NEW @ THCB PRESS: Surviving Workplace Wellness. Spring 2014. Al Lewis and Vik Khanna. e-book edition. # LIGHTHOUSE Healthcare. Illuminated.

Population Health

flying cadeucii

A long time ago, when I worked in Sweden’s Socialized health care system, there were no incentives to see more patients.

In the hospital and in the outpatient offices there were scheduled coffee breaks at 10 and at 3 o’clock, lunch was an hour, and everyone left on the dot at five. On-call work was reimbursed as time off. Any extra income would have been taxed at the prevailing marginal income tax rate of somewhere around 80%.

There was, in my view, a culture of giving less than you were able to, a lack of urgency, and a patient-unfriendly set of barriers. One example: most clinics took phone calls only for an hour or two in the morning.

After that, there was no patient access; no additions were made to providers’ schedules, even if some patients didn’t keep their appointments, not that there was a way to call and make a same-day cancellation.

As my father always said: “There must be a reward for working”.

But, high productivity can sometimes mean churning out patient visits without accomplishing much, or it can mean providing unnecessary care just to increase revenue. For example, some of my patients who spend winters in warmer climates come back with tall tales of excessive testing while away.

A recent Wall Street Journal article offers an interactive display of doctors who collect the highest Medicare payments. The difference between providers in the same specialties across the country makes interesting reading. It is hard to imagine that many individual doctors are billing Medicare more than $10,000,000 per year.

So it might make sense to insure against paying for excessive care by also demanding a certain level of quality.

But defining quality is fraught with scientific and ethical problems, since quality targets really aren’t, or shouldn’t be, the same for all of our patients.

Continue reading “How Should Doctors Get Paid? Hourly Wage, Piecework or Quality?”

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I was reading a medical home advocacy group’s upbeat approach to a recent JAMA study that had found scant benefit in the concept when, suddenly, we tumbled into Alice in Wonderland territory.

The press release from the leadership of the Patient-Centered Primary Care Collaborative (PCPCC) started out reasonably enough. The three-year study of medical practices had concluded that the patient-centered medical home (PCMH) contributed little to better quality of care, lower cost and reduced utilization. This was an “important contribution,” said the PCPCC, because it showed “refinement” of the concept that was still necessary.

That was just the set up, though, to this challenge from Marci Nielsen, chief executive officer of the group. “It is fair,” said Nielsen, “to question whether these pilot practices (studied) had yet transformed to be true medical homes.”

Where might one find these true medical homes? The answer turns out to be as elusive as a white rabbit. Formal recognition as a medical home via accreditation “can help serve as an important roadmap for practices to transform.” However, accreditation as a PCMH “is not necessarily synonymous with being one.” Conversely, you can be a “true PCMH” without having received any recognition at all!

But maybe the true medical home does not yet exist, since, “the evidence base” for the model “is still being developed.”

In Through the Looking Glass, Humpty Dumpty scornfully informs Alice: “When I use a word, it means just what I choose it to mean – neither more nor less.” And so we learn that a true medical home means just what the PCPCC says it does.

It’s confusing. If the truly transformational medical home lies in the future, why does the PCPCC chide the JAMA researchers in this “otherwise well-conducted study” for failing to “reference the recent PCPCC annual report which analyzed 13 peer-reviewed and 7 industry studies and found cost savings and utilization reductions in over 60 percent of the evaluations”?

Continue reading “The Medical Home’s Humpty Dumpty Defense”

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There’s that line about art, “good artists copy, great artists steal.” There’s some debate about if Picasso said it first, but most of us geeks know it from Steve Jobs.

Often, I see things from companies and industries outside of healthcare —processes, products, best practices —which inspire me. I like these little inspirations because they often aren’t rocket science, but nonetheless fuel some creative thoughts about their applicability in healthcare.

The other night, around 9:00 PM on a holiday Monday, I ordered some obscure aviation stuff from Amazon. I needed a new headset, a leg-mounted chart holder, a paper calculating tool called an E6B computer and a portable canister of oxygen.

I have Amazon Prime, their subscription service which provides expedited 2 day shipping, so I expected to see my stuff on Wednesday afternoon. I was blown away when there was an Amazon box outside my door by 9:00 AM the next morning, Tuesday.

A box showed up early, big deal, right?

Here’s what I think happened and why I’m so impressed. I had been browsing for some aviation stuff for a few days. Amazon clearly knows and tracks my window shopping. It’s how they suggest items when you come back to the site.

I believe they preemptively moved some of those obscure aviation items to the closest distribution center in anticipation of my purchase. In fact, Amazon was awarded a patent for exactly that process last week.

By predicting my purchasing behavior, Amazon was able to beat my expectations for delivery – a known threat to their model is the instant gratification of local retail – and get my package to me in 12 hours.

We’ve got a lot of data in healthcare. That’s to the lagging but persistent implementation of electronic medical records, doctors and health systems are beginning to apply some big data science to their patient populations. For instance, any credible EMR can tell a physician how many of her patients have asthma.

More advanced systems, including bolt on solutions can look at disease panels and cross sample against last visit date. Mr. Smith, we see it’s been a year since your last visit, how’s your arthritis? Can we schedule you and appointment with Dr. Jones?

Continue reading “Amazon.com as a Delivery Model for Population Health”

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At least two-thirds of the perpetrators and victims of gun violence are males under the age of 30. What else do they have in common? They live in neighborhoods with high crime rates and low family incomes, they knew each other before the violence broke out, they usually aren’t employed.

But there’s another commonality these young people share which isn’t often mentioned in discussions about gun violence and crime.

It turns out that the part of the brain that controls processing of information about impulse, desire, goals, self-interest, rules and risk develops latest and probably isn’t fully formed until the mid-20s or later. And while adolescents and young men understand the concepts of ‘good’ versus ‘bad’ as well as older adults, they tend to let peer pressures rather than expected outcomes guide their behavior when choosing between risks and rewards.

Take this neurological-behavioral profile of males between ages 15 to 30 and stick a gun in their hands. The brain research clearly demonstrates that kids and young adults walking around with guns understand the risks involved. Whether it’s the NSSF’s new Project ChildSafe, the NRA’s Eddie Eagle or the grassroots gun safety programs that have expanded since Sandy Hook, nobody’s telling the kids something they don’t already know.

So what can we do to mitigate what President Obama calls this ‘epidemic’ of gun violence when the population most at risk consciously chooses to ignore the risk? I suggest that we look at what communities have done to protect themselves from other kinds of epidemics that threatened public health in the past.

And the most effective method has been to quarantine, or isolate, the area or population where the threat is most extreme. It worked in 14th-century Italy, according to Boccaccio in The Decameron. Why wouldn’t it work now?

Last month the city of Springfield, Mass., recorded its 12th gun homicide. If the killing rate continues, the city might hit 15 shooting fatalities this year, a number it actually surpassed in 2010. This gives the city a homicide rate of 10.2 per 100,000 residents, nearly three times the national rate. Virtually all the violence takes place in two specific neighborhoods bounded by Interstate 291 and State Route 83, and all the victims are between 15 and 30 years old.

Continue reading “If Gun Violence is a Health Epidemic, Can We Quarantine It Like a Virus?”

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Having been supported by several small business grants from the National Cancer Institute to create online interventions for cancer patients, I have been learning gradually about commercialization models to get our work out to the public. I am dismayed about the major disconnect between eHealth entrepreneurs and eHealth intervention researchers (my personal reference group).

Last year I attended Stanford Medicine X and last week I did a demo of one of our web sites at Health 2.0 in Santa Clara. Both times, I was struck by the assumption in the IT developer and consumer community that giving people realtime feedback about their health will automatically result in major positive changes in behavior, not to mention cost savings for insurers.

The Connected Patient movement seems particularly naïve to me. Psychologists have been using self-monitoring, i.e. recording behaviors such as smoking, eating, and exercise, for at least 30 years to promote behavior change. First we used paper-and-pencil diaries, but researchers like Saul Schiffman quickly adapted the first handheld computers to prompt people to record their behaviors in realtime, greatly increasing the accuracy and power of self-monitoring.

As technology has advanced, so have our means of self-monitoring. Overall, however, the technology matters far less than the procedure itself. For most people, tracking their smoking, calories, mood, or steps does change unhealthy behaviors somewhat, for a limited period of time. A small group of highly educated, motivated people is more successful in using self-monitoring to make larger, more lasting changes.

I was reminded of this last year in a seminar on tracking at Stanford Medicine X, when a concierge physician from San Francisco and several of his patients talked about being empowered to change their health by using feedback from various types of sensors. One had paid out of pocket for a continuous blood glucose monitor since his insurance would not cover the costs to use it for his Type II diabetes.

Another doggedly demanded access to the data from his cardiac defibrillator. They believed their experiences heralded a sea change in health care in the United States. I am all for empowering patients with knowledge, tracking tools, and social support.

However, if knowledge and feedback was all it took to change unhealthy behaviors, psychologists would be superfluous in the world.

Continue reading “Healthcare’s Tech Disconnect: Why Aren’t We Building the Products Patients Really Need?”

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I am known in the disease management and wellness fields as a naysayer, critic, curmudgeon, and/or traitor…and those are only the nouns that are allowed to be blogged across state lines.  This is because I am driven not by wishful thinking but rather by data.  The data usually goes the wrong way, and all I do is write down what happened.  Then the vendors blame me for being negative — sort of like blaming the thermometer because the room is too hot — because they can’t execute a program.

However, the nonprofit Iowa Chronic Care Consortium (ICCC) apparently can execute a program.  They reduced total diabetes events by 6% in the rural counties they targeted.  This success supports a hypothesis that in rural (presumably underserved) areas, disease management fulfills a critical clinical gap:  it provides enough basic support that otherwise would not be provided even to those who actively seek it to reduce near-term complications and exacerbations.

This result will likely produce its own unanticipated consequence: because many people now believe (thanks, ironically, to some of my own past work) that disease management doesn’t produce savings, there will be widespread skepticism about the validity of this study.  Quite the opposite:  this “natural experiment” is as close to pristine as one could hope for in population health, for five reasons:

  1. There was no participation/self-selection bias because outcomes were measured on all Iowa Medicaid members.
  2. The program was offered in some Iowa counties but not others, so there was no eligibility or benefits design bias, Medicaid being a statewide program.
  3. The program encompassed only one chronic condition (diabetes) rather than all five common chronic conditions normally managed together (asthma, CAD, CHF, and COPD being the other four).   Since all five conditions were tracked concurrently, whatever confounders affected the event rate in one of those conditions should have affected all of them.   And event rates in the four other conditions did indeed move together in both the control and study counties.   Just not diabetes.
  4. The data was collected exactly the same manner by the same (unaffiliated) analysts using exactly the same database so there is no inter-rater reliability issue.
  5. Both groups contained hundreds of thousands of person-years and thousands of events.

As one who has reviewed another high-profile “natural experiment,” North Carolina Medicaid, and found that the financial outcomes were the reverse of what the state’s consultants originally claimed (incorrectly, as they later acknowledged by changing their answer), I can also say that natural experiments in population health don’t harbor some as-yet-unidentified confounder that causes the study population to outperform the control population.

Continue reading “Stop the Presses: A Disease Management Program Worked”

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Since 1973, when Jack Wennberg published his first paper describing geographic variations in health care, researchers have argued about both the magnitude and the causes of variation.  The argument gained greater policy relevance as U.S. health care spending reached 18 percent of GDP and as evidence mounted, largely from researchers at Dartmouth, that higher spending regions were failing to achieve better outcomes.   The possibility of substantial savings not only helped to motivate reform but also raised the stakes in what had been largely an academic argument.   Some began to raise questions about the Dartmouth research.

Today, the prestigious Institute of Medicine released a committee report, led by Harvard’s Professor Joseph Newhouse and Provost Alan Garber, that weighs in on these issues.

The report, called for by the Affordable Care Act and entitled “Variation in Health Care Spending: Target Decision Making, Not Geography,” deserves a careful read. The committee of 19 distinguished academics and policy experts spent several years documenting the causes and consequences of regional variations and developing solid policy recommendations on what to do about them.  (Disclosure: We helped write a background study for the committee).

But for those trying to make health care better and more affordable, whether in Washington or in communities around the country, there are a few areas where the headlines are likely to gloss over important details in the report.

And we believe that the Committee risks throwing out the baby with the bathwater by appearing, through its choice of title, to turn its back on regional initiatives to improve both health and health care.

What the committee found

The report confirmed three core findings of Dartmouth’s research.

First, geographic variations in spending are substantial, pervasive and persistent over time — the variations are not just random noise. Second, adjusting for individuals’ age, sex, income, race, and health status attenuates these variations, but there’s still plenty that remain. Third, there is little or no correlation between spending and health care quality. The report also effectively identifies the puzzling empirical patterns that don’t fit conveniently into the Dartmouth framework, such as a lack of association between spending in commercial insurance and Medicare populations.

Continue reading “Making Sense of Geographic Variations in Health Care: the New IOM Report”

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You’d be forgiven if, after reading last month’s Health Affairs, you came to the conclusion that all manner of wellness programs simply will not work; in it, a spate of articles documented myriad failures to make patients healthier, save money, or both.

Which is a shame, because – let’s face it – we need wellness programs to work and, in theory, they should. So I’d rather we figure out how to make wellness work. It seems that a combination of behavioral economics, technology, and networking theory provide a framework for creating, implementing, and sustaining programs to do just that.

Let’s define what we’re talking about. “Wellness program” is an umbrella term for a wide variety of initiatives – from paying for smoking cessation, to smartphone apps to track how much you walk or how well you comply with your plan of care, and everything in between. The term is almost too broad to be useful, but let’s go with it for now.

When we say “Wellness programs don’t work,” the word work does a lot of, well, work. If a wellness program makes people healthier but doesn’t save lives, is it “working”? What if it saves money but doesn’t make people healthier?

Continue reading “Wellness Programs Aren’t Working. Three Ideas That Could Help.”

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“We spend far more on health care than other peer countries yet have worse outcomes. Why is U.S. health care so expensive?” I’m sure you’ve encountered similar statements, maybe even expressed it yourself. It occurs often, including by knowledgeable people and health-related institutions. However, it’s a fallacy because it confuses health care with population health.

Health care is a proper subset of population health. For example, longevity is determined by more than just health care. Using a specific recent estimate (Appendix Exhibit A6 – gated), an average 20-year old U.S. white male who did not graduate high school will live 10.5 fewer years than a similar man with a college degree. That’s over ten years of life related to educational attainment. Sure, there are many reasons for the difference, and health care or the lack of it is only one of them.

Continue reading “Health Care Shibboleth”

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Writing in the March 20 issue of JAMA, Drs. Douglas Noble and Lawrence Casalino say that supporters of Accountable Care Organizations (ACOs) are all muddled over “population health.”

This correspondent says the article is what is muddled and that the readers of JAMA deserve better.

According to the authors, after the Affordable Care Act launched the Medicare Accountable Care Organizations (ACOs), their stated purpose has morphed from Health-System Ver. 2.0 controlling the chronic care costs of their assigned patients to Health System Ver. 3.0 collaboratively addressing “population health” for an entire geography.

Between the here of “improving chronic care” and the there of “population health,” Drs Noble and Casalino believe ACOs are going to have to confront the additional burdens of preventive care, social services, public health, housing, education, poverty and nutrition. That makes the authors wonder if the term “population health” in the context of ACOs is unclear. If so, that lack of clarity could ultimately lead naive politicians, policymakers, academics and patients to be disappointed when ACOs start reporting outcomes that are limited to chronic conditions.

Continue reading “Accountable Care Organizations Can Change Everything, But Only If We Get the Definition Right”

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MASTHEAD


Matthew Holt
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Vikram Khanna
Editor-At-Large, Wellness

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Associate Editor

Michael Millenson
Contributing Editor










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