We often look to medical research to enhance the quality of life and survival of dialysis patients. But in striving toward those goals we must not ignore the role that financial incentives—or disincentives—play in the major for-profit industry that dialysis care has become. The principal revenue source for this industry in the United States is Medicare. How Medicare structures its payment rules matters.1
I have previously described how the present Medicare payment-per-treatment policy for dialysis services places the interests of providers in direct conflict with the well being and survival interests of patients.1,2 Profits are maximized through shorter treatments for more patients vs. longer, gentler and more frequent treatments for fewer patients. Consequently, American dialysis treatments are the shortest in the developed world. Idle capacity is reserved for new in-center patients rather than longer treatments for existing patients. More than thrice-weekly treatments upsets scheduling balance. It also creates a disincentive for providers to suggest that new patients consider home dialysis because utilizing already paid for idle in-center capacity is cheaper—even though home dialysis is less expensive in the long run. I proposed that this conflict and its divisive consequences could be resolved by replacing the present policy of payment per treatment by payment per treatment-hour.
But there is another conflict of interest between patients and their providers created by Medicare payment policy: A conflict of interest in each patient’s survival rate.
In the first three months of dialysis treatment (“Waiting Period”), Medicare pays nothing for patient care (unless home dialysis training has begun) and the patient and provider must rely on private insurance, if any. Beginning with the fourth month, and extending through the 33rd month (“Coordination of Benefits Period”), the patient’s private insurer is the primary payer and Medicare is the secondary payer. But commencing at month 34, the roles are reversed and Medicare becomes primary, and provider revenue for caring for the patient plummets.
Medicare, as primary payer, pays only about $249 per treatment (subject to some patient and facility adjustments) while private insurers as primary payers pay much more—up to four times or more the Medicare amount. Thus, the ratio of the number of privately insured patients to total patients is the single most important metric determining profit or loss for a provider. And this metric is a direct consequence of patient dialysis vintage—privately insured patients with a vintage of less than 34 months are enormously profitable. But when vintage reaches 34 months and Medicare becomes the primary payer, each further treatment results in an inescapable and continuing financial loss until the patient leaves the center via transplant, transfer, or death.
It is instructive to explore 2015 data for the U.S. dialysis duopoly —DaVita and Fresenius—and the distribution by payer of treatments, associated revenue, and resulting operating income (profit).a.
At DaVita, only one out of nine patients is profitable; the remaining eight lose money and will continue to lose money so long as these patients remain in their care. DaVita, alone, publishes treatments and operating income data. But Fresenius revenue distribution suggests about one out of six patients is privately insured and hence profitable. Differences between the two companies may reflect greater emphasis on home hemodialysis by DaVita with its likely survival advantage. They may also reflect different corporate resource allocation decisions, as we will describe.
Providers must negotiate rates with private insurers high enough to not only account for all their profits, but also cover all of their Medicare and other government insurance losses. They must never forget that profitable patients will become unprofitable as each passes a vintage of 33 months.
This is the financial dilemma facing every dialysis provider—including not-for-profits. Its impact is largely hidden at the point of care because of the strong bonds typically forged between patients and their caregivers—vintage is largely ignored. But senior managers charged with responsibility for asset allocation—arguably their most important task—cannot ignore it. In DaVita’s words:
“Within the U.S. dialysis and related lab services operating segment, the company considers each of its dialysis centers to constitute an individual business for which discrete financial information is available. However, since these dialysis centers have similar operating and economic characteristics, and the allocation of resources and significant investment decisions concerning these businesses are highly centralized and the benefits broadly distributed, the company has aggregated these centers and deemed them to constitute a single reporting unit.”3
Imagine, just for a moment, that your task is to make these “significant investment decisions.” The primary financial criterion on which to base allocation of resources is easily inferred from the table: maximize the ratio of privately insured patients to total patients.
Setting, age/comorbidity. The first decision set to consider is opening, acquiring, expanding, selling, downsizing or closing dialysis centers. Clearly, favoring locations with highest employment rates (and highest employer private insurance) is mandated while avoiding the lowest. Thus, avoid inter-cities and rural areas—anywhere with higher unemployment.
Patient age is tricky—younger patients are more likely to be employed and have private insurance, but are also more likely to survive beyond month 33. Avoid areas with a high proportion of Asian patients—their 5-year survival is 40% greater than whites (blacks survive 19% longer and Hispanics 21%).
Favor areas with the highest diabetes rates while avoiding those where glomerulonephritis is unusually prevalent (patients whose kidney fail from GN survive 45% longer than diabetics). 4 Since most provider growth is by center acquisition rather than startup, this analysis can often focus on actual target center patient profiles, not just local demographics.
Physical Plant. The second decision set would be where to invest in process improvements in your centers. Suppose you were presented with two such opportunities. Your first option is new technology that promises to halve the excess mortality experienced in the first months of dialysis.
The second promises to extend—even double—long-term survival from its present 3-1/2 -year average. You quickly choose the first process investment and regretfully reject the second. Why? Both benefit patients, but the first would increase profits and the second would increase losses.
Do you find it disquieting to be asked to consider these decisions, given the criterion you are expected to use to make them? I know of no other government in the world that has adopted a dialysis payment policy with such callous implications. It is a policy—regardless of intent—that directly incentivizes death.
A new change
The solution? Get rid of the noxious “Coordination of Benefits “ policy. Replace it with one that is independent of dialysis vintage and hence independent of patient race, ethnicity, geography or ESRD cause. Then, providers will be allowed to manage each patient according to their clinical needs—not their impact on next quarter’s financial statement—or on the very survival of their organization.
This Medicare change—together with replacing payment per treatment with payment per treatment hour—represent important opportunities to materially brighten the lives and extend the survival of dialysis patients. They won’t need to just wait for future breakthroughs in medical science.
- Hodge M. Quality will improve if we pay for dialysis based on time. Nephrology News & Issues. 2016;30(11):21-26
- Hodge M. From “Adequate” to optimal dialysis – by changing one word, Am J Kidney Dis. 2017;69(3):334-336
- DaVita Healthcare Partners Inc., Form 10-Q, Jun 30, 2016, United States Securities and Exchange Commission
- United States Renal Data System. 2015 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2015 2(6) Table 1.1