Cost of Joint Replacement Using Bundled Payment Models

Navathe, Amol S.; Troxel, Andrea B.; Liao, Joshua M.; Nan, Nan; Zhu, Jingsan; Zhong, Wenjun; Emanuel, Ezekiel J.

doi:10.1001/jamainternmed.2016.8263

Cited by 278 publications

(201 citation statements)

References 16 publications

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“…This finding is consistent with recent work that points to post-acute care as a potential culprit for waste in the US system (Navathe et al 2017; Baicker and Chernew 2017; Newhouse and Garber 2013). Such care is a major contributing factor to residual geographic variation in healthcare spending among the over-65 population (Newhouse and Garber 2013; Newhouse, Garber, and Graham 2013), a result found for the under-65 population as well (Franzini et al 2014).…”

supporting

confidence: 92%

Uncovering waste in US healthcare

Doyle¹,

Graves

Gruber³

2017

Journal of Health Economics

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There is widespread agreement that the US healthcare system wastes as much as 5% of GDP, yet much less agreement on the source of the waste. This paper uses the effectively random assignment of patients to ambulance companies to generate comparisons across similar patients treated at different hospitals. We find that assignment to hospitals whose patients receive large amounts of care over the three months following a health emergency have only modestly better survival outcomes compared to hospitals whose patients receive less. Outcomes are related to different forms of spending. Patients assigned to hospitals with high levels of inpatient spending are more likely to survive to one year, while high levels of outpatient spending result in lower survival. In particular, we discovered that downstream spending at skilled nursing facilities (SNF) is a strong predictor of mortality. Our results highlight SNF admissions as a quality measure to complement the commonly used measure of hospital readmissions and suggest that in the search for waste in the US healthcare, post-acute SNF care is a prime candidate.

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supporting

confidence: 92%

Uncovering waste in US healthcare

Doyle¹,

Graves

Gruber³

2017

Journal of Health Economics

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“…Our study adds to prior work examining BPCI and the related Comprehensive Care for Joint Replacement (CJR) program. Prior single‐center studies of joint replacement under BPCI have suggested that savings accrue from reducing high‐cost institutional PAC care . Dummit et al found evidence of modest savings among BPCI participants compared to nonparticipants, as well as reduction in institutional PAC use and duration, and Finkelstein et al found similar patterns in CJR .…”

Section: Discussionmentioning

confidence: 97%

Post‐Acute Care After Joint Replacement in Medicare's Bundled Payments for Care Improvement Initiative

Maddox

Orav

Zheng

et al. 2019

J American Geriatrics Society

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IMPORTANCE Bundled payments, in which services provided around a care episode are linked together, are being tested under Medicare's Bundled Payments for Care Improvement (BPCI) program. Reducing post‐acute care (PAC) is critical under bundled payment, but little is known about whether this is done through provider selection or consolidation, and whether particular patterns of changes in PAC are associated with success under the program. OBJECTIVE To characterize patterns of change in PAC under lower‐extremity joint replacement episodes in BPCI. DESIGN Retrospective difference‐in‐differences study. SETTING US Medicare, 2013 to 2015. PARTICIPANTS A total of 264 US hospitals participating in BPCI for lower‐extremity joint replacement and matched controls. EXPOSURES Participation in BPCI. MEASUREMENTS Use and duration of institutional PAC (proportion discharged to a skilled nursing facility, an inpatient rehabilitation facility, and a long‐term care hospital), dispersion of PAC (proportion of discharges to commonly used providers), and quality of PAC (Star Ratings, readmission rates, length of stay, and nurse staffing); part A Medicare payments. RESULTS BPCI participants decreased the use and duration of institutional PAC compared to controls: overall institutional PAC declined 4.4% in BPCI hospitals vs 2.1% in non‐BPCI hospitals (difference = −2.2%; P = .033), and duration decreased by 1.6 days in BPCI hospitals compared to 0.0 days in non‐BPCI hospitals (difference in differences = −1.5 days; P < .001). However, BPCI participants did not change their PAC referral patterns to reduce dispersion or refer patients to higher‐quality PAC providers. Hospitals that were more successful in reducing Medicare payments started with higher payments and higher use of institutional PAC settings and demonstrated greater drops in use and duration of institutional PAC, but no differences in dispersion or referral to high‐quality providers. CONCLUSIONS AND RELEVANCE Reductions in spending under BPCI were driven by a shift from higher‐ to lower‐cost discharge settings, and by shortening the duration of institutional PAC. Hospitals that reduced payments the most had the highest spending at baseline. J Am Geriatr Soc 67:1027–1035, 2019.

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“…This result is partially driven by the significant upfront investments in care redesign and the inability to recover these costs for low‐volume hospitals. However, for commoditized procedures like LEJR, significant cost reductions can be obtained through interventions which do not require potentially expensive care redesign efforts such as reducing implant costs or avoiding higher cost postacute care . Our findings provide information that could enhance the tailoring of care management pathways to reduce costs for patients more likely to incur greater costs.…”

Section: Discussionmentioning

confidence: 84%

Identifying high‐cost episodes in lower extremity joint replacement

Philpot

Swanson

Inselman

et al. 2018

Health Services Research

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Objectives To evaluate the ability of claims‐based risk adjustment and incremental components of clinical data to identify 90‐day episode costs among lower extremity joint replacement (LEJR) patients according to the Centers for Medicare & Medicaid Services (CMS) Comprehensive Care for Joint Replacement (CJR) program provisions. Data Sources Medicare fee‐for‐service (FFS) data for qualifying CJR episodes in the United States, and FFS data linked with clinical data from CJR‐qualifying LEJR episodes performed at High Value Healthcare Collaborative (HVHC) and Mayo Clinic in 2013. HVHC and Mayo Clinic populations are subsets of the total FFS population to assess the additive value of additional pieces of clinical data in correctly assigning patients to cost groups. Study Design Multivariable logistic models identified high‐cost episodes. Data Collection/Extraction Methods Clinical data from participating health care systems merged with Medicare FFS data. Principal Findings Our three populations consisted of 363 621 patients in the CMS population, 4881 in the HVHC population, and 918 in the Mayo population. When modeling per CJR specifications, we observed low to moderate model performance (CMS C‐Stat = 0.714; HVHC C‐Stat = 0.628; Mayo C‐Stat = 0.587). Adding CMS‐HCC categories improved identification of patients in the top 20% of episode costs (CMS C‐Stat = 0.758, HVHC C‐Stat = 0.692, Mayo C‐Stat = 0.677). Clinical variables, particularly functional status in the population for which this was available (Mayo C‐Stat = 0.783), improved ability to identify patients within cost groups. Conclusions Policy makers could use these findings to improve payment adjustments for bundled LEJR procedures and in consideration of new data elements for reimbursement.

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Cost of Joint Replacement Using Bundled Payment Models

Cited by 278 publications

References 16 publications

Uncovering waste in US healthcare

Uncovering waste in US healthcare

Post‐Acute Care After Joint Replacement in Medicare's Bundled Payments for Care Improvement Initiative

Identifying high‐cost episodes in lower extremity joint replacement

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