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Two-Year Evaluation of Mandatory Bundled Payments for Joint Replacement

. 2019 Jan 17;380(3):252-262. doi: 10.1056/NEJMsa1809010. Epub 2019 Jan 2. Two-Year Evaluation of Mandatory Bundled Payments for Joint Replacement

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Two-Year Evaluation of Mandatory Bundled Payments for Joint Replacement

Michael L Barnett et al. N Engl J Med. 2019.

. 2019 Jan 17;380(3):252-262. doi: 10.1056/NEJMsa1809010. Epub 2019 Jan 2. Affiliation

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Background: In 2016, Medicare implemented Comprehensive Care for Joint Replacement (CJR), a national mandatory bundled-payment model for hip or knee replacement in randomly selected metropolitan statistical areas. Hospitals in such areas receive bonuses or pay penalties based on Medicare spending per hip- or knee-replacement episode (defined as the hospitalization plus 90 days after discharge).

Methods: We conducted difference-in-differences analyses using Medicare claims from 2015 through 2017, encompassing the first 2 years of bundled payments in the CJR program. We evaluated hip- or knee-replacement episodes in 75 metropolitan statistical areas randomly assigned to mandatory participation in the CJR program (bundled-payment metropolitan statistical areas, hereafter referred to as "treatment" areas) as compared with those in 121 control areas, before and after implementation of the CJR model. The primary outcomes were institutional spending per hip- or knee-replacement episode (i.e., Medicare payments to institutions, primarily to hospitals and post-acute care facilities), rates of postsurgical complications, and the percentage of "high-risk" patients (i.e., patients for whom there was an elevated risk of spending - a measure of patient selection). Analyses were adjusted for the hospital and characteristics of the patients and procedures.

Results: From 2015 through 2017, there were 280,161 hip- or knee-replacement procedures in 803 hospitals in treatment areas and 377,278 procedures in 962 hospitals in control areas. After the initiation of the CJR model, there were greater decreases in institutional spending per joint-replacement episode in treatment areas than in control areas (differential change [i.e., the between-group difference in the change from the period before the CJR model], -$812, or a -3.1% differential decrease relative to the treatment-group baseline; P<0.001). The differential reduction was driven largely by a 5.9% relative decrease in the percentage of episodes in which patients were discharged to post-acute care facilities. The CJR program did not have a significant differential effect on the composite rate of complications (P=0.67) or on the percentage of joint-replacement procedures performed in high-risk patients (P=0.81).

Conclusions: In the first 2 years of the CJR program, there was a modest reduction in spending per hip- or knee-replacement episode, without an increase in rates of complications. (Funded by the Commonwealth Fund and the National Institute on Aging of the National Institutes of Health.).

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Figures

Figure 1.. Adjusted Trends in Primary Outcomes,…

Figure 1.. Adjusted Trends in Primary Outcomes, 2015–2017

Adjusted estimates for each of the three…

Figure 1.. Adjusted Trends in Primary Outcomes, 2015–2017

Adjusted estimates for each of the three primary outcomes by quarter from 2015–2017 for LEJR episodes in the treatment group (red solid line) vs. the control group (blue dashed line). The left panel shows trends in institutional spending, the middle shows composite complication rates and the right shows the proportion of patients in the highest quartile of risk. The solid black vertical lines mark the pre-intervention and post-intervention periods, with the January-June 2016 “washout” period in the middle (see Methods). All estimates adjust for hospital and MSA random effects. Estimates for institutional spending and complication rate also adjust for patient and episode characteristics as described in the Methods and Appendix Methods. The proportion of patients in highest risk quartile outcome does not adjust for patient or episode characteristics because these characteristics are used to generate the patient risk score, which uses coefficients estimated from 2013–2014 data.

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