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ImportanceBundled 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.ObjectiveTo investigate the association of hospital participation in BPCI-A with spending, and to characterize hospitals receiving financial bonuses vs penalties.Design, Setting, and ParticipantsDifference-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.ExposuresBPCI-A model years 1 and 2 (October 1, 2018, through December 31, 2019).Main Outcomes and MeasuresHospitals’ per-episode spending, CMS gross and net spending, and the incentive allocated to each hospital.ResultsThe 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 RelevanceAmong 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.

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 PARTICIPANTSThis 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 MEASURESPenalties in the prestratification vs poststratification schemes. RESULTSThe 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 RELEVANCEThis 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.

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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.