Patients treated in for-profit dialysis facilities experienced 17 percent more instrumental variable risk-adjusted hospital days than in their nonprofit counterparts, or about 3 additional days per year. There was no statistically significant difference in hospital days among chain-affiliated and independent facilities. Adjustments for case mix, facility size, payer type, market factors and for referral bias in the choice of dialysis facility
were made in order to ensure that these results were not due to either observable risk factors or unobservable referral bias (i.e., sicker patients being referred to for-profit providers and thus artificially increasing hospital days).
Our main finding is consistent with our original hypothesis that patients treated in for-profit dialysis providers will have higher hospital days per year because the cost of any interventions that would prevent lengthy hospital admissions are greater than the financial rewards from avoiding missed treatment. In that context, prevention of lengthy hospital admissions is an example of an unprofitable activity and thus less likely to be adopted by for-profit providers.
A recent study has also provided results that are consistent with the same basic hypothesis that for-profit dialysis providers are more likely than their nonprofit counterparts to take profitable actions and shirk on unprofitable ones. Large for-profit dialysis chains were found to use higher epoetin dose adjustments and target higher hemoglobin levels, probably because the cost of achieving higher hemoglobin concentrations is less than the additional reimbursement for higher epoetin dose (Thamer et al. 2007
). However, alternative explanations exist. Even though the unit for comparing differences in outcomes was the dialysis provider, the frequency and duration of hospital admissions are also influenced by the nephrologists and other admitting physicians and by the admitting hospital. One could not rule out the hypothesis that the difference is due to the behavior of these other parties: for instance, revenue maximizing physicians would be more likely to admit patients and keep patients in hospital longer, because payments for hospital care of ESRD patients are derived on a fee-for-service basis, whereas routine maintenance dialysis care is capitated. This perverse incentive might be tempered if physicians' financial interests were more closely linked to the dialysis facility
but may be exaggerated if physicians only see a small number of patients (because of increased competition). We were unable to directly adjust for hospital and physician-specific effects (admitting hospital and physician identifiers are not included in the USRDS data). However, we partially corrected for such effects by controlling for observable facility and market characteristics that are expected to be correlated with physician incentives: chain affiliation (physicians are more likely to have a financial stake in independent facilities) and market competition (physician revenue from capitated outpatient treatments drops when competition for patients increases, raising the importance of revenue from services derived from hospital stays).
It is also possible that the differences identified here could be explained by relationships for-profit and nonprofit dialysis providers form with hospitals and physicians in their area. Specifically, our data have revealed that only patients treated in chain-affiliated nonprofit clinics had a lower number of hospital days. There were no significant differences across patients treated in independent clinics or for-profit chains (results not shown). In the time period considered here, there were four for-profit chains and one nonprofit chain. The nonprofit chain's business model, unlike its for-profit counterparts, relied on close relationships with academic centers and nephrologists in geographic proximity with its clinics. This relationship may affect the incentives of admitting physicians to admit and discharge patients from a hospital, and it can influence the difference in the number of hospital days in patients across the four for-profit and the one nonprofit chain.
Generally, our results suggest that financial incentives in the current reimbursement system inadequately reward efforts to reduce lengthy hospital stays, leading to potentially preventable differences in the number of hospital days between for-profit and nonprofit providers. A pay-for-performance system in which payments to providers depend on downstream outcomes could strengthen the providers' incentives to improve care or adopt business models that lower the number of hospital days per patient per year. For example, a facility's composite payment rate could be supplemented by a bonus payment when it outperforms its expected hospitalization rates. The monthly capitation rate paid to physicians could also be similarly modified.
Certainly, any modification to the current payment system would require adequate case mix adjustment to prevent cherry picking by dialysis providers. The current formula for case mix adjustment depends only on age and body size. A more comprehensive adjustment might include selected comorbid conditions with reasonably objective diagnostic criteria (e.g., diabetes). Audits may be needed as part of such a system in order to avoid “classification creep” (Pitches, Burl, and Fry-Smith 2003
Previous studies have examined the relation between profit status and mortality in dialysis facilities (Garg et al. 1999
; Irvin 2000
; Port, Wolfe, and Held 2000
; Devereaux et al. 2002
; Brooks et al. 2006
;), and among profit status, facility size, and intermediate outcomes such as dialysis adequacy and control of anemia (Frankenfield et al. 2000
). While some (Garg et al. 1999
; Port, Wolfe, and Held 2000
; Devereaux et al. 2002
;) reported 8–20 percent higher mortality in for-profit facilities, Irvin found no differences. These studies were criticized on the basis that their findings could be explained by referral bias: sicker patients referred to facilities of higher perceived quality. Brooks et al. (2006)
provided the most comprehensive examination to date: using a more complete set of risk adjustors and an instrumental variable approach to adjust for selection bias, these authors identified no statistically significant differences in mortality across for-profit and nonprofit providers. However, this could be due to the fact that instrumental variable methods may not have enough power to detect differences in mortality across groups (Greene 2002
Our approach is similar to that in Brooks et al. (2006)
, uses more recent data, and is based on a two-stage regression method validated in McClellan, McNeil, and Newhouse (1994)
. In contrast, we considered an alternative outcome—hospital days—that occurs more frequently than death and may be more modifiable. Using hospital days, the instrumental variable approach may be sensitive enough to detect significant differences across providers, whereas no difference was shown in mortality. A statistical test provided evidence that this approach could have partially corrected for referral bias, although the method is not fool proof: if unobservable risk factors are correlated with the instrument (i.e., travel distance to a dialysis facility) used to create the random patient cohorts, then the estimated effects may still be biased.
This study has several other important limitations. Claims data do not capture information on hospitalization for patients who have non-Medicare insurance, so the results we have observed may not be generalizable to patients with employer group health insurance or those below 65 years of age during the first 3 years of ESRD. Moreover, these results may not apply to hospital-affiliated dialysis facilities, which were excluded from the analysis. Furthermore, as discussed earlier, hospital admissions and discharges are not only influenced by dialysis providers and referring nephrologists but also by other physicians—generalists and specialists—and by hospital processes. For example, if dialysis facilities were more likely to be located closer to for-profit hospitals, then the difference in hospital outcomes between for-profit and nonprofit facilities could be due to the processes in for-profit hospitals. We were unable to adjust for differences in hospital characteristics (or other physician behavior) because claims data could not be linked to specific hospitals and physicians.
The results presented add to an established literature (Himmelstein et al. 1999
; Tu and Reschovsky 2002
; Rosenau and Linder 2003
; Schlesinger and Gray 2006
;) comparing outcomes among for-profit and nonprofit health care organizations, including dialysis providers, hospitals, and HMOs. Unlike some previously published reports, we are disinclined to interpret our results as proof that for-profits are inherently inferior. Rather, we view it as evidence that for-profit dialysis organizations may be more responsive to market forces and may be more likely than their nonprofit counterparts to take actions to improve outcomes where adequately rewarded for the cost of these actions.