Al Lewis calls workplace wellness programs “get well quick schemes”

Al Lewis is an actuarial consultant that has long focused on challenging wellness and care management vendors to prove their value.  He founded the “Disease Management Purchasing Consortium” and established a training and certification program for “critical outcomes report analysis.”

Al has been calling out the methodological carelessness and dirty tricks of wellness and care management vendors and health plans for years.   These shoddy and unethical methods produce deceptively optimistic results, often to the delight of the customers of the programs who crave evidence that they made a wise choice. Many of the methods have been discredited long ago, but like cockroaches and ants, they just keep coming back. Faced with this unsavory state of affairs over many years, poor Al has resorted to sarcasm — probably partly to avoid getting bitter, and partly to keep his audience awake long enough to absorb the otherwise dry, tedious concepts.

He recently collaborated with Vick Khanna in a blog post in Health Affairs that focused on a particular type of wellness and care management program — workplace wellness — now a $6B industry.  Such programs typically are funded and sponsored by employers, and involve incentivizing employees to complete a health risk assessment and then, hopefully, pursue healthier lifestyle behaviors. Employers purchasing these programs typically believe they will lead to substantial, short term increases in worker productivity and decreases in health care costs. The blog post is definitely worth reading.

To summarize:

  1. Both workplace wellness program vendors and the benefit consultants who advocate for them have conflicts of interest which lead them to use deceptive methods and publish papers and marketing material which claim implausible levels of savings and return-on-investment.
  2. Although health plans often sell workplace wellness programs to self-insured employers (for a profit), virtually none of them believes they really produce savings, so they don’t spend the money on such programs for the fully-insured business for which the health plan itself bears the risk.  Health plans don’t eat their own dog food.
  3. The most common trick is to compare the outcomes for highly motivated employees who choose to complete the health risk assessments and participate in wellness interventions to the outcomes for poorly motivated employees who do not.  Epidemiologists call this “volunteer bias.”  It is a problem in evaluation studies of all types of member/patient-facing programs, but is obviously an even bigger problem with workplace wellness, when motivation to change behavior is the whole point of the program.
  4. Other common tricks include taking credit for improvements that occur due to random variation (“regression to the mean”), or taking credit for improvements that occurred before the program actually started — as was the case with the widely-touted results from Safeway’s famous workplace wellness program.
  5. They recommend that employers should avoid these “get well quick” schemes and, instead, do the harder work of creating a deep culture change promoting wellness.  If employers want to try workplace wellness programs, they should at least commit to identifying and then counting the events that the wellness program is intended to reduce to see whether they really decrease across the entire work force after the program is implemented.
  6. Lastly, they point out that the workplace wellness industry convinced the federal government to include taxpayer-financed wellness incentives in the Affordable Care Act.   The Federal Employee Plan is in the process of picking a wellness vendor.  They recommended dropping federally-funded wellness programs until valid evaluations show they work.
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Conceptualizing “over-treatment” waste: Don’t deny health economics

A Health Policy Brief published in Health Affairs on December 13, 2012 referenced an analysis published last April in JAMA regarding waste in health care.  In this analysis, Don Berwick (one of my health care heroes) and Andrew Hackbarth (from RAND) estimated that waste in health care consumed between $476 billion and $992 billion of the $2.6 trillion annual health care expenditures in the US.  That’s 18-37% waste.  They divided this estimate into 5 categories of waste.  Their mid-point estimates are as follows:

Berwick and Hackbarth estimates of waste in health care - JAMA 2011

They consider “failures in care delivery” to include failures to execute preventive services or safety best practices, resulting in avoidable adverse events that require expensive remediation.  By “failures of care coordination,” they mean care that is fragmented, such as poorly planned transitions of care, resulting in avoidable hospital readmissions.  They categorize as “overtreatment” care ordered by providers that ignored scientific evidence, were motivated to increase income or to avoid medical malpractice liability, or out of convenience or habit.  They considered “administrative complexity” to be spending resulting from “inefficient or flawed rules” of insurance companies, government agencies or accreditation organizations.  They estimated the magnitude of administrative complexity by comparing administrative expense in the US to that in Canada’s single payer system.  They considered “pricing failures” to be prices that are greater than those which are justified by cost of production plus a “reasonable profit,” presumably due to the absence of price transparency or market competition.  Finally, they considered “fraud and abuse” to be the cost of fake medical bills and the additional inspections and regulations to catch such wrongdoing.

Underestimating Over-treatment

These estimates are generally in alignment with other attempts to categorize and assess the magnitude of waste in health care.  But, I think Berwick and Hackbarth’s estimates of “overtreatment” are probably far too low.  That’s because they, like so many other health care leaders, are so reluctant to address the issue of cost-effectiveness.  Obviously, the definition of over-treatment depends on one’s philosophy for determining what treatments are necessary in the first place.  Everyone would agree that a service that does more harm than good for the patient is not necessary.  Most would agree that a service that a competent, informed patient does not want is not necessary.  Some argue that, if there is no evidence that a treatment is effective, it should not be considered necessary, while others argue that even unproven treatments should be considered necessary if the patients wants it.   Berwick and Hackbarth are ambiguous about their application of this last category.

But, the big disagreement occurs when evaluating treatments for which there is evidence that the treatment offers some benefit, but the magnitude of the benefit is small in relation to the cost of the treatment.  This is a question about cost-effectiveness.  It is at the heart of medical economics.  In my experience, most health care leaders and an even higher proportion of political leaders choose to deny the principles of medical economics and the concept of cost-effectiveness.  They describe attempts to apply those principles as “rationing” — a term which has taken on a sinister, greedy meaning, rather than connoting the sincere application of rational thought to the allocation of limited resources.   Berwick and Hackbarth implicitly take that view.  They are unwilling to define over-treatment based on cost-ineffectiveness.

The analysis I want to see

For years, I’ve been looking for an analysis that attempted to estimate the magnitude of waste from over-treatment based on the principles of health economics.  The diagram below illustrates the hypothetical results of the type of analysis I’d like to see.

Diagram re Conceptualizing Overtreatment

 In this diagram, the horizontal axis represents the total cost of health care to a population.  I don’t want to see the entire US health care system.  What is more relevant is the population served by an Accountable Care Organization or an HMO.  To create such a diagram, we would first need to break down health care cost into a large number of specific treatment scenarios.  Each of these scenarios would specify a particular treatment (or diagnostic test) with enough clinical context to permit an assessment of the likely health and economic outcomes.  For each scenario, each of the possible health outcomes would be assigned a probability, a duration, and a quality of life factor.  My multiplying the duration by the quality of life factor, we could calculate the “quality-adjusted life years” (or “QALY”) for the outcome.  Then, by taking the probability-weighted average of all the possible health states for the scenario, and then dividing the result by the cost, we could calculate the “cost-effectiveness ratio” for the scenario, measured in “$/QALY.”   Then, we would sort all the treatment scenarios by the cost-effectiveness ratios, with the treatment scenarios with the most favorable health economic characteristics on the left.

Some of the scenarios will generate net savings, such as for certain preventive services where the cost of the avoided disease is greater than the initial cost of the preventive service.  These are clearly cost-effective.  On the other end of the spectrum are scenarios that offer net harm to the patient, such as when adverse side-effects are worse than the benefits of the treatment.  These are clearly cost-ineffective.  In the middle of these extremes are scenarios where there is a positive net benefit to the patient and a positive net cost borne by the population.

If a person rejects the principles of health economics, they would consider all of these middle scenarios to be “necessary” or “appropriate” regardless of how small the benefits or how large the costs.  But, among those who accept the principles of health economics, some of these scenarios could be judged to be cost-effective and others to be cost-ineffective.  Such judgments would presumably reveal some threshold cost-effectiveness ratio that generally separated the scenarios into cost-effective and cost-ineffective.  Since different people have different values, their judgments could reveal different cost-effectiveness thresholds.  If we had many people making these judgments, we could find a range of cost-effectiveness ratios that were considered to be reasonable by 90% of the people.    Applying this range to all the treatment scenarios, one could find a group of scenarios that were considered wasteful by most, and another group of scenarios that were considered wasteful only by some.

Variations on this theme have been used throughout the world for decades by various practice guidelines developers, healthcare policy analysts, health services researchers and health economists.  It is complex and time-consuming.  As I’ve discussed before, it is also controversial in the United States.

Right now, in the US, we all recognize that health care costs are too high.  We’re all focusing on merging providers into larger organizations, installing computer software, and exploring new reimbursement arrangements to address the problem.  But, I’m convinced that over-treatment with cost-ineffective services is a major source of waste.  We will inevitably get back to the core issue of having to figure out which treatment scenarios are wasteful.  We will inevitably have to overcome our denial of health economics and our irrational fear of rational allocation.


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Is current low trend in health care cost growth due only to recession? Ken Kaufman suggests that 5 other factors may be contributing.

In a very interesting recent post to the Health Affairs blog, Ken Kaufman challenges the widely repeated assertion that the current low level of health care cost growth can be attributed to the recession.  After decades of double digit trend, the rate slowed in recent years down to a mere 3.9% in 2010.  The conventional wisdom is that recessions cause decreases in employment by employers offering good insurance and general recessionary belt-tightening includes reductions in utilization of discretionary health care services.

Kaufman noted that inpatient hospital utilization by Medicare patients dropped 8% from 2006 to 2010 — patients that are presumably retired and therefore not affected by recessionary unemployment.  He also noted that states with a larger drop in Medicare utilization were the ones with the smallest drop in employment rates, the opposite of what one would expect if the recession was the driver of the drop.  From these observations, Kaufman proposes that we should look for other factors at play.  He suggests an initial list of five other factors:

  1. As doctors move from entrepreneurial self-employment to employment by hospitals and large groups, they come under the influence of care protocols, disease management and other clinical programs that attempt to drive down avoidable utilization
  2. As hospitals’ revenue growth slowed, they changed from an “all things to all people” philosophy to a policy of eliminating unprofitable programs
  3. New “care models,” including new approaches to physician incentives and reimbursement, are starting to have an effect
  4. Dramatic shift from brand name to generic drugs
  5. Health care utilization may have reached the point of “diminishing marginal utility,” where people’s appetite for more is diminished and other alternatives for resource allocation are relatively more appealing than more health care

As we (hopefully) continue to recover from the recession, everyone expects health care trends to creep back upward.  But, perhaps they won’t creep all the way back up to the teens due to these forces keeping cost growth in check.

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Telling a 46 year health care cost growth story in one graph

In a recent post to the Health Affairs Blog, Charles Roehrig, an economist who serves as VP and director of Altarum’s Center for Sustainable Health Spending, presented some very interesting graphics of long term health care cost growth in the U.S.  He shows the often-presented statistic of health care cost as a percent of Gross Domestic Product (GDP) over the 46 year period since Medicare was established in 1965.  The climbing graph is bumpy due to the effect of recessions and recoveries on the GDP measure in the denominator.  To see the underlying health care cost growth more clearly, Roehrig calculates what the GDP would have been during each time period if the economy was at a full employment state, called the “potential GDP.”  He then calculates health care cost as a percentage of potential GDP.  This creates a nice, steady upward ramp from 6% to almost 17%.

Then, using the potential GDP as a base, Roehrig created a great graphic showing how fast hospital, physician, prescription drug and other cost components grew in excess of the potential GDP.  In his blog post, Roehrig tells the story in graphs and words.  I created the following version of Roehrig’s graph to try to incorporate more of the story into the graph itself.

Roehrig concluded that the “policy responses to excess growth for hospital services, physician services, and prescription drugs seem to have been fairly successful.”  But, he referenced Tom Getzen, who warns against assuming that the recent lower growth rate is the “new normal.”  Rather, it may be temporarily low due to the lagged effects of the recent recession.  So, it may be too early to break out the champagne.

I really like showing such a long time horizon and breaking down health care costs into these five categories.  And, I am convinced that using potential GDP represents an improvement over the conventional GDP measure as a denominator for cost growth statistics.  But, I’ve never understood the popularity of expressing costs as a percentage of GDP in the first place.  In my opinion, it is more relevant to just show growth in real (inflation-adjusted) per capita costs, or the insurance industry variant of that, “per member per month” (PMPM) costs. Using GDP or potential GDP in the denominator seems to imply that, as our country gets more populous and richer, we should increase health care costs accordingly.  I agree with the idea that population growth should logically lead to increasing health care expenditures.  Expressing costs on a per capita basis handles that issue.  But, if we are prioritizing health care services as essential services, we would not necessarily need to spend that much more on health care if we got richer.

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Klar 4: Why is it important for CMS to Share Claims Data with ACOs?

Ron Klar, MD, MPH

Ron Klar, MD, MPH is a health care consultant with a long history of involvement in federal health care policy and health plan innovation. He published a recent series of three posts regarding the draft rules for the Medicare Shared Savings Program (MSSP) in the Health Affairs Blog, an influential forum for debating health policy issues. This is my last in a series of 4 posts describing areas of agreement and disagreement with Dr. Klar. (The others are available at post 1, post 2 and post 3)

In his third post to the Health Affairs Blog, Dr. Klar proposed to eliminate CMS sharing of claims data with providers. He argued that it is too delayed to serve any useful purpose to the ACO, either for clinical operations or analysis.  He also argued that supplying ACOs with claims data would be expensive for both CMS and the ACOs that would need to create interfaces, databases and applications to receive and use the data.  He argued that it would distract providers from investment in electronic health records (EHR) and health information exchange (HIE).  Finally, he argued that it would violate the confidentiality of non-ACO providers who would be identified in the data.  Klar implies that all of the data needs of the ACO can be met with EHR data, augmented with HIE data.

I strongly disagree with this thinking.  The success of ACOs will require a transformation of the health care organization from one that reactively cares for individual patients to one that also proactively takes responsibility for a population of patients.  The analytics to support that transformation requires a comprehensive view of all the health care services received by the population.  Since patients are free to seek care from any provider participating in Medicare, only CMS can provide this comprehensive view of the data. An EHR may be richer and more up-to-date, but it lacks this comprehensive view.  An HIE might increase the completeness somewhat, but without data from the payer, it is not possible to know how much missing data might be beyond the reach on any particular HIE at any point in its development.  For the foreseeable future, EHR and HIE data are too inconsistently structured and too incomplete to give a true population-oriented measure of utilization.

In earlier demonstration projects of accountable care and care management, the participants complained that the data shared by CMS was not delivered in a useful and timely way.  So, not only must we keep the CMS claims data sharing in the ACO rules, we must also make sure that CMS does a better job of delivering it this time around.


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