Unbiased Analysis of Today's Healthcare Issues

Why “value” is central to true health care reform

Written By: Jason Shafrin - Sep• 20•17

Morning Consult has an interesting piece from Mark Linthicum, the Director of Scientific Communication at the Innovation and Value Initiative (IVI) titled “Why Understanding Value Is Central to True Health Care Reform“.  In the piece he argues:

The true problem is that dollars are poured into a system without any clear understanding of how worthwhile one dollar spent is, relative to another. The remedy lies in making spending and coverage decisions based on the value – not just the price tag – of health care treatments and services.

Value-based spending is a simple notion that amounts to prioritizing spending on services where the benefits outweigh cost – spend less on recognized areas of low-value care, like unnecessary tests and procedures, and more on high-value areas like vaccines.

Implementing value-based spending in practice, however, is more complicated because health care system stakeholders each have disparate, and sometimes conflicting, definitions of value.

Interesting throughout. Do read the whole piece.

Tuesday Links

Written By: Jason Shafrin - Sep• 18•17

How doctor’s die

Written By: Jason Shafrin - Sep• 17•17

Although this Saturday Evening Post article comparing how physicians and patients prefer end of life treatment is from 2013, it is interesting throughout.

Years ago, Charlie, a highly respected orthopedist and a mentor of mine, found a lump in his stomach. He had a surgeon explore the area, and the diagnosis was pancreatic cancer. This surgeon was one of the best in the country. He had even invented a new procedure for this exact cancer that could triple a patient’s five-year-survival odds—from 5 percent to 15 percent—albeit with a poor quality of life. Charlie was uninterested. He went home the next day, closed his practice, and never set foot in a hospital again. He focused on spending time with family and feeling as good as possible. Several months later, he died at home. He got no chemotherapy, radiation, or surgical treatment. Medicare didn’t spend much on him.

Why did this renowned surgeon forego medical treatment that could have potentially extended his life?

Of course, doctors don’t want to die; they want to live. But they know enough about modern medicine to know its limits. And they know enough about death to know what all people fear most: dying in pain and dying alone. They’ve talked about this with their families. They want to be sure, when the time comes, that no heroic measures will happen—that they will never experience, during their last moments on earth, someone breaking their ribs in an attempt to resuscitate them with CPR (that’s what happens if CPR is done right).

So why don’t physicians treat patients like they would like to be treated?  Oftentimes, caregiver preferences may be to extend the patient’s life whereas patients may focus on improved quality of life.  Patients themselves may ask to “do everything” to save their life, but without a sense of what everything means.  In addition, incentives in the health care system favor more intervention.

Consider the case of a man named Jack who had documented do not resuscitate (DNR) order.  When the patient’s primary physician intervened to end the provision of life support, here is what happened.

One of the nurses, I later found out, even reported my unplugging of Jack to the authorities as a possible homicide. Nothing came of it, of course; Jack’s wishes had been spelled out explicitly, and he’d left the paperwork to prove it. But the prospect of a police investigation is terrifying for any physician. I could far more easily have left Jack on life support against his stated wishes, prolonging his life, and his suffering, a few more weeks. I would even have made a little more money, and Medicare would have ended up with an additional $500,000 bill. It’s no wonder many doctors err on the side of over-treatment.


HWR is up

Written By: Jason Shafrin - Sep• 14•17

Yesterday was an eventful day in health policy world with Medicare for All bill and Graham-Cassidy both being introduced almost simultaneously and a Health Affairs event on Understanding the Value of Medical Innovations … but despite this,Louise Norris was still managed to compile The Neverending Summer of Healthcare Legislation Edition of the Health Wonk Review at Colorado Health Insurance Insider.  Check it out!

Understanding The Value Of Innovations In Medicine

Written By: Jason Shafrin - Sep• 13•17

Today, there was an excellent briefing put on by Health Affairs at the National Press Club. The topic was “Understanding the Value of Innovations in Medicine” and the briefing contained two panel discussions (see agenda).  The first panel , “Many Stakeholders, Many Values: Measuring Value In A Diverse Healthcare” featured expert economists, epidemiologists, and patient advocates and was moderated by Sam Nussbaum, who also serves as the Chair of the Innovation and Value Initiative’s Strategic Advisory Panel.   Bios of all panelists are here.

  • Lou Garrison presented on the ISPOR value Framework.
  • Rena Conti discussed how well prices currently align  with value in the pharmaceutical market.
  • Darius Lakdawalla highlighted the importance of measuring value to the patients, including less well known components of value such as the value of hope, option value, and insurance value.
  • Jeroen Jansen focused on the need for open source, transparent models to measure value.
  • Anna Hyde discussed the need to incorporate the patient’s perspective into any value assessment.

The second panel on “What’s Next For Value-Based Reimbursement In Healthcare?” was lead by Alan Weil, the editor in chief of Health Affairs. The panelists included:

  • Peter Neumann – A Tufts Professor and Chair of the Second Panel on Cost Effectiveness in Health and Medicine
  • Alan Balch – CEO of the Patient Advocate Foundation and National Patient Advocate Foundation
  • Michael Sherman – The senior vice president and chief medical officer of Harvard Pilgrim Health Care.
  • Kavita Patel, a nonresident fellow at the Brookings Institution, was not able to make the meeting due to a last minute family emergency and Darius Lakdawalla sat in her place for the discussion.

You can view video of the whole panel discussion here and also view the slide presentations from the first panel here.  It was a very interesting morning and well worth the trip to DC.

Health care market concentration

Written By: Jason Shafrin - Sep• 13•17

One question is whether more physician concentration is a good thing.  On the one hand, larger practices could lead to more efficient care. On the other hand, larger practices could give providers more market power and could drive up prices.

A separate question is whether federal authorities could do anything about increased physician market concentration.  According to a paper by Capps, Dranove, and Oby (2017), the answer is no.

Using proprietary claims data from states collectively containing more than 12 percent of the US population, we found that 22 percent of physician markets were highly concentrated in 2013, according to federal merger guidelines. Most of the increases in physician practice size and market concentration resulted from numerous small transactions, rather than a few large transactions. Among highly concentrated markets that had increases large enough to raise antitrust concerns, only 28 percent experienced any individual acquisition that would have been presumed to be anticompetitive under federal merger guidelines. Furthermore, most acquisitions were below the dollar thresholds that would have required the parties to report the transaction to antitrust authorities. Under present mechanisms, federal authorities have only limited ability to counteract consolidation in most US physician markets.

Concentrations levels among physicians, however, are lower compared to other providers.  A paper by Fulton (2017) finds that

In 2016, 90 percent of Metropolitan Statistical Areas (MSAs) were highly concentrated for hospitals, 65 percent for specialist physicians, 39 percent for primary care physicians, and 57 percent for insurers.



The cost of cancer care: Examining four common cancers

Written By: Jason Shafrin - Sep• 11•17

An interesting study by Chen et al. (2017) examines the cost of cancer care among Medicare patients.  Using SEER-Medicare data of people diagnosed with cancer between 2007 and 2011, they found:

Over the year of diagnosis, mean per-patient annual Medicare spending varied substantially by cancer type: $35,849 for breast cancer, $26,295 for prostate cancer, $55,597 for lung cancer, and $63,063 for colorectal cancer. More advanced stage at diagnosis was associated with higher annual spending for breast, prostate, and colorectal cancer…Over the year of death, mean per-patient annual spending was more consistent across cancer types: Breast cancer patients had an average of $61,429 in annual spending, compared to prostate ($62,351), lung ($59,912), and colorectal ($72,883).

In what care setting did the costs occur?

Over the year of diagnosis, inpatient services accounted for 50 percent of lung and 58 percent of colorectal cancer spending but only 22 percent of breast and 25 percent of prostate cancer spending (Figure 4). Outpatient costs were responsible for the majority of initial spending among patients newly diagnosed with breast and prostate cancer (66 percent for both).

Note that most injectable cancer drugs are included in the outpatient spending category.


The cost of quality measurement

Written By: Jason Shafrin - Sep• 10•17

An interesting editorial in JAMA by Schuster, Onorato and Meltzer (2017) makes the following point:

So how should quality measures be prioritized? Many factors are currently considered, including a measure’s expected effect on patients and health care, potential for promoting improvement, scientific underpinnings, usability, and feasibility. But there is a major omission from this list: the cost of each measure. The cost of specific measures has received limited attention in discussions about global costs of quality measurement and is not formally considered when evaluating and selecting measures, in no small part because that cost is usually unknown. Without understanding the cost of a specific measure, assessing its value cannot be fully determined.

The development of quality measures I believe should be independent of costs.  The reason is that the cost of implementation is likely very heterogeneous.  For a single practice physician to start collecting a wider range of MIPS quality measures is likely very onerous; to implement these same measures as part of a large, regional integrated delivery network is likely much lower (likely not in total but on on a per patient basis or as a share of total cost).  Thus having quality measures available and certified is likely useful to some but not all physician practices.

The point is that the implementation of quality measures or the formation of mandatory quality metric sets should clearly be based on not only the importance of the measure, but also the cost of data collection (among other factors).


Friday Links

Written By: Jason Shafrin - Sep• 07•17

Identifying high quality providers in the presence of heterogeneous preferences

Written By: Jason Shafrin - Sep• 07•17

Why is it so difficult for health care payers to identify a “best” provider?  A paper by Gutacker and Street (2017) explains:

There are two key elements that complicate assessment of how well public sector organisations are doing their job (Besley & Ghatak, 2003; Dixit, 2002). First, they lack a single overarching objective against which performance can be assessed. Instead, they pursue multiple objectives, and this requires performance measurement along a range of performance dimensions. These objectives may conflict, such that higher performance along one dimension may come at the expense of performance along another. Second, they typically serve several stakeholders, including those using services, tax-payers, regulatory bodies, and politicians. The values that stakeholders attach to objectives are often not known and unlikely to be identical.

This is a fundamental issue whenever decisions are made by a centralized authority rather than by individuals.  One solution is simply to weight all the objectives and create a composite score.  Then providers can be compared based on this composite.  However, these weights may not reflect individual stakeholder preferences.

The solutions that the authors propose is a multidimensional performance assessment framework.  In theory, this approach is simple.  To take a concrete example from the paper, assume that stakeholders care about hospital length of stay, waiting times, readmission rates, and patient reported health status after treatment.  If a hospital is superior along all dimensions, then clearly that hospital is better than one that ranks worse on all these dimension.  Hospitals that are better than other hospitals along all dimensions are dominant, those worse on all dimensions are domintated, and those with mixed differences are considered non comparable.

A complicating factor is what is the magnitude needed for a hospital to be considered “better” along a given dimension.  Clearly, if Hospital A has 25% fewer readmissions than Hospital B, clearly Hospital A is the higher quality provider along this dimension.  However, if Hospital A has only 0.01% fewer readmissions than Hospital B, then in practice they are likely the same quality.

One way to determine if a hospital is dominated is simply do univariate statistical tests (e.g., t-test, Chi-squared) along each dimension and then only consider a hospital as dominant (or dominated) if the statistical test rejects the null hypothesis along all dimensions.  The novel approach proposed by Gutacker and Street is that they conduct “multivariate hypothesis tests of parameters of interest that take into account the correlation between dimensions and achieve correct coverage probabilities.”

The clear benefit of this approach is that hospitals considered dominant are clearly the best and those considered dominant are clearly the worst.  The drawback of this approach is that the majority of hospitals will be in the non-comparable group.  In the Gutacker and Street example of hospitals in England as long as the dimensions 5 of the 252 hospitals (2%) were considered dominant and 8 of the 252 (3%) were considered dominated at the 90% confidence level.  Thus, 95% of hospitals were non-comparable.  Even if we decrease the confidence level to 50%, 24 out of 252 (10%) were dominated and 30 out of 252 (12%) were dominated, meaning that 78% of hospitals were non-comparable.  This is better than the univariate approach that doesn’t take into measure correlations were by only 99% and 95% of hospitals were not comparable at the 90% and 50% confidence levels respectively.  Further, the approach assumes that all the quality dimensions of interest to all stakeholders are included in the analysis.  As the number of quality metrics increases, the likelihood a hospital is dominant (or dominated) will fall and the method becomes less informative.

Nevertheless, given the different stakeholder preferences, using a multidimensional performance assessment framework is a potentially appealing approach.