Health for life

The cost-shifting ghost!

The Disease Management Care Blog continues to welcome blog posts from outside authors. This is another one, courtesy of Erik Tollefson, who works in the health policy field. He can be reached at erikDOTmDOTtollefsonATgmailDOTcom.

Of all the mythologies in the arcane world of health economics, cost shifting holds a hallowed place. First conjured up by commercial insurers in the 1970s to warn against catastrophic Medicaid cuts on hospitals’ financial positions, the rhetorical phantasm of cost shifting continues to rise from the dead to haunt the public sphere, particularly when politicians propose to orm public insurance reimbursement levels or undertake large-scale orms.

The theory of cost shifting is fairly straight forward: hospitals raise prices on private insurance customers when public payments are cut in order to make up for lost revenue. For all the importance afforded to cost shifting, however, there still remains a (highly) inconvenient truth: Numerous academic studies over the past 20 years have failed to find systematic evidence of its existence.

A recent National Bureau of Economic Research paper by Dranove, Garthwaite, and Ody examines the phenomenon of cost shifting in a new light. While scholars traditionally have examined hospitals’ pricing responses to planned changes in Medicare and Medicaid reimbursement levels, the financial crisis of 2007 provided a unique opportunity to analyze how they responded to a one-time loss in wealth. That crisis had a substantive impact on most hospitals; Not only did consumer demand for services decline, but many hospitals lost a substantial portion of their endowments due to the ensuing market turmoil. Dranove and his co-authors wanted to explore if hospitals that lost a significant proportion of their endowment would “cost shift” in order to make up for lost wealth, compared to hospitals that did not suffer similar losses.

What the authors found was disconcerting. Only a small sample of hospitals raised prices in the aftermath of the crisis. Many more responded with another strategy: cutting costs. Hospitals axed planned and ongoing capital expenditure projects (e.g.,  electronic health records) and shut down low-profit centers, including resource-intensive trauma and psychiatric centers.

Although the paper’s results cannot necessarily be generalized to all health care markets, it does suggest that hospitals can and will respond to financial downturns by cutting vital services.
 
Since the concept of cost shifting offends widely held notions of fairness, the further subsidization of baby boomers’ Medicare benefits in the purported era of austerity might not be politically palatable. The paper by Dranove et al, however, shows that a far worse scenario is possible if Medicare payment rates are slashed: cuts to costly but high value clinical programs. That’s ironic, because many of the benefits ascribed to the Affordable Care Act were predicated on increasing access to crucial medical services, particularly in underserved areas.

The only good news is that if hospitals react to changes in reimbursement levels and wealth loss by cutting important services, policy makers will be unable to summon forth the spirit of cost shifting.  While skeptical economists everywhere may rejoice, that will be small comfort to communities that find that their local hospitals are cutting basic services.

Measuring ROI

In yesterdays posting on Al Lewis book Why No One Believes the Numbers, the Disease Management Care Blog pointed out that the measurement of population health management (PHM) outcomes remains an inexact and still evolving science. While that can be a source of endless fascination for the DMCB, the inability of the industry to rustle up credible "return on investment" numbers has prompted some observers to condemn PHM as a waste of money.

The search for simple answers explains much of the appeal of this book.

According to author, one important solution is the "dummy year analysis" (DYA). This relies on repeated year-over-year measurements of utilization that use multiple comparison pairings of all patients with the condition of interest. When thats combined with a "plausibility" check list, Mr. Lewis says purchasers of the Patient Centered Medical Home (PCMH), disease management or wellness programs should be able to get a better fix on whether they saved any money. You can a sense of that perspective here.

The DMCB isnt too sure about that because a) other factors that have nothing to do with population health management can also impact utilization during and after the dummy years, making it difficult to assign an attributable ROI and b) entire health plan populations can likewise regress toward a regional or national mean.

The DMCB also sees three additional reasons why there may be less to this books methodology than meets the eye:

1. When employers, health plans, accountable care organizations or other buyers have a list of names that have been through a care program, they typically want to understand the outcomes for the individuals on that list. If thats the case, the challenge is to find an adequate comparator that portrays what would have happened in the absence of the care program. Multiple options for identifying a parallel comparator have been used in published science for decades. Thats difficult, imperfect, but not broken.  It remains an option.

2. While the book is replete with examples of "actuaries behaving badly," it is impossible to underestimate the influence of actuarial science and trending on premium rate setting, statutory accounting, and the regulation of insurance. As a result, if the actuaries say money is - or is not - being saved, health system leaders ignore their insights at their peril.

3. Isolating the impact of PCMH, disease management or wellness program out of all the other "noise" of a changing economy, evolving consumerism, benefit changes, electronic health record databases, medical advances, inflation and the news media is a function of an increasingly sophisticated and changing statistical sciences and computational technology. Its ironic, but one outcome has been a better description and measurement of the uncertainty surrounding a result.
 
To the authors credit, Why No One Believes the Numbers is not being promoted as the single best methodology that will lead PCMH, disease management and wellness programs to outcomes certainty. Rather, it is one option among many in asking whether a program had any financial impact.

Ultimately, theore, thats why the DMCB advises that measuring outcomes in PHM - absent an ironclad methodology - comes down to using multiple approaches to triangulate on the truth. After reading Why No One Believes the Numbers, some readers may choose it as one of those approaches.