapezoid that ramps up smoothly between 7:00 a.m. and 9:00 a.m., stays flat from 9:00 a.m. until 4:00 p.m. and then ramps down smoothly from 4:00 p.m. on” in Figure 4.

Coming up with realistic slots that keep patients moving smoothly throughout the day cuts patient waiting times drastically, reduces nurse overtime without eliminating breaks and keeps chair utilization as high as possible for as long as possible. Small perturbations in this system are more like a fender bender at midnight, a small annoyance that causes a few minutes of delay for a small number of people instead of holding up rush hour traffic for hours.

Smoothing Patient Flow – A Large Economic Opportunity

The above graphs are sanitized versions of real data from a cancer infusion treatment center at a real hospital that used these techniques to solve their flow problems. The results they achieved are staggering and point to the massive economic and social opportunity optimal patient flow presents.

Post implementation of a product called “LeanTaaS iQueue,” they now experience:

  • 25% higher patient volumes
  • 17% lower unit cost of service delivery
  • 31% decrease in median patient wait times
  • 50% lower nurse overtime
  • Significantly higher nurse satisfaction – no missed meals

Imagine applying this kind of performance improvement to every clinic, hospital and surgery suite in the country and the impact it will have on population health through increased patient access to the system.

The Problem Is Going to Get a Lot Worse Unless Providers Address It Now

This problem is going to get a lot worse for a simple reason – the demand for medical services has never been stronger, and it’s only going to increase. Just looking at the U.S. market:

  • Population Growth: By 2050, there will be more than 438 million Americans, up from 320 million in 2015.
  • Demographics: By 2030, more than 20% of the country is expected to be older than 65, up from 15% in 2015 – increasing the demand for chronic clinical therapies. In raw numbers, the Census Bureau estimates that by 2030, when the last round of Baby Boomers will hit retirement age, the number of Americans older than 65 will hit 71 million, up from 41 million in 2011, a 73% increase. When this happens, one in five Americans will be older than 65. Not surprisingly, by 2025, 49% of Americans will be affected by a chronic disease and need corresponding therapies.
Access to healthcare is a looming crisis – multiple drivers of significant demand growth

  • The Affordable Care Act: The Affordable Care Act will add 30 million Americans to the healthcare system by 2025. That means more demand for healthcare – more doctor visits, more hospital visits, more emergency emergency room visits and more need for resources (e.g., surgery rooms, MRI / CAT scans). Reimbursements will increasingly depend on outcomes and efficacy, quality of care and patient access. Unless providers become a lot more efficient in how they process and treat patients, they will need to spend billions in capital spending on new infrastructure – clinics, operating rooms, infusion centers and the like.
  • In an online poll conducted by the American College of Emergency Physicians (ACEP), 86% expect emergency visits to increase over the next three years. More than three-fourths (77%) say their ERs are not adequately prepared for significant increases.
  • The Commonwealth Fund, a New York-based fund that tracks healthcare performance, projects that primary care providers will see, on average, 1.34 additional office visits per week, accounting for a 3.8% increase in visits nationally. Hospital outpatient departments will see, on average, 1.2 to 11 additional visits per week, or an average increase of about 2.6% nationally.
  • It is estimated that the U.S. will face a shortage of 90,000 physicians and 500,000 nurses by 2030.

The Good News

Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, “flow”-based systems have experienced.

The good news is that there are tools, software and resources that can be used to bring about this transformation. Companies like LeanTaaS are at the forefront of this thinking and are applying complex data science algorithms to help hospitals solve these problems.

Hospitals that are serious about solving patient flow issues and the related problems now have access to the best computational minds and tools.

I see a world in which our healthcare system can see every patient on time without imposing hardship on care providers, disruption on current processes or increasing cost of services.

Here’s to that world!

Stigma’s Huge Role in Mental Health Care

The role of stigma for people who are in need of mental health treatment is both profound and devastating. According to a 2011 study by the Association for Psychological Science, only 60% of people diagnosed with mental health problems reported receiving treatment. That means 40% of the millions of people in the U.S. who need professional help are getting no treatment whatsoever. Social stigma, myths and stereotypes play a huge role in limiting both access to care and discouraging people from pursuing mental health treatment. The problem is multifaceted and complex and has a wide-reaching effects on people’s education, employment, health, well-being and relationships.

There are many forms of stigma and stereotypes. First, there is a widespread public perception that people with mental illness are dangerous, unpredictable and responsible for their own illness and not deserving of compassion and care. As a result, people in need of help are excluded from jobs, education and much-needed social interaction.

This problem also plays out in the professional medical setting, where negative stereotypes often lead medical providers to be less likely to focus on the patient rather than the disease and to not place the needed focus on recovery and referral for needed consultation and care.

Stigma in society and lack of awareness among medical providers also contributes to what is known as self-stigma. That is: People in need of help believe these stereotypes themselves and develop low self-esteem, which results in denial, attempts to hide problems, alcohol and drug abuse and a sense of hopelessness — they feel they are unable to recover, so why try? These are the people who make up the 40% not seeking treatment and consultation.

Stigma results in a double problem for many people. They have real underlying symptoms, which lead to an actual disability, while myths and misconceptions lead to stereotypes and prejudice. Too often, people turn against themselves. Depression, for example, has been referred to by mental health professionals as “rage turned inward.” This can lead to fear of rejection, isolation and hostile behavior. The result often is that the needed health care system is replaced by the criminal justice system.

How many people incarcerated today have an underlying untreated mental health condition? My guess is most, if not all. These are the people who did not pursue potential life opportunities for themselves but rather pursued illegal drugs or crime out of a sense of low-self-esteem and hopelessness. The overall result is both devastating to them and society as a whole.

Underlying mental health issues also have a huge impact on both healthcare and disability costs for private and public employers, health and disability insurers and both Medicare and Medicaid and the Social Security disability system (SSDI). How many people collecting private or public disability have an underlying, undiagnosed mental health problem? Nobody really knows, but many disability experts believe the number is staggering. The resulting costs to employers, insurers and taxpayers of untreated or undiagnosed mental health issues is in the billions of dollars.

In 2003, I helped conduct an unpublished study for a major U.S. corporation regarding its active employees out of work on full disability with a primary diagnosis of depression. The analysis cross-referenced these employees’ disability claim data with their health insurance data base. It was found that 80% of the primary treating providers in the healthcare benefit side had no mention whatsoever of a primary or secondary diagnosis of depression. This means that their primary treating provider or “family doctor” was either unaware of the underlying mental health issues or failed to acknowledge or consider the possibility.

What was not able to be studied in this research was how many workers out on disability or workers compensation for a “bad back” really had an underlying mental health issue. The study did determine the No. 1 and 2 co-morbidities for employees out on disability for depression was musculoskeletal conditions and gastrointestinal conditions. The overwhelming number of medical providers treating and submitting claims for these co-morbidities (80%) had no mention of an underlying mental health issue despite the fact that their patient was out of work on full disability with a primary diagnosis of depression. The healthcare and disability costs of these employees out on full disability with a primary diagnosis of depression was staggering and in the millions just to this U.S. corporation. Because this large employer was self-insured for healthcare, disability and workers’ compensation these costs go directly to its bottom line. These costs are then indirectly passed on to corporate customers and the general public purchasing the company’s products and needed services.

What needs to be done to address underlying and untreated mental health conditions?

I do not believe any new federal legislation is required at this time. The Affordable Care Act (ACA), the Americans with Disabilities Act (ADA) and the Mental Health Parity Act are all in place to help people receive needed mental healthcare access. There is no reason people should not seek professional help that they need.

As in most complex public health issues, the answer lies in awareness, education, outreach and research dollars. Educating the public is a very difficult task. As we have learned the hard way with overall prejudices, urban myths and misinformation in society, in general educating people can take generations. Medical authorities in leading medical schools and institutions have also stated that documented research and best practices based on evidence-based medicine can take 20 years to filter down to local medical practices, if ever.

People suffering with underlying mental health issues don’t have 20 years to wait for proper referral and treatment. Medical professionals on the front line need to be educated today to ask the right questions with their patients about potential underlying mental health issues and help reassure people that the overwhelming majority of mental health issues can be diagnosed and successfully treated.

As a society we can no longer allow people to hurt themselves or others when treatment is readily available for people who need help because of genetic and other environmental causes that are no fault of their own. How many of our major problems such prejudice and gun violence have a root cause in untreated mental health issues? Maybe all of them.

Three Ways to Fix Health Insurance (No Matter What Happens With Obamacare)

Whether Obamacare is fully implemented or collapses under the weight of its 906 pages of law, its 15,000 pages of regulations, and the well-publicized glitches in its rollout, the underlying, ineluctable problems with health insurance remain largely unresolved. How we respond will determine whether we hit the iceberg and sink or veer away in time to save our private health care system.

To understand some of the real cost drivers for health insurance, let’s look at the “Doe” family. John and Jane Doe pay $600 per month for health insurance for their family of four. Most states have a list of benefits, or “mandates,” that, by law, insurers must cover – from gastric electrical stimulation to breast implant removal. While some states have fewer mandates, others have piled them on. (Utah has 26, while Rhode Island, Maryland, and Minnesota all have at least 65.) The Doe family could see savings up to 50% or more on their insurance rates if they could just buy a basic health plan without the mandates. That could drop their monthly premium to as low as $300.

Premiums would come down even further if tort reform ended “jury lotto,” where patients get large, unjustifiable settlements or jury awards for medical treatment gone awry. While doctors are human and are certainly capable of errors, the legal system allows for these big settlements even when doctors are not at fault.

Here’s the scenario: Imagine that Doctor Smith treats a woman who complains of an ear infection and gives her a prescription, telling her to call if the condition doesn’t improve. The woman dies a few days later from a brain tumor. The family sues, alleging the doctor should have been able to diagnose the tumor. The jury sympathizes with the grieving family, believes that doctors should be omniscient, and reasons that rich doctors and their insurers can easily afford a large payment, so the family receives a $10 million award. The pestilential result is that everyone’s health insurance rates go up to cover such settlements, the doctor’s malpractice rates increase, and he now orders extra tests for the next patient to protect himself from the next lawsuit.

Tort reform could provide significant savings to the health care system, resulting in insurance premiums dropping as much as 10%. The Doe family might now see its insurance rate go down to as low as $240 – a whopping 60% drop in their monthly premium.

(Some have talked about allowing consumers to buy across state lines to reduce premiums even further by increasing competition and making it easier to buy policies in states that mandate fewer benefits, though this has not yet been shown to be true.)

A third way to drive insurance rates down is consumer engagement – changing the dynamic so that people actually know and care about what their health care costs. As long as it is Other People’s Money (OPM), there is little incentive to lower the cost of care, which continues to rise and, in turn, drives up insurance rates. (Contrary to public opinion, a recent analysis by the accounting firm PriceWaterhouseCoopers found that health insurers pay an average of 87 cents to providers of medical and pharmaceutical services out of each premium dollar and, after expenses, earn just three cents in profit. The problem, then, with health insurance isn’t that insurers are gouging people; it’s that costs are high, and consumers are generally unaware and unconcerned.)

So how can we get engaged? Even while we wait for the regulatory and legal changes that will need to occur to reduce mandates and rein in unjustified malpractice awards, here are two things for consideration in lowering health care costs.

First, we need to change our mindset as consumers when it comes to health insurance. What if we treated health insurance more like homeowner’s insurance? In other words, what if we bought coverage for the unexpected (illness or injury), while paying for our day-to-day medical needs out of pocket, as we do for home repair and maintenance? Great insurance options to consider include high-deductible health plans with linked Health Savings Accounts (HSAs). In general, we need to shift our thinking on health care from OPM (Other People’s Money) to MM (My Money).

Second, how about a radical “Groupon” type of approach? Let’s say John Doe is diagnosed with a hernia and needs an operation. There are three hospitals in town. All three are fully credentialed and meet quality standards. John’s surgeon can admit to them all. Hospital 1 is an older, traditional facility in a more frugal setting, with an estimated cost for the surgery at $10,000. Hospital 3 is a new, state-of-the art “Hyatt” hospital with high end amenities and a fancier environment – estimated cost: $50,000. Hospital 2 is in the middle, with an estimated $25,000 price tag. Here’s what John’s health insurance company tells him:

“You are covered at all three hospitals. But if you go to hospital 3, you have an additional $2,000 copay. If you go to hospital 2, we’ll cover the cost at 100%. If you go to hospital 1, we’ll pay you $2,000. Your choice.”

John is comfortable at hospital 1 and likes the idea of getting rewarded for choosing a lower cost setting. He has his surgery done there. He gets the $2,000, while the insurance company saves $38,000 off the cost of hospital 3.

This kind of savings will eventually be reflected in lower premiums for everyone. Decisions like John’s will also encourage hospitals to lower costs, as market forces come into play, leading to even more reductions in insurance costs.

Conclusion

We are not going to reform the health care system and resolve our health insurance problems overnight. And even if Obamacare is fully implemented, we still need to make fundamental changes, including how we see and use health insurance as consumers. If we are going to steer the Titanic away from the iceberg, we as consumers need to change our mindset and get engaged – and have financial incentives to do so, leading to powerful market forces. Once the sleeping giant of the American consumer awakens, watch out.

19 Specific Taxes Directly Related To Healthcare Reform

Introduction
As we approach April 15 and many of us are thinking about our taxes, we are starting to notice some tax changes and many of these are related to healthcare reform, i.e. the Patient Protection and Affordable Care Act of 2010 (PPACA). Whether or not you are “for” or “against” healthcare reform as currently legislated, you will definitely feel the impact of some new taxes.

This article attempts to identify and list the taxes that are directly related to the Patient Protection and Affordable Care Act. This article is intended to list and identify taxes associated with healthcare reform — it is not intended to take any formal position for or against healthcare reform. In fact, the author has strong opinions that the US healthcare system is desperately in need of serious healthcare reform and that many aspects of the current reform approach outlined in the Patient Protection and Affordable Care Act make serious attempt to address some of the key issues.

Background
Based upon the count of many experts, there appear to be 19 specific taxes or increased taxes directly related to healthcare reform, estimated by some experts to total $500 billion over 10 years. I have ranked the taxes from largest to smallest, with a description of the tax and when it was effective or will be effective for those not yet in effect.

Summary of PPACA Related Taxes

  1. Surtax on Investment Income ($123B): By far the largest tax and going into effect January 2013, this is a new 3.8% surtax on investment income for households with more than $250,000 income.1
  2. Increase in Medicare Payroll Tax ($87B): Beginning January 2013, this tax increases the Medicare employee and self-employed tax on wages in excess $200,000 for an individual/$250,000 for a family by 0.9%.2
  3. Mandate Tax ($65B): Beginning January 2014, any individual without a qualified health plan will be subject to an income surtax.3 In addition any employer not offering health coverage, and at least one employee qualifies for a health tax credit, will be subject to a tax. Any employer requiring a waiting will be subject to an additional tax.4
  4. Tax on Health Insurers ($60B): Beginning January 2014, all health insurance companies and health plans are subject to a federal premium tax, which phases in until 2018.5
  5. Excise Tax on Comprehensive Health Insurance Plans ($32B): Beginning in January 2018, there will be a 40% tax on “Cadillac” health insurance plans.6 Cadillac health insurance plans are those with very rich benefits and are determined by a comparison to an inflation adjusted premium level.
  6. “Black Liquor” Tax ($24B): This is a tax on a special type of bio-fuel.7 Tax in effect during 2007 – 2009, ended in January 2010 as part of healthcare reform bill.
  7. Tax on Innovator Drug Companies ($22B): $2.3 Billion annual tax on the industry imposed relative to sales made that year. Began in January 2010.8
  8. Tax on Medical Device Manufacturers ($20B): Beginning January 2013 there is a 2.3% excise tax on all items >$100.9
  9. High Medical Bills Tax ($15B): Beginning January 2013, the threshold for deducting high medical bills was increased to 10% of adjusted gross income.10
  10. Flexible Spending Account Cap ($13B): Beginning January 2013, the formerly unlimited FSA is now capped at $2,500. This tax has been called the “special needs kids tax” since this eliminates the option of families with special needs children to tax effectively pay for tuition for these children.11
  11. Medicine Cabinet Tax($5B): As of January 2011, Health Savings Accounts(i.e., HSA) are no longer able to purchase non-prescription, over-the-counter medicines other than insulin.12
  12. Elimination of Prescription Drug Subsidy($5B): As of January 2013, employers no longer able to deduct prescription drug subsidy for coordination with Medicare Part D drug program.13
  13. Codification of “economic substance doctrine” ($5B): Beginning January 2010, new provision permitting the IRS to disallow legal tax deductions when the IRS deems the action lacks “substance.”14
  14. Tax on Indoor Tanning Services ($3B): Beginning July 2010, new 10% excise tax on indoor tanning salons.15
  15. HSA Withdrawal Tax Increase ($1.4B): Beginning January 2011, taxes for withdrawals were increased from 10% to 20%.16
  16. Health Insurance Executive Compensation Limit ($0.6B): Beginning January 2013, PPACA establishes a $500,000 limit for annual executive compensation.17
  17. Blue Cross Blue Shield Tax Increase ($0.4B): Beginning January 2010 a special tax deduction is permitted only if loss ratio is greater than 85%.18
  18. Excise Tax on Charitable Hospitals (minimal): Beginning January 2010, new $50,000 tax on hospitals if they fail to meet specific rules established by CMS.19
  19. Employer Reporting of Health Insurance on W-2 (minimal): Requires employers to include additional information on W-2’s regarding health insurance plans.20

Bottom Line
Although there may be many well intended uses for these taxes, it is clear that healthcare reform has significantly increased tax revenues. The outstanding question remains, will the Patient Protection and Affordable Care Act reduce the cost of health care, increase the access to care, and improve the quality of care for everyone? These were and have been the primary objectives of any healthcare reform effort. As we as a country look forward to our financial futures, we need to carefully assess the impact of health care reform, make modifications where appropriate, and refocus on the key objectives to be sure we achieve what we all know to be the key issues at hand: reduce costs, improved access, and maximum quality of care.

1 Reconciliation Act, pages 87-93.

2 Reconciliation Act, pages, 87-93, 2000-2003.

3 PPACA, page 317-337.

4 PPACA, Pages 345-346.

5 PPACA, Pages 1986-1993.

6 PPACA, Pages 1941-1956.

7 Reconciliation Act: Page 105.

8 PPACA, Pages 1971-1980.

9 PPACA, Pages 1980-1986.

10 PPACA, Pages 1994-1995.

11 PPACA, Pages 2388-2389.

12 PPACA, Pages 1957-1959.

13 PPACA, Page 1994.

14 Reconciliation Act, Pages 108-113.

15 PPACA, Pages 2397-2399.

16 PPACA, Page 1959.

17 PPACA, Pages 1995-2000.

18 PPACA, Page 2004.

19 PPACA, Pages 1961-1971.

20 PPACA, Page 1957.