By ANISH KOKA, MD

In 2009 a youthful Barack Obama addressed a joint session of congress on health care on a cold fall evening.  The country was still recovering from the great financial crisis, and the new President was now attempting to turn the nation’s eye to health care. As he had done many times before, Obama spoke powerfully of the tragedy of millions of citizens with ‘no access’ to health care, but spoke practically of the unsustainable and untenable economics of health care.

“Finally, our health care system is placing an unsustainable burden on taxpayers.  When health care costs grow at the rate they have, it puts greater pressure on programs like Medicare and Medicaid.  If we do nothing to slow these skyrocketing costs, we will eventually be spending more on Medicare and Medicaid than every other government program combined.  Put simply, our health care problem is our deficit problem.  Nothing else even comes close.  Nothing else.  (Applause.)

Now, these are the facts.  Nobody disputes them.  We know we must reform this system.  The question is how. “

These were the indisputable facts, he said.  ‘Nobody disputes them’.Continue reading…

By MERCEDITAS VILLANUEVA , MD

When The White House announced their Precision Medicine Initiative last year, they referred to precision medicine as “a new era of medicine,” signaling a shift in focus from a “one-size-fits-all-approach” to individualized care based on the specific characteristics that distinguish one patient from another. While there continues to be immense excitement about its game-changing impact in terms of early diagnoses and targeting specific treatment options, the advancements in technology, which underlie this approach, may not always yield the best medical results. In some cases, low cost approaches, based on sound clinical judgment, are still the better option. 

For example, tuberculosis (TB) is an infectious disease that continues to pose global burden with 9.6 million new cases and 1.5 million deaths reported in 2014 alone. The large toll is partly due to lack of effective treatments (particularly for drug-resistant cases) but also due to delays in diagnosis. One might think that precision medicine technology leading to improved diagnosis would be effective at minimizing the related death toll but we shouldn’t automatically assume that. It turns out that sometimes the latest technological advancements can be so sensitive that we detect organisms that are not causing disease.
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By ASHISH JHA, MD

Our recent paper on differences in outcomes for Medicare patients cared for by male and female physicians has created a stir.  While the paper has gotten broad coverage and mostly positive responses, there have also been quite a few critiques. There is no doubt that the study raises questions that need to be aired and discussed openly and honestly.  Its limitations, which are highlighted in the paper itself, are important.  Given the temptation we all feel to overgeneralize, we do best when we stick with the data.  It’s worth highlighting a few of the more common critiques that have been lobbed at the study to see whether they make sense and how we might move forward.  Hopefully by addressing these more surface-level critiques we can shift our focus to the important questions raised by this paper.

Correlation is not causation

We all know that correlation is not causation.  Its epidemiology 101.  People who carry matches are more likely to get lung cancer.  Going to bed with your shoes on is associated with higher likelihood of waking up with a headache.  No, matches don’t cause lung cancer any more than sleeping with your shoes on causes headaches. Correlation, not causation.

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By JIM PURCELL

“The essence of strategy is choosing what not to do.”  Michael Porter.

It is so often the case that organizations try to do things they should not do.  Call it irrational exuberance; getting out in front of the curve; or a bridge too far.  Hospital systems are examples of that.  Already large, complex organizations doing incredibly challenging things with billions of dollars flowing through their systemic blood vessels, they are understandably tempted to do more.  They always are.  That is the inevitable urge of active hospital board members and ambitious executives.  Do more; not do less.  After all, who arrives to such an exalted position to do less?

Their collective corporate eye is cast toward health insurers who have been called bloated and inefficient; dinosaurs; dim witted at best.  The President of the United States, no less, disparaged insurers while promoting the ACA, labelling them the “villains” of the healthcare system.  Speaker Pelosi called them “immoral.”  How difficult can it be to do health insurance better than the insurers have done it? Should be easy for people as smart as those who run complex healthcare delivery systems.

“Hospitals think this is a way to cut out the middle person, tailor care more closely and save a lot of extra money, but there’s a history to this and it generally doesn’t work,” said Howard Berliner, a visiting professor of health policy at NYU. “It sounds easy, but it winds up being incredibly complicated.”

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By SAURABH JHA, MD

Halfway through the “Bell Curve,” which is an analysis of differences in intelligence between races, I realized what had been bothering me about Charles Murray’s thesis. It wasn’t the accuracy of his analysis, which concerned me, too. It was that he analyzed. The truth, I used to believe, was always beautiful, whether it was what happened in the multiverse at T equals zero, or the historical counterfactual if Neville Chamberlain hadn’t signed the peace accord with Adolph Hitler. After reading Murray’s book, I realized that the truth can be irrelevant, ugly, and utterly useless. Even if the average intelligence of races was truly different, so what? Surely, civilized people must judge each other as individuals, regardless of the veracity of the statistical baggage of their ethnicities.

Murray was castigated, deservedly, for swallowing the bell curve uncritically. But his detractors missed one point. Murray wasn’t just wrong because he was factually wrong or for inquiring. In fact, it was worse, because Murray, it turned out, was wronger than wrong.

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By ASHISH JHA, MD

About a year ago, Yusuke Tsugawa – then a doctoral student in the Harvard health policy PhD program – and I were discussing the evidence around the quality of care delivered by female and male doctors. The data suggested that women practice medicine a little differently than men do. It appeared that practice patterns of female physicians were a little more evidence-based, sticking more closely to clinical guidelines.  There was also some evidence that patients reported better experience when their physician was a woman.  This is certainly important, but the evidence here was limited to a few specific settings or in subgroups of patients. And we had no idea whether these differences translated into what patients care the most about: better outcomes. We decided to tackle this question – do female physicians achieve different outcomes than male physicians. The result of that work is out today in JAMA Internal Medicine.

Our approach

First, we examined differences in patient outcomes for female and male physicians across all medical conditions. Then, we adjusted for patient and physician characteristics. Next, we threw in a hospital “fixed-effect” – a statistical technique that ensures that we only compare male and female physicians within the same hospital. Finally, we did a series of additional analyses to check if our results held across more specific conditions.

We found that female physicians had lower 30-day mortality rates compared to male physicians. Holding patient, physician, and hospital characteristics constant narrowed that gap a little, but not much. After throwing everything into the model that we could, we were still left with a difference of about 0.43 percentage points (see table), a modest but clinically important difference (more on this below).Continue reading…

By DAN ORENSTEIN

On December 7, 2016, the United States Senate approved the 21st Century Cures Act by an overwhelming margin. Having already passed the House with similarly broad bipartisan support, it now goes to President Obama for signature. Several years in the making, the Cures Act is broad and sweeping legislation that covers many topics, mostly on streamlining and accelerating the discovery of new drugs and medical devices. It includes provisions to improve mental health and substance abuse treatment, and to improve patient access to new therapies, among many other areas covered by the Act.

The Act also includes several provisions that will help accelerate the work of health information technology (HIT) companies and providers working to use healthcare data and information to improve outcomes, reduce variations in care, and better coordinate care delivery. These provisions establish programs and oversight to promote health information interoperability and prohibit information blocking practices.

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By LEONARD D’AVOLIO

Recently, a great op-ed published in The Wall Street Journal called “Turn Off the Computer and Listen to the Patient” brought a critical healthcare issue to the forefront of the national discussion. The physician authors, Caleb Gardner, MD and John Levinson, MD, describe the frustrations physicians experience with poor design, federal incentives, and the “one-size-fits-all rules for medical practice” implemented in today’s electronic medical records (EMRs).

From the start, the counter to any criticism of the EMR was that the collection of digital health data will finally make it possible to discover opportunities to improve the quality of care, prevent error, and steer resources to where they are needed most. This is, after all, the story of nearly every other industry post-digitization.

However, many organizations are learning the hard way that the business intelligence tools that were so successful in helping other industries learn from their quantified and reliable sales, inventory, and finance data can be limited in trying to make sense of healthcare’s unstructured, sparse, and often inaccurate clinical data.

Data warehouses and reporting tools — the foundation for understanding quantified and reliable sales, inventory, and finance data of other industries – are useful for required reporting of process measures for CMS, ACO, AQC, and who knows what mandates are next. However, it should be made clear that these multi-year, multi-million dollar investments are designed to address the concerns of fee-for-service care: what happened, to whom, and when. They will not begin to answer the questions most critical to value-based care: what is likely to happen, to whom, and what should be done about it.

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