Richard Hirth, PhD Marc Turenne, PhD Jack Wheeler, PhD Qing Pan, MS Joseph Messana, MD

1. Provider Monitoring and Pay-for-Performance When Multiple Providers Affect Outcomes:An Application to Renal Dialysis Richard Hirth, PhD Marc Turenne, PhD Jack Wheeler, PhD Qing Pan, MS Joseph Messana, MD University of Michigan Funding: Centers for Medicare and Medicaid Services contracts 500-2006-0048C & 500-00-0028

2. Background • Measuring and rewarding performance is a major focus of U.S. health policy • Monitoring/reporting • Inform quality assurance/improvement efforts • Inform consumers regarding choice of provider • Payment system design • P4P ties financial rewards to measured performance • Capitation/bundling presume a provider’s influence on resource use and manage associated risks

3. Key question in designing measurement/reward systems • Who should be measured/rewarded? • Practices/protocols of multiple types of providers can affect outcomes or efficiency • e.g., hospital/surgical team/surgeon • Ideally, measure and reward the provider(-s) most able to affect relevant outcomes • However, selection of locus for measurement or reward has not been empirically driven

4. Challenges • In principal, performance could be measured/rewarded at multiple levels, but difficult in practice • Identifying the “responsible” providers • Small n’s/excessive financial risk at some levels • Obtaining valid and clinically meaningful performance data

5. Renal Dialysis Example • Outcomes and resource utilization may reflect practices that vary across both dialysis facilities and nephrologists • However, measurement (e.g., Dialysis Facility Compare) and QI efforts (e.g., ESRD Networks) focus on the facility, as do P4P proposals • Implicitly attributes responsibility to the facility for the practices of non-employee physicians • Without incentives at the physician level, opportunities to improve care and efficiency may not be fully realized • Provides no guidance to patients regarding choice of physician

6. Appropriateness of Renal Dialysis for Studying Locus of Measurement • Patients have ongoing relationships with institutional provider and physician • Data availability (most patients covered by Medicare) • Demographic and clinical data available for case-mix adjustment • Discretionary resource use can be measured (e.g., drugs and labs) • Guidelines-based quality measures • Active policy context (current proposals to bundle more services into a PPS and develop P4P)

7. Research Question • How much of the variation in resource utilization and outcomes is attributable to the dialysis facility at which the patient is treated vs. the nephrologist responsible for outpatient, dialysis-related care?

8. Data • Outpatient institutional and physician/supplier claims for hemodialysis patients with Medicare as the primary payer in 2004 (1.9M patient-months) • Case-mix adjusters • Demographics, body size, conditions present at onset of ESRD (Medical Evidence Form) • Approximately 40 diagnoses reported on claims • Only recent claims used to define acute conditions (e.g., GI bleed)

9. Outcome Measures • Resource utilization • Medicare Allowable Charges (MAC) per dialysis session for services delivered in conjunction with dialysis • Injectable medications (primarily EPO, iron, vitamin D) • Lab tests billed by facility or ordered by nephrologist • Miscellaneous supplies • Societal perspective • MAC include Medicare payment and patient copay obligations • Clinical outcomes • Anemia management: Hematocrit (Hct) ≥ 33% • Adequacy of dialysis: Urea reduction ratio (URR) ≥ 65%

10. Providers • For each patient-month, used PINs to identify the dialysis facility billing the most sessions and the physician billing the Monthly Capitation Payment • 85% random sample of facility-physician pairs treating at least 5 patients selected for analysis (n=9994) • 24.6 patients and 151.2 patient-months per facility/physician pair

11. Methods • Variance Components Analysis • Yijk = β’Xijk + γj + ηk + εijk • Yijk is the outcome for patient i under the care of physician j in facility k • Xijk is a vector of characteristics of patient i with physician j in facility k • β is a vector of estimated regression coefficients for Xijk • γj is physician j’s random intercept. For all patients cared for by physician j, their outcomes increase/decrease by a common amount γj. γj is distributed N(0,ξ2). • ηk is facility k’s random intercept which is distributed N(0,ω2) • εijk is the residual after adjusting for all covariates and random effects for patient i with physician j in facility k, which is distributed N(0,σ2)

12. Results: Selected Characteristics

13. Extent of Crossover between Facilities and Physicians • To statistically distinguish facility and physician level variation, it is necessary that some facilities have multiple physicians or some physicians treat patients at multiple facilities • In nearly 2/3 of facilities, more than one physician billed MCPs for ≥ 5 patients (Figure 1) • More than half of physicians billed for ≥ 5 patients’ MCPs in multiple dialysis facilities (Figure 2)

14. Physicians per Facility

15. Facilities per physician

16. Outcomes varied at both the physician and facility levels • Each figure illustrates variation at the physician, facility, and patient levels as the mean for the outcome variable +/- 1 SD • In each case, outcomes varied more at the facility level than at the physician level • In each case, unexplained variation across patients exceeded the variation at either of the provider levels

17. Resource use per patient

18. % of patient months w/Hct≥33

19. % of months with URR≥65%

20. Conclusions • Because variation attributable to facilities is consistently larger, if monitoring/P4P targets only one type of provider, the facility is the appropriate locus • Nonetheless, existence of variation across physicians implies that quality reports, bundling and P4P may place facilities at risk for outcomes they only partially control • Cooperation between managers and physicians to optimize outcomes and resource utilization will become increasingly important under P4P programs and proposed reforms to pay prospectively for drugs and lab tests • Methods to align the incentives of dialysis facilities and nephrologists should be developed

21. Conclusions • Financial impact of variation in resource use is large • Facility-level SD of $19.45 per session translates to $155,600 for a facility performing 8000 HD treatments annually • If policy-makers and insurers can better understand sources of outcome variation, they will be better able to develop incentive systems • Likewise, such information can be used by providers to anticipate and manage financial risks and opportunities under prospective payment and P4P

22. Limitations • Random effects identify the statistical contribution of providers to observed outcomes, but cannot distinguish differences arising from discretionary practices from those arising from unobserved case mix differences • However, we control for a broader set of comorbidities than do the current, publicly reported dialysis facility outcomes data • MAC is a utilization based measure of cost; actual input costs are not available • Standardized Mortality Ratios (SMRs) are also reported at the dialysis facility level but were not studied here • The factors studied here are likely to be more sensitive to provider practices than SMRs