Differential Hospital Participation in Bundled Payments in Communities with Higher Shares of Marginalized Populations.

BACKGROUND: Medicare's voluntary bundled payment programs have demonstrated generally favorable results. However, it remains unknown whether uneven hospital participation in these programs in communities with greater shares of minorities and patients of low socioeconomic status results in disparate access to practice redesign innovations. OBJECTIVE: Examine whether communities with higher proportions of marginalized individuals were less likely to be served by a hospital participating in Bundled Payments for Care Improvement Advanced (BPCI-Advanced). DESIGN: Cross-sectional study using ordinary least squares regression controlling for patient and community factors. PARTICIPANTS: Medicare fee-for-service patients enrolled from 2015-2017 (pre-BPCI-Advanced) and residing in 2,058 local communities nationwide defined by Hospital Service Areas (HSAs). Each community's share of marginalized patients was calculated separately for each of the share of beneficiaries of Black race, Hispanic ethnicity, or dual eligibility for Medicare and Medicaid. MAIN MEASURES: Dichotomous variable indicating whether a given community had at least one hospital that ever participated in BPCI-Advanced from 2018-2022. KEY RESULTS: Communities with higher shares of dual-eligible individuals were less likely to be served by a hospital participating in BPCI-Advanced than communities with the lowest quartile of dual-eligible individuals (Q4: -15.1 percentage points [pp] lower than Q1, 95% CI: -21.0 to -9.1, p < 0.001). There was no consistent significant relationship between community proportion of Black beneficiaries and likelihood of having a hospital participating in BPCI-Advanced. Communities with higher shares of Hispanic beneficiaries were more likely to have a hospital participating in BPCI-Advanced than those in the lowest quartile (Q4: 19.2 pp higher than Q1, 95% CI: 13.4 to 24.9, p < 0.001). CONCLUSIONS: Communities with greater shares of dual-eligible beneficiaries, but not racial or ethnic minorities, were less likely to be served by a hospital participating in BPCI-Advanced Policymakers should consider approaches to incentivize more socioeconomically uniform participation in voluntary bundled payments.

Spillover Effects of Medicare's Voluntary Bundled Payments for Joint Replacement Surgery to Patients Insured by Commercial Health Plans.

BACKGROUND: Under the Bundled Payments for Care Improvement (BPCI) program, bundled paymtents for lower-extremity joint replacement (LEJR) are associated with 2% to 4% cost savings with stable quality among Medicare fee-for-service beneficiaries. However, BPCI may prompt practice changes that benefit all patients, not just fee-for-service beneficiaries. OBJECTIVE: To examine the association between hospital participation in BPCI and LEJR outcomes for patients with commercial insurance or Medicare Advantage (MA). DESIGN: Quasi-experimental study using Health Care Cost Institute claims from 2011 to 2016. SETTING: LEJR at 281 BPCI hospitals and 562 non-BPCI hospitals. PATIENTS: 184 922 patients with MA or commercial insurance. MEASUREMENTS: Differential changes in LEJR outcomes at BPCI hospitals versus at non-BPCI hospitals matched on propensity score were evaluated using a difference-in-differences (DID) method. Secondary analyses evaluated associations by patient MA status and hospital characteristics. Primary outcomes were changes in 90-day total spending on LEJR episodes and 90-day readmissions; secondary outcomes were postacute spending and discharge to postacute care providers. RESULTS: Average episode spending decreased more at BPCI versus non-BPCI hospitals (change, -2.2% [95% CI, -3.6% to -0.71%]; P = 0.004), but differences in changes in 90-day readmissions were not significant (adjusted DID, -0.47 percentage point [CI, -1.0 to 0.06 percentage point]; P = 0.084). Participation in BPCI was also associated with differences in decreases in postacute spending and discharge to institutional postacute care providers. Decreases in episode spending were larger for hospitals with high baseline spending but did not vary by MA status. LIMITATION: Nonrandomized studies are subject to residual confounding and selection. CONCLUSION: Participation in BPCI was associated with modest spillovers in episode savings. Bundled payments may prompt hospitals to implement broad care redesign that produces benefits regardless of insurance coverage. PRIMARY FUNDING SOURCE: Leonard Davis Institute of Health Economics at the University of Pennsylvania.

Overlap Between Medicare's Comprehensive Care for Joint Replacement Program and Accountable Care Organizations.

BACKGROUND: Overlap between Medicare's Comprehensive Care for Joint Replacement (CJR) model and accountable care organizations (ACOs) may result in positive or negative synergies. In this study, we describe the overlap between the programs at the beneficiary and hospital levels. METHODS: We conducted a retrospective study of patient and hospital characteristics using data from 2016 Medicare claims, the US Census Bureau, the American Hospital Association annual survey, Hospital Compare, and the Centers for Medicare & Medicaid Services Improving Medicare Post-Acute Care Transformation file. On the beneficiary level, we conducted 2 comparisons: (1) among patients who received joint replacement at CJR hospitals, ACO patients (overlap) vs not (CJR-only) and 2) among patients who received joint replacement elsewhere, ACO patients (ACO-only) vs not (neither). On the hospital level, we compared hospitals in the top quartile of overlap rate (high overlap) vs those in the bottom 3 (low overlap). RESULTS: We studied 14,519 overlap, 38,972 CJR-only, 26,872 ACO-only, and 68,945 neither beneficiaries. Compared with CJR-only patients, the overlap group was less likely to be older than 85, of black race, of low socioeconomic status, and burdened with clinical complications. Similar results were observed when the ACO-only group was compared with the neither group. Compared with low overlap hospitals, high overlap ones were more likely to be of nonprofit and less likely to be of safety net. CONCLUSION: CJR-ACO overlap is associated with differences in beneficiary and hospital characteristics, which raises key issues for providers and policymakers.

The Impact of Medicare's Alternative Payment Models on the Value of Care.

Over the past decade, the Centers for Medicare and Medicaid Services (CMS) have led the nationwide shift toward value-based payment. A major strategy for achieving this goal has been to implement alternative payment models (APMs) that encourage high-value care by holding providers financially accountable for both the quality and the costs of care. In particular, the CMS has implemented and scaled up two types of APMs: population-based models that emphasize accountability for overall quality and costs for defined patient populations, and episode-based payment models that emphasize accountability for quality and costs for discrete care. Both APM types have been associated with modest reductions in Medicare spending without apparent compromises in quality. However, concerns about the unintended consequences of these APMs remain, and more work is needed in several important areas. Nonetheless, both APM types represent steps to build on along the path toward a higher-value national health care system.

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

BACKGROUND: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. OBJECTIVE: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients. DESIGN: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups. PARTICIPANTS: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries. MAIN MEASURES: Spending, utilization, and mortality. KEY RESULTS: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+). CONCLUSIONS: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Association Between the Implementation of a Population-Based Primary Care Payment System and Achievement on Quality Measures in Hawaii.

Importance: Hawaii Medical Service Association (HMSA), the Blue Cross Blue Shield of Hawaii, introduced Population-based Payments for Primary Care (3PC), a new capitation-based primary care payment system, in 2016. The effect of this system on quality measures has not been evaluated. Objective: To evaluate whether the 3PC system was associated with changes in quality, utilization, or spending in its first year. Design, Setting, and Participants: Observational study using HMSA claims and clinical registry data from January 1, 2012, to December 31, 2016, and a propensity-weighted difference-in-differences method to compare 77 225 HMSA members in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in the first wave of the 3PC and 222 233 members attributed to 312 PCPs and 14 physician organizations that continued in a fee-for-service model in 2016 but had 3PC start dates thereafter. Exposures: Participation in the 3PC system. Main Outcomes and Measures: The primary outcome was the change in a composite measure score reflecting the probability that a member achieved an eligible measure out of 13 pooled Healthcare Effectiveness Data and Information Set quality measures. Primary care visits and total cost of care were among 15 secondary outcomes. Results: In total, the study included 299 458 HMSA members (mean age, 42.1 years; 51.5% women) and 419 primary care physicians (mean age, 54.9 years; 34.8% women). The risk-standardized composite measure scores for 2012 to 2016 changed from 75.1% to 86.6% (+11.5 percentage points) in the 3PC group and 74.3% to 83.5% (+9.2 percentage points) in the non-3PC group (differential change, 2.3 percentage points [95% CI, 2.1 to 2.6 percentage points]; P < .001). Of 15 prespecified secondary end points for utilization and spending, 11 showed no significant difference. Compared with the non-3PC group, the 3PC system was associated with a significant reduction in the mean number of primary care visits (3.3 to 3.0 visits vs 3.3 to 3.1 visits; adjusted differential change, -3.9 percentage points [95% CI, -4.6 to -3.2 percentage points]; P < .001), but there was no significant difference in mean total cost of care ($3344 to $4087 vs $2977 to $3564; adjusted differential change, 1.0% [95% CI, -1.3% to 3.4%]; P = .39). Conclusions and Relevance: In its first year, the 3PC population-based primary care payment system in Hawaii was associated with small improvements in quality and a reduction in PCP visits but no significant difference in the total cost of care. Additional research is needed to assess longer-term outcomes as the program is more fully implemented and to determine whether results are generalizable to other health care markets.

Do Changes in Post-acute Care Use at Hospitals Participating in an Accountable Care Organization Spillover to All Medicare Beneficiaries?

BACKGROUND: While early evidence suggests that Medicare accountable care organizations (ACOs) may reduce post-acute care (PAC) utilization for attributed beneficiaries, whether these effects spill over to all beneficiaries admitted to hospitals participating in ACOs stray is unknown. OBJECTIVE: The objective of this study was to evaluate whether changes in PAC use and Medicare spending spill over to all beneficiaries admitted to hospitals participating in the Medicare Shared Savings Program (MSSP). DESIGN: Observational study using a difference-in-differences design comparing changes in PAC utilization and spending among beneficiaries admitted to ACO-participating hospitals before and after the start of the ACO contracts, compared to those admitted to non-ACO hospitals. SETTING: A total of 233 hospitals participate in MSSP ACOs and 3103 non-ACO hospitals. PARTICIPANTS: A national sample of 11,683,573 Medicare beneficiaries experiencing 26,503,086 hospital admissions from 2010 to 2013. EXPOSURE: Admission to a hospital participating in an MSSP ACO. MAIN MEASURES: The probability of discharge and Medicare payments to inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF), and home health agencies (HHA). KEY RESULTS: For beneficiaries admitted to hospitals that joined an ACO, the likelihood of being discharged to PAC did not change after the hospital joined the ACO compared with non-ACO hospitals over the same period (differential change in probability of discharge to any PAC was 0.000 (P = 0.89), SNF was 0.000 (P = 0.73), IRF was 0.000 (P = 0.96), and HHA was 0.001 (P = 0.57)). Payments reduced significantly for PAC overall (- $130.41, P = 0.03), but not for any individual PAC type alone. These results were consistent in samples that were conditional on discharge to any PAC, across conditions with high PAC use nationally, and among ACO-participating hospitals that also had a PAC participant. CONCLUSIONS: Hospital participation in an ACO did not result in spillovers in PAC utilization or payments to all beneficiaries, even when considering high PAC-use conditions and ACO hospitals that also have an ACO-participating PAC.

Principles to Inform Risk Adjustment Policy: Reflection From the Front Lines.

This Viewpoint discusses the benefits and drawbacks of current risk adjustment tools, outlines the pressures clinicians may face to use these tools, and proposes principles and policy solutions to ensure that risk adjustment is clinically meaningful and to minimize gaming and waste.

Hospital Consolidation: The Rise of Geographically Distant Mergers.

This Viewpoint discusses how and why cross-market hospital mergers are different than prototypical within-market mergers in their effects on patients and communities, why the trend may be accelerating, and future policy and research directions.

Association of Hospital Participation in a Medicare Bundled Payment Program With Volume and Case Mix of Lower Extremity Joint Replacement Episodes.

Importance: Medicare's Bundled Payments for Care Improvement (BPCI) initiative for lower extremity joint replacement (LEJR) surgery has been associated with a reduction in episode spending and stable-to-improved quality. However, BPCI may create unintended effects by prompting participating hospitals to increase the overall volume of episodes paid for by Medicare, which could potentially eliminate program-related savings or prompt them to shift case mix to lower-risk patients. Objective: To evaluate whether hospital BPCI participation for LEJR was associated with changes in overall volume and case mix. Design, Setting, and Participants: Observational study using Medicare claims data and a difference-in-differences method to compare 131 markets (hospital referral regions) with at least 1 BPCI participant hospital (n = 322) and 175 markets with no participating hospitals (n = 1340), accounting for 580 043 Medicare beneficiaries treated before (January 2011-September 2013) and 462 161 after (October 2013-December 2015) establishing the BPCI initiative. Hospital-level case-mix changes were assessed by comparing 265 participating hospitals with a 1:1 propensity-matched set of nonparticipating hospitals from non-BPCI markets. Exposures: Hospital BPCI participation. Main Outcomes and Measures: Changes in market-level LEJR volume in the before vs after BPCI periods and changes in hospital-level case mix based on demographic, socioeconomic, clinical, and utilization factors. Results: Among the 1 717 243 Medicare beneficiaries who underwent LEJR (mean age, 75 years; 64% women; and 95% nonblack race/ethnicity), BPCI participation was not significantly associated with a change in overall market-level volume. The mean quarterly market volume in non-BPCI markets increased 3.8% from 3.8 episodes per 1000 beneficiaries before BPCI to 3.9 episodes per 1000 beneficiaries after BPCI was launched. For BPCI markets, the mean quarterly market volume increased 4.4% from 3.6 episodes per 1000 beneficiaries before BPCI to 3.8 episodes per 1000 beneficiaries after BPCI was launched. The adjusted difference-in-differences estimate between the market types was 0.32% (95% CI, -0.06% to 0.69%; P = .10). Among 20 demographic, socioeconomic, clinical, and utilization factors, BPCI participation was associated with differential changes in hospital-level case mix for only 1 factor, prior skilled nursing facility use (adjusted difference-in-differences estimate, -0.53%; 95% CI, -0.96% to -0.10%; P = .01) in BPCI vs non-BPCI markets. Conclusions and Relevance: In this observational study of Medicare beneficiaries who underwent LEJR, hospital participation in Bundled Payments for Care Improvement was not associated with changes in market-level lower extremity joint replacement volume and largely was not associated with changes in hospital case mix. These findings may provide reassurance regarding 2 potential unintended effects associated with bundled payments for LEJR.

The Current State of Evidence on Bundled Payments.

A review of the evidence shows that bundled payments for surgical procedures can generate savings without adversely affecting patient outcomes. Less is known about the effect of bundled payments for chronic medical conditions, but early evidence suggests that cost and quality improvements may be small or non-existent. There is little evidence that bundles reduce access and equity, but continued monitoring is required.

Using Behavioral Economics to Design Physician Incentives That Deliver High-Value Care.

Behavioral economics provides insights about the development of effective incentives for physicians to deliver high-value care. It suggests that the structure and delivery of incentives can shape behavior, as can thoughtful design of the decision-making environment. This article discusses several principles of behavioral economics, including inertia, loss aversion, choice overload, and relative social ranking. Whereas these principles have been applied to motivate personal health decisions, retirement planning, and savings behavior, they have been largely ignored in the design of physician incentive programs. Applying these principles to physician incentives can improve their effectiveness through better alignment with performance goals. Anecdotal examples of successful incentive programs that apply behavioral economics principles are provided, even as the authors recognize that its application to the design of physician incentives is largely untested, and many outstanding questions exist. Application and rigorous evaluation of infrastructure changes and incentives are needed to design payment systems that incentivize high-quality, cost-conscious care.