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.

Meals high in saturated fat (SAFA), as opposed to monounsaturated fat (MUFA), may impair artery function. That was the finding of a small study published in the December issue of Diabetes Care:

Differential Effects Of Two Different Isoenergetic Meals One Rich In Saturated And One Rich In Monounsaturated Fat On Endothelial Function In Subjects With Type 2 Diabetes Mellitus, Diabetes Care, December 2008

After 33 participants with type 2 diabetes consumed either a meal high in saturated fat (butter), or high in monounsaturated fat (olive oil), (meals had the same number of calories) their artery function was assessed.

Flow-mediated dilation (FMD):

• Declined by 16.7% after the saturated-fat-rich meal
  
• Increased by 5.2% after the monounsaturated-fat-rich meal

FMD measures the relaxation of an artery due to increased blood flow. FMD is a marker for heart disease risk. As FMD decreases, blood flow is impaired.

The authors concluded:

"Consumption of an SAFA-rich meal is harmful for the endothelium, while a MUFA-rich meal does not impair endothelial function in subjects with type 2 diabetes."

________

"I  think I need to go back to the hospital....."

The Disease Management Care Blog remembers her very well. She had come to the United States after escaping on foot from post-war Hungary.  She liked to have sliced pears and cognac for lunch. As her health began to fail, her stubbornness began to exceed her self-sufficiency, leading to multiple hospitalizations. One time, despite making sure she had a post-discharge outpatient appointment, a fresh pile of prescriptions and home visiting nurse services, her illness flared and she had to come back to hospital.

She was not only my patient, but she represented one of Medicares dreaded readmission statistics.

By now, DMCB readers know that CMS, buoyed by its value-based purchasing program, has targeted readmissions by reducing payment levels to hospitals that fall outside the expected norm. Threatened by the loss of income, it’s assumed that hospitals will respond by developing higher quality discharge planning and care programs that keep patients from having to come back.

An important part of reducing readmissions is to identify those patients that are at greatest risk. That would help on two levels:

1) if a hospital had more than its fair share of patients at risk, it could argue that an increased number of readmissions is the result of a sicker patient population and not quality of care. As a result, the hospital could be held "harmless;

2) hospitals would be able to focus extra care resources on those patients who are spotted early as likely to come back, thereby reducing the readmission rate.

In other words, patients like the lady from Hungary would not necessarily lead to a cut in hospital payment rates and, for example, she could be proactively given extra care, such as a doctor appointment within 48 hours, a week’s supply of free medications and twice a day home nurse visits.

Which is why this just published JAMA article "Risk Prediction Models for Hospital Readmission" by Devan Kansagara, Honora Englander, Amanda Salanitro David Kagen, Cecelia Theobald, Michele Freeman and Sunil Kripalani is important.  The authors set out to see what the evidence-based published scientific literature had to say about predicting readmissions.   They filtered thousands of erences, reviewed 286 publications found 30 rigorous studies that described 26 models. 

To the DMCBs delight, the authors applied a “c statistic” to the 30 publications to assess a wide variety of retrospective and concurrent prediction methodologies using a host of data inputs such as age, gender and past diagnoses. According to this article, the c (or "concordance") statistic measures how well a test can predict the presence or absence of a "condition" which, in this case, was being readmitted to the hospital.  One way to think of this is the likelihood of correctly identifying a condition when there are two people, one with it and one without it.  If the likelihood is 50%, thats no better than random guessing.  If its 100%, thats perfect.  By the way,if this sounds a lot like the area under the receiver operator curve, youre right.

And what did the all-seeing "c statistic" say?  All of the published models had disappointingly similar levels of performance that ranged between the extremes of .52 to .83 with most in the .50 to .7 range. Whats more, only one study examined the most important question of all: is it possible to find patients with preventable readmissions?

What does the DMCB think?

1) This may be another area where national health policy has gotten out in front of the scientific evidence.  If we cant reliably assess or predict readmissions with sufficient accuracy, there is a distinct likelihood that statistical variation, not quality of care, will lead to some hospitals being victimized by CMS with lower payment rates. Whats more, if hospitals cant tell which patients are likely to come back, how are they supposed to target their expensive care management programs at those who are most likely to benefit?

2) There are undoubtedly some proprietary predictive models that havent been reported in the literature that claim to have higher levels of accuracy.  Yet, without the scrutiny of successful peer-reviewed publication, itd be difficult to believe that theyre really any better than the mainstream published range of .5 to .7.  The next time the DMCB runs into one of these outfits, its going to ask about the "c statistic" and if they havent published their results, why not.

3) Last but not least, while the hospital payment rates are being held hostage by CMS, its the doctors that are making the call on readmissions based on the best interest of their patient.  The c statistic suggests that that will be the most important determinant in the readmission rate.