Physician Group Practices Accrued Large Bonuses Under Medicare's Bundled Payment Model, 2018-20.

The Bundled Payments for Care Improvement Advanced Model (BPCI-A), a voluntary Alternative Payment Model for Medicare, incentivizes hospitals and physician group practices to reduce spending for patient care episodes below preset target prices. The experience of physician groups in BPCI-A is not well understood. We found that physician groups earned $421 million in incentive payments during BPCI-A's first four performance periods (2018-20). Target prices were positively associated with bonuses, with a mean reconciliation payment of $139 per episode in the lowest decile of target prices and $2,775 in the highest decile. In the first year of the COVID-19 pandemic, mean bonuses increased from $815 per episode to $2,736 per episode. These findings suggest that further policy changes, such as improving target price accuracy and refining participation rules, will be important as the Centers for Medicare and Medicaid Services continues to expand BPCI-A and develop other bundled payment models.

Financial Burden of Health Care in the Privately Insured US Population.

This cross-sectional study examines how changes in privately insured families' contributions to insurance premiums and out-of-pocket spending have affected the financial burden of health care in recent decades.

Changes in Cardiovascular Spending, Care Utilization, and Clinical Outcomes Associated With Participation in Bundled Payments for Care Improvement - Advanced.

BACKGROUND: Bundled Payments for Care Improvement - Advanced (BPCI-A) is a Medicare initiative that aims to incentivize reductions in spending for episodes of care that start with a hospitalization and end 90 days after discharge. Cardiovascular disease, an important driver of Medicare spending, is one of the areas of focus BPCI-A. It is unknown whether BPCI-A is associated with spending reductions or quality improvements for the 3 cardiovascular medical events or 5 cardiovascular procedures in the model. METHODS: In this retrospective cohort study, we conducted difference-in-differences analyses using Medicare claims for patients discharged between January 1, 2017, and September 30, 2019, to assess differences between BPCI-A hospitals and matched nonparticipating control hospitals. Our primary outcomes were the differential changes in spending, before versus after implementation of BPCI-A, for cardiac medical and procedural conditions at BPCI-A hospitals compared with controls. Secondary outcomes included changes in patient complexity, care utilization, healthy days at home, readmissions, and mortality. RESULTS: Baseline spending for cardiac medical episodes at BPCI-A hospitals was $25 606. The differential change in spending for cardiac medical episodes at BPCI-A versus control hospitals was $16 (95% CI, -$228 to $261; P=0.90). Baseline spending for cardiac procedural episodes at BPCI-A hospitals was $37 961. The differential change in spending for cardiac procedural episodes was $171 (95% CI, -$429 to $772; P=0.58). There were minimal differential changes in physicians' care patterns such as the complexity of treated patients or in their care utilization. At BPCI-A versus control hospitals, there were no significant differential changes in rates of 90-day readmissions (differential change, 0.27% [95% CI, -0.25% to 0.80%] for medical episodes; differential change, 0.31% [95% CI, -0.98% to 1.60%] for procedural episodes) or mortality (differential change, -0.14% [95% CI, -0.50% to 0.23%] for medical episodes; differential change, -0.36% [95% CI, -1.25% to 0.54%] for procedural episodes). CONCLUSIONS: Participation in BPCI-A was not associated with spending reductions, changes in care utilization, or quality improvements for the cardiovascular medical events or procedures offered in the model.

Reconciliation Payments in the Bundled Payments for Care Improvement Advanced Program and Reductions in Clinical Spending Needed for CMS to Avoid Financial Losses.

This study examines the magnitude of reconciliation payments and clinical spending reductions necessary for the Centers for Medicare & Medicaid Services to break even in the first 4 performance periods of the BPCI-A (Bundled Payments for Care Improvement Advanced) program.

Equity Implications of Hospital Penalties During 4 Years of the Comprehensive Care for Joint Replacement Model, 2016 to 2019.

This cross-sectional study assesses the penalties against hospitals under the Comprehensive Care for Joint Replacement model mandated by Medicare, with particular attention to safety-net hospitals and those serving a high proportion of Black or Hispanic patients.

Association of Hospital Participation in Bundled Payments for Care Improvement Advanced With Medicare Spending and Hospital Incentive Payments.

Importance: Bundled Payments for Care Improvement Advanced (BPCI-A) is a Centers for Medicare & Medicaid Services (CMS) initiative that aims to produce financial savings by incentivizing decreases in clinical spending. Incentives consist of financial bonuses from CMS to hospitals or penalties paid by hospitals to CMS. Objective: To investigate the association of hospital participation in BPCI-A with spending, and to characterize hospitals receiving financial bonuses vs penalties. Design, Setting, and Participants: Difference-in-differences and cross-sectional analyses of 4 754 139 patient episodes using 2013-2019 US Medicare claims at 694 participating and 2852 nonparticipating hospitals merged with hospital and market characteristics. Exposures: BPCI-A model years 1 and 2 (October 1, 2018, through December 31, 2019). Main Outcomes and Measures: Hospitals' per-episode spending, CMS gross and net spending, and the incentive allocated to each hospital. Results: The study identified 694 participating hospitals. The analysis observed a -$175 change in mean per-episode spending (95% CI, -$378 to $28) and an aggregate spending change of -$75.1 million (95% CI, -$162.1 million to $12.0 million) across the 428 670 episodes in BPCI-A model years 1 and 2. However, CMS disbursed $354.3 million (95% CI, $212.0 million to $496.0 million) more in bonuses than it received in penalties. Hospital participation in BPCI-A was associated with a net loss to CMS of $279.2 million (95% CI, $135.0 million to $423.0 million). Hospitals in the lowest quartile of Medicaid days received a mean penalty of $0.41 million; (95% CI, $0.09 million to $0.72 million), while those in the highest quartile received a mean bonus of $1.57 million; (95% CI, $1.09 million to $2.08 million). Similar patterns were observed for hospitals across increasing quartiles of Disproportionate Share Hospital percentage and of patients from racial and ethnic minority groups. Conclusions and Relevance: Among US hospitals measured between 2013 and 2019, participation in BPCI-A was significantly associated with an increase in net CMS spending. Bonuses accrued disproportionately to hospitals providing care for marginalized communities.

Three-Year Impact Of Stratification In The Medicare Hospital Readmissions Reduction Program.

The Medicare Hospital Readmissions Reduction Program (HRRP) financially penalizes hospitals with high readmission rates. In fiscal year 2019 the program was changed to account for the association between social risk and high readmission rates. The new approach stratifies hospitals into five groups by hospitals' proportion of patients dually enrolled in Medicare and Medicaid, and it evaluates performance within each stratum instead of within the national cohort. Its impact on hospitals caring for vulnerable populations has not been studied. We calculated the change in average annual penalty percentage, before and after stratification, for safety-net hospitals, rural hospitals, and hospitals caring for a high share of Black and Hispanic or Latino patients. We found that stratification by proportion of dual enrollees was associated with a decrease in penalties by -0.09 percentage points at hospitals with the highest proportion of dual enrollees, -0.08 percentage points at rural hospitals, and -0.06 percentage points at hospitals with a large share of Black and Hispanic or Latino patients. Fully adjusted analyses suggest that these patterns were driven by penalty reductions at rural hospitals and hospitals disproportionately serving Black and Hispanic or Latino patients. Given the allocation of fewer penalties to these hospitals, we conclude that the stratification mandate was a modest step toward equity within the HRRP.

Association of Stratification by Proportion of Patients Dually Enrolled in Medicare and Medicaid With Financial Penalties in the Hospital-Acquired Condition Reduction Program.

Importance: The Hospital-Acquired Condition Reduction Program (HACRP) is a value-based payment program focused on safety events. Prior studies have found that the program disproportionately penalizes safety-net hospitals, which may perform more poorly because of unmeasured severity of illness rather than lower quality. A similar program, the Hospital Readmissions Reduction Program, stratifies hospitals into 5 peer groups for evaluation based on the proportion of their patients dually enrolled in Medicare and Medicaid, but the effect of stratification on the HACRP is unknown. Objective: To characterize the hospitals penalized by the HACRP and the distribution of financial penalties before and after stratification. Design, Setting, and Participants: This economic evaluation used publicly available data on HACRP performance and penalties merged with hospital characteristics and cost reports. A total of 3102 hospitals participating in the HACRP in fiscal year 2020 (covering data from July 1, 2016, to December 31, 2018) were studied. Exposures: Hospitals were divided into 5 groups based on the proportion of patients dually enrolled, and penalties were assigned to the lowest-performing quartile of hospitals in each group rather than the lowest-performing quartile overall. Main Outcomes and Measures: Penalties in the prestratification vs poststratification schemes. Results: The study identified 3102 hospitals evaluated by the HACRP. Safety-net hospitals received $111 333 384 in penalties before stratification compared with an estimated $79 087 744 after stratification-a savings of $32 245 640. Hospitals less likely to receive penalties after stratification included safety-net hospitals (33.6% penalized before stratification vs 24.8% after stratification, Δ = -8.8 percentage points [pp], P < .001), public hospitals (34.1% vs 30.5%, Δ = -3.6 pp, P = .003), hospitals in the West (26.8% vs 23.2%, Δ = -3.6 pp, P < .001), hospitals in Medicaid expansion states (27.3% vs 25.6%, Δ = -1.7 pp, P = .003), and hospitals caring for the most patients with disabilities (32.2% vs 28.3%, Δ = -3.9 pp, P < .001) and from racial/ethnic minority backgrounds (35.1% vs 31.5%, Δ = -3.6 pp, P < .001). In multivariate analyses, safety-net status and treating patients with highly medically complex conditions were associated with higher odds of moving from penalized to nonpenalized status. Conclusions and Relevance: This economic evaluation suggests that stratification of hospitals would be associated with a narrowing of disparities in penalties and a marked reduction in penalties for safety-net hospitals. Policy makers should consider adopting stratification for the HACRP.

Claims-based Frailty Indices: A Systematic Review.

OBJECTIVE: The objective of this study was to evaluate claims-based frailty indices (CFIs) used to assess frailty on a population-based level. BACKGROUND: Frailty is a key determinant of patient outcomes, independent of demographics and comorbidities. Measuring frailty in large populations has implications for targeted interventions, public reporting, and risk adjustment. Frailty indices based on administrative data in health insurance claims allow such population-level assessments of frailty. METHODS: We used PubMed to search for studies that: (1) were development or validation studies of a CFI that predicted frailty; and (2) used only diagnosis codes within administrative claims or health services claims. We evaluated the CFIs on 6 axes: databases used to build the CFIs; variables used to designate frailty; methods used to build the CFIs; model performance for predicting frailty; model relationship to clinical outcomes; and model limitations. RESULTS: We included 17 studies. They showed variation in the claims codes used to designate frailty, although themes like limited mobility and neurological and psychiatric impairment were common to most. C-statistics demonstrated an overall strong ability to predict patient frailty and adverse clinical outcomes. All CFIs demonstrated strong associations between frailty and poor outcomes. CONCLUSIONS: While each CFI has unique strengths and limitations, they also all had striking similarities. Some CFIs have been more broadly used and validated than others. The major takeaway from this review is that frailty is a clinically relevant, highly predictive syndrome that should be incorporated into clinical risk prediction when feasible.

Assessment of HF Outcomes Using a Claims-Based Frailty Index.

OBJECTIVES: This study used a claims-based frailty index to investigate outcomes of frail patients with heart failure (HF). BACKGROUND: Medicare value-based payment programs financially reward and penalize hospitals based on HF patients' outcomes. Although programs adjust risks for comorbidities, they do not adjust for frailty. Hospitals caring for high proportions of frail patients may be unfairly penalized. Understanding frail HF patients' outcomes may allow improved risk adjustment and more equitable assessment of health care systems. METHODS: Adapting a claims-based frailty index, the study assigned a frailty score to each adult in the National in-patient Sample (NIS) from 2012 through September 2015 with a primary diagnosis of HF and dichotomized frailty by using a cutoff value established in the general NIS population. Multivariate regression models were estimated, controlling for comorbidities and hospital characteristics, to investigate relationships between frailty and outcomes. RESULTS: Of 732,932 patients, 369,298 were frail. Frail patients were more likely than nonfrail patients to die during hospital stay (3.57% vs. 2.37%, respectively; adjusted odds ratio [aOR]: 1.67; 95% confidence interval [CI]: 1.61 to 1.72; p < 0.001); were less likely to be discharged to home (66.5% vs. 79.3%, respectively; aOR: 0.58; 95% CI: 0.57 to 0.58; p < 0.001); were hospitalized for more days (5.89 vs. 4.63 days, respectively; adjusted coefficient: 0.21 days; 95% CI: 0.21 to 0.22; p < 0.001); and incurred higher charges ($47,651 vs. $40,173, respectively; adjusted difference = $9,006; 95% CI: $8,596 to $9,416; p < 0.001). CONCLUSIONS: Frail patients with HF had significantly poorer outcomes than nonfrail patients after accounting for comorbidities. Clinicians should screen for frailty to identify high-risk patients who could benefit from targeted intervention. Policymakers should perform risk adjustments for frailty for more equitable quality measurement and financial incentive allocation.