2018 John Charnley Award: Analysis of US Hip Replacement Bundled Payments: Physician-initiated Episodes Outperform Hospital-initiated Episodes : Clinical Orthopaedics and Related Research®

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2018 John Charnley Award: Analysis of US Hip Replacement Bundled Payments: Physician-initiated Episodes Outperform Hospital-initiated Episodes

Murphy, William S. AB; Siddiqi, Ahmed DO; Cheng, Tony MBA; Lin, Ben BA; Terry, David MBA; Talmo, Carl T. MD; Murphy, Stephen B. MD

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Clinical Orthopaedics and Related Research 477(2):p 271-280, February 2019. | DOI: 10.1097/CORR.0000000000000532
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Medical spending, including joint replacement, has consumed an increasing percentage of the gross domestic product in the United States over the past three decades. To reduce cost, the Centers for Medicare & Medicaid Services (CMS) launched an Innovation Center to investigate and study payment model alternatives to the traditional fee-for service model [9, 12-14, 16, 17, 20-22, 28, 29, 33]. In 2013, the CMS Innovation Center launched the Bundled Payment for Care Improvement (BPCI) program as one such alternative, in which physician group practices (PGPs) and hospital networks were eligible to apply as episode initiators, who assume comprehensive responsibility and financial risk during an episode of care. Preliminary BPCI reports for total joint arthroplasty (TJA) have shown promising improvement in cost-effectiveness with similar clinical outcomes [5, 14, 21, 29, 33].

As CMS transitions from fee-for-service payments to bundled payment programs such as BPCI, the optimal party to manage the bundled care episode is as of yet unknown. The BPCI program provided an opportunity to compare the payments and outcomes of bundles that were run by physicians (PGPs) and those that were run by hospitals to determine the optimal manager. Hospitals and PGPs have different structures and leadership; hospitals are generally led by individuals who are typically not directly involved in patient care, whereas PGPs are more likely to be managed by the treating physicians. In practice, hospital-initiated BPCI programs generally involved an increase in administrative overhead and control, including care navigators and standardized clinical pathways [16]. BPCI began accepting PGP-initiated episode initiators in the second enrollment period, a group that has grown to up to 50% of BPCI participants [4, 10, 11]. Because of the common concern that hospitals have less control over their patient populations than do PGPs, this study also aimed to control for the major confounding variables such as background payment trend, patient age, comorbidities, race, sex, and regional variation in procedure reimbursements to characterize if hospitals or PGPs are the optimal group to manage episodes of bundled care.

Study Questions

(1) Is BPCI associated with lower 90-day payments, readmissions, or mortality for elective THA? (2) Is there a difference in 90-day payments, readmissions, or mortality between episodes initiated by PGPs and episodes initiated by hospitals for elective THA? (3) Is BPCI associated with reduced total Elixhauser comorbidity index or age for elective THA?

Materials and Methods

Data Source

We performed a retrospective study to determine the clinical and economic results of hospital- and PGP-initiated episodes in BPCI. We obtained the CMS Limited Data Set (LDS) for fee-for-service claims from the CMS. This LDS database included all CMS expenditures except for Part B and durable medical equipment [7]. In the LDS data, each beneficiary is assigned an anonymous unique identifier that enables the attribution of CMS payments associated with each surgery episode through 90 days postoperatively. The data included all THAs performed on CMS-insured patients in the United States excluding Maryland from January 1, 2013, to March 31, 2016. Software applications (Archway Health Advisors, Watertown, MA, USA) were developed to identify each THA episode and aggregate associated expenditures from the LDS. Payments from the date of surgery through 90 days postoperatively were calculated, and each patient’s demographics, diagnosis codes, admission type (for example, elective versus emergency), readmissions, and mortality were identified for each episode. All incomplete data points (for example, episodes without an associated hospital, gender, race, or comorbidities) were excluded from the data analysis. Finally, the data analysis was restricted to elective THA without major comorbidity Diagnosis-Related Group (DRG) 470 episodes. After removing improperly coded episodes, there were 573,671 Medicare THA episodes over the study period. Of those THAs, 383,523 were elective procedures. Of those elective THAs, 371,586 were DRG 470, the final analysis population for this study. The total dollar amount analyzed for the THA episodes (elective hip DRG 470) from January 1, 2013, to March 31, 2016 was USD 7.1 billion.


We calculated all Part A CMS payments in the LDS data from the inpatient stay through the 90th day after discharge. These payments were subcategorized to inpatient stay, home health, skilled nursing, inpatient rehabilitation facility, long-term care hospital, outpatient, and readmission payments. Part B CMS payments were not included. We adjusted these payments for inflation to first quarter US 2013 dollars according to CMS BPCI trend factors for DRG 470. Furthermore, to account for geographic variations in payments, we normalized payments for each CMS core-based statistical area and each type of payment category according to Medicare final rules for each year and payment type [35].

Medical Comorbidities

We analyzed each episode for the comorbidity diagnosis codes provided by the CMS. The diagnosis codes were then categorized according to Elixhauser comorbidities using the Enhanced International Classification of Diseases (ICD), 9th Revision and Enhanced ICD, 10th Revision categorization and methodology from Quan et al. [31]. The Elixhauser comorbidity index is a validated measure for patient health status using administrative databases [18].

BPCI Attribution

Each episode was matched to the operating surgeon and anchor hospital. Because BPCI participants did not all enroll simultaneously, we obtained the list of BPCI participants and start dates from the CMS website [3]. For hospital-initiated BPCI procedures, we categorized the episodes by anchor hospital CMS certification number. For PGP-initiated programs, the participating practices were identified and then crossreferenced with the CarePrecise EPGH database to determine the operating surgeon (determined by National Provider ID [NPI]) associated with each PGP [2]. This analysis resulted in obtaining NPIs for 74 of 98 (76%) PGP programs in BPCI. The PGP group consisted of 74 private practices and 44,662 THAs. The hospital-directed group consisted of 222 hospitals and 42,922 THAs (Fig. 1). Notably, our study period ended on March 31, 2016, and excluded patients treated in the Comprehensive Care for Joint Replacement Model that started on April 1, 2016 [6].

Fig. 1:
This study analyzed the elective, DRG 470 episodes from a national CMS data set. CCN = CMS’ certification number.

Background Trend

We also analyzed the payments for all fee-for-service elective THA episodes outside of BPCI during the same period to calculate the effect of BPCI itself in addition to the overall background payments for THAs in the United States.


The adjusted payments data were transformed by natural log and the top 0.01% of episodes by payments were removed to better approximate normality. A log-transformed linear regression model that controlled for age, sex, race, background trend, and comorbidities was used to compare payments for BPCI groups. A logistic regression model controlling for the same variables was used to compare readmission and mortality for BPCI groups. We calculated the effect of BPCI by comparing the payments, readmissions, and mortality of procedures performed by the same surgeons in the hospital and PGP groups from before and after the initiative was started. Statistical significance was calculated using the Student’s t-test associated with the linear regression and z test for odds ratios (ORs) associated with logistic regression. Regression models were created for 90-day readmission and mortality and adjusted for the same factors. Statistical differences were assessed from regression performed using Stata IC, Version 15 (StataCorp LLC, College Station, TX, USA). The level of significance was set at p < 0.05.


Payments, Readmissions, and Mortality After BPCI

For the groups who participated in BPCI, there was a 4.44% (95% confidence interval [CI], -4.58% to -4.30%; p < 0.001) decrease in payments for all participants when controlling for confounding variables (age, race, gender, comorbidities, geography, and date of surgery); however, 90-day mortality and readmission were unchanged in elective DRG 470 THA episodes. For BPCI participants, this reflected an observed USD 1240 decrease from the base price of USD 18,800 per episode over the time of the study. The BPCI participants had lower payments at baseline than non-BPCI-participating groups by 3.09% (95% CI, -3.42% to -2.76%; p < 0.001) when controlling for age, race, gender, comorbidities, and date of surgery; after BPCI began, participating groups had lower payments than non-BPCI groups by 7.39% (95% CI, -7.85% to -6.94%; p < 0.001; Fig. 2).

Fig. 2:
This is a comparison of mean adjusted part A Medicare spending per elective DRG 470 THA from the first quarter of 2013 to the first quarter of 2016 for non-BPCI episodes, for BPCI episode initiators historically, and for BPCI episode initiators during the BPCI program.

When comparing post-BPCI episodes to pre-BPCI episodes for all groups enrolled in BPCI, 90-day readmission (odds ratio [OR], 0.98, 95% CI, 0.92–1.04; p = 0.50) and 90-day mortality (OR: 1.12, 95% CI, 0.88–1.41; p = 0.36) were no different after initiation of the program. Compared with non-BPCI episodes, BPCI participants were associated with an OR of 0.94 (95% CI, 0.91–0.97; p < 0.001) for 90-day readmission before beginning BPCI and an OR of 0.92 (95% CI, 0.87–0.97; p = 0.001) after BPCI initiation (Fig. 3). This indicates that BPCI participant episodes were less likely to be readmitted within 90 days than non-BPCI participants but that the OR of readmission did not change after they initiated BPCI. Relative to non-BPCI episodes, BPCI participants were associated with an OR of 0.71 (95% CI, 0.62–0.82; p < 0.001) for mortality in 90 days before the onset of BPCI episodes and an OR of 0.79 (95% CI, 0.65–0.97; p = 0.02) after initiation of BPCI. This indicates that BPCI participant episodes were less likely to end in mortality within 90 days than non-BPCI participants, but that the OR of mortality did not change after they initiated BPCI.

Fig. 3:
This is a comparison of 90-day mortality and 90-day readmission per elective DRG 470 THA from the first quarter of 2013 to the first quarter of 2016 for non-BPCI episodes, for BPCI episode initiators historically, and for BPCI episode initiators during the BPCI program.

Comparing Hospital-run versus PGP-run BPCI Episodes

When controlling for age, race, gender, comorbidities, geography, and date of surgery, PGP-initiated THA BPCI episodes were associated with a 4.81% decrease in 90-day payments (95% CI, -5.01% to -4.61%; p < 0.001), dropping from a baseline of USD 17,841 to USD 16,506 after BPCI (Fig. 4). Hospital BPCI THA episodes were associated with a 4.04% decrease in 90-day payments (95% CI, -4.24% to 3.84%; p < 0.01), dropping from a baseline of USD 19,799 to USD 18,661 after BPCI.

Fig. 4:
This is a comparison of mean adjusted Medicare spending per elective DRG 470 THA from the first quarter of 2013 to the first quarter of 2016 for non-BPCI episodes, hospital-BPCI episode initiators before and during BPCI, and PGP episode initiators before and during BPCI.

Neither PGP nor hospital programs demonstrated changes in 90-day readmissions after beginning BPCI. When comparing post-BPCI episodes to pre-BPCI episodes, 90-day readmission rate was no different for hospital participants (OR: 0.97, 95% CI, 0.90–1.06, p = 0.53) or PGP participants (OR: 0.99, 95% CI, 0.91–1.07, p = 0.72) after initiation of the BPCI program compared with baseline performance. PGP BPCI participant THA episodes had a 90-day readmission OR of 0.89 (95% CI, 0.83–0.95; p = 0.001) compared with non-BPCI episodes; pre-BPCI baseline for the same surgeons was 0.90 (95% CI, 0.86–0.95; p < 0.001). Hospital BPCI participant THA episodes were associated with a 90-day readmission OR compared with non-BPCI episodes of 0.95 (95% CI, 0.89–1.02; p = 0.16); pre-BPCI baseline for the same hospitals was 0.97 (95% CI, 0.93–1.02; p = 0.32).

Likewise, neither PGP nor hospital BPCI programs experienced changes in 90-day mortality rates compared with their baseline rate before BPCI initiation (Fig. 5). When comparing post-BPCI episodes to pre-BPCI episodes, the 90-day mortality rate was no different for hospitals (OR: 1.18, 95% CI, 0.86–1.63; p = 0.31) or PGPs (OR: 1.05, 95% CI, 0.76–1.46; p = 0.77) after initiation of the BPCI program compared with baseline performance. Compared with non-BPCI episodes, PGP BPCI participant THA episodes were associated with a 90-day mortality OR of 0.77 (95% CI, 0.59–1.02; p = 0.06) from a pre-BPCI baseline for the same surgeons of 0.73 (95% CI, 0.61–0.89; p = 0.001). Compared with non-BPCI episodes, hospital BPCI participant episodes were associated with a 90-day mortality OR of 0.81 (95% CI, 0.63–1.06; p = 0.13) from a pre-BPCI baseline of the same hospitals of 0.69 (95% CI, 0.57–0.84; p < 0.001).

Fig. 5:
This is a comparison of 90-day mortality and 90-day readmission per elective DRG 470 THA from the first quarter of 2013 to the first quarter of 2016 for non-BPCI episodes, hospital-BPCI episode initiators before and during BPCI, and PGP episode initiators before and during BPCI.

BPCI and Elixhauser Comorbidity Index and Age

Patients in BPCI episodes were the same age (mean age difference 0.02; 95% CI, -0.10 to 0.13; p = 0.76) and had a 0.02 lower Elixhauser comorbidity index than patients not in BPCI programs (95% CI, -0.04 to -0.01; p = 0.01). This was less than 1% of the mean Elixhauser comorbidity index of 2.10 (Fig. 6). Comparing before BPCI with during BPCI for BPCI episode initiators, the mean patient comorbidity index did not change (mean difference 0.00, 95% CI, -0.03–0.02; p = 0.73), nor did the mean age (mean difference 0.06, 95% CI, -0.08–0.19; p = 0.41).

Fig. 6:
This is a comparison of mean Elixhauser comorbidity index and age of beneficiaries for non-BPCI episodes, hospital-BPCI episode initiators before and during BPCI, and PGP episode initiators before and during BPCI.

Age did not change after beginning the BPCI program for PGP episode initiators (mean age difference 0.11, 95% CI, -0.08–0.29; p = 0.27) or hospital episode initiators (mean age difference 0.01; 95% CI, -0.18–0.20; p = 0.94). The Elixhauser comorbidity index did not change for PGP-run programs (mean change -0.03; 95% CI, -0.06–0.00; p = 0.09) or hospital-run programs (mean change 0.02, 95% CI, -0.01–0.05; p = 0.21).


Since its inception, the BPCI initiative has been reported to reduce costs per episode of care while maintaining quality of care [14, 21, 29, 33]. Most studies showing cost savings in BPCI are from larger academic centers, which have reported increased administrative overhead to manage these episodes [16, 33]. To our knowledge, there are no prior studies of BPCI that control for confounding variables, including age, race, gender, comorbidities, geography, and date of surgery for each episode and account for the background trends of changing cost of concurrently performed fee-for-service THA episodes. In addition, we are also unaware of any prior studies that have evaluated these data for the entire country. Furthermore, there have been no previous comparisons assessing whether treating physicians or hospital systems are more effective at achieving the fundamental goals of healthcare reform. Our results are consistent with those of previous studies that show BPCI initiation is associated with lower payments without increased adverse complications [1, 11, 16, 20, 29, 33].

This study has limitations. First, this is a retrospective study of a national government data set. Therefore, the validity of the findings is based on the accuracy of the existing data set and the possibility of inaccurate coding cannot be excluded. Second, although confounding variables were adjusted for in statistical analyses, there may be selection bias still present not captured by the demographics and severity of the medical comorbidities. Although gender, race, and hospital zip code were considered, there is no information within the data set to specifically control for education level or socioeconomic status, for example. Third, patient-reported functional and other outcome measures were not used. Fourth, although most PGPs were catalogued, 24% of PGP initiators were not present in the database used. Fifth, a 90-day period was selected for analysis, whereas a shorter or longer period could have been chosen. However, a 90-day postoperative period is most common for BPCI Model 2, and Chen et al. [8] recently reported that a great proportion of surgery-related readmissions within 1 year of discharge is captured by a 90-day postoperative timeframe. Sixth, the data set included only Medicare Part A and excluded Part B expenditures; increased or decreased use of physician services for these surgeries was not analyzed [34]. Finally, the data used only included patients insured by Medicare; these findings may not be applicable to other patient populations. Nevertheless, Medicare patients account for nearly 60% of all TJAs [25, 26], and CMS continues to provide leadership in the development of alternative payment models and value-based care. Further studies should be performed in a non-Medicare population. Finally, because BPCI is an optional program, it is possible that the results of the groups that chose to participate are not generalizable to all programs at large.

This study demonstrated that BPCI was successful in lowering payments without changing 90-day rates of readmission or mortality. Although the savings percentages do not appear to be as large as have been previously reported [1, 9, 11, 13-16, 20, 21, 29, 33], they may be considered to be true savings as a result of BPCI because they exclude the contribution of the background trend of decreasing payments calculated for concurrent nonbundled episodes. The low percentage of mean savings, coupled with costs of implementation, increased administrative costs, and management fees for BPCI participants, highlights that BPCI is not necessarily a profitable endeavor for all participants. In addition, many participants may have experienced one-time savings in the transition into BPCI, calling into question the ability of similar bundled payment programs to continue achieving cost savings.

Although this study did not include a payments part analysis, it is clear that much of the reduction in BPCI payments may come from decreased use of skilled nursing facilities, which is the area of largest decrease across groups (Fig. 4). Studies from larger academic centers in bundled programs have shown that lower readmission rates, decreased hospital length of stay, and reduced postacute care facility discharge are keys to success and cost savings [12, 16, 29]. However, although readmission is a critical quality measure, because readmissions are low at baseline [15, 23, 24, 27], the potential savings associated with a decreased incidence of readmission is relatively minimal. Furthermore, this study of the United States overall shows unchanged readmission rates pre- and post-BPCI. By contrast, decreased use of postacute care facilities and decreased reliance on home care in favor of outpatient care or self-directed care have led to very large savings without an increased incidence of complications [15, 16, 30, 32, 34]. Our analysis shows a trend toward less reliance on postacute inpatient care in both BPCI and non-BPCI patient cohorts (Fig. 2).

PGP-initiated THA episodes were associated with lower episode payments at baseline and a greater decrease in payments in BPCI than hospital-initiated care, even when accounting for available potential confounding variables. Neither group showed changes in the outcomes of readmission or mortality after initiating BPCI. The greater savings of PGP-initiated versus hospital-initiated episodes raises questions about handing the reins of healthcare reform to hospital systems. Although the reasons for differing performance is outside the scope of this analysis, empowering the operating surgeon who has the longest direct relationship with the patient to manage the episode of care may be a more successful strategy than doing the same for a hospital administration.

This study showed no difference in the age of patients in hospital-run or PGP-run BPCI episodes compared with those of the baseline period. There was also no change in the mean Elixhauser comorbidity index of patients in the PGP-run or hospital-run episodes when controlling for all other variables. There has been concern that BPCI participants may be selecting for lower cost patients and referring patients with greater medical comorbidities to tertiary care centers or to nonparticipating providers [1, 11, 19]. However, the current study demonstrates no difference in age or medical comorbidities in PGP or hospital groups after beginning BPCI, indicating that there is no evidence of patient selection in these data.

This national analysis of elective THA episodes demonstrates that BPCI was associated with decreased payments compared with baseline without affecting the outcomes of readmission or mortality. The results also demonstrate that physician-initiated THA episodes are less costly at baseline and achieve greater reductions in payments after BPCI initiation compared with hospital-controlled programs. Additionally, there is no evidence that BPCI participants engage in patient selection regarding age or medical comorbidities and that there is a true cost savings associated with BPCI. Postacute care, including skilled nursing, appears to be a potential target for cost savings without compromise of outcomes. In light of these findings, bundled payment programs that exclude physician group practices from becoming episode initiators such as the Comprehensive Care for Joint Replacement Model may not achieve the highest possible savings [6]. With advanced BPCI on the horizon, it is likely that surgeon control of the bundled THA episodes will continue to outpace hospitals without increased patient risk in value-based care improvement.


We thank Bernard Rosner PhD, for his valuable guidance throughout this analysis.


1. Althausen PL, Mead L. Bundled Payments for Care Improvement: lessons learned in the first year. J Orthop Trauma. 2016;30(Suppl 5):S50-S53.
2. CarePrecise. CarePrecise deep healthcare provider data2017. Available at: https://www.careprecise.com/detail_extended_medical_data.htm. Accessed September 4, 2017.
3. Centers for Medicare & Medicaid Services. BPCI model 2: retrospective acute & post acute care episode center for Medicare & Medicaid innovation. 2016. Available at: https://innovation.cms.gov/initiatives/BPCI-Model-2. Accessed September 4, 2017.
4. Centers for Medicare & Medicaid Services. CMS Bundled Payments for Care Improvement initiative models 2-4: year 2 evaluation & monitoring annual report. 2016. Available at: https://innovation.cms.gov/Files/reports/bpci-models2-4-yr2evalrpt.pdf. Accessed June 24, 2017.
5. Centers for Medicare & Medicaid Services. CMS Bundled Payments for Care Improvement Initiative models 2-4: year 3 evaluation & monitoring annual report. 2017. Available at: https://downloads.cms.gov/files/cmmi/bpci-models2-4yr3evalrpt.pdf. Accessed March 19, 2018.
6. Centers for Medicare & Medicaid Services. Comprehensive care for joint replacement model. 2017. Available at: https://innovation.cms.gov/initiatives/cjr. Accessed September 4, 2017.
7. Centers for Medicare & Medicaid Services. What Part B covers. 2017. Available at: https://www.medicare.gov/what-medicare-covers/part-b/what-medicare-part-b-covers.html. Accessed November 27, 2017.
8. Chen BP, Dobransky J, Poitras S, Forster A, Beaule PE. Impact of definition and timeframe on capturing surgery-related readmissions after primary joint arthroplasty. J Arthroplasty. 2017;32:3563-3567.
9. Chen KK, Harty JH, Bosco JA. It is a brace new world: alternative payment models and value creation in total joint arthroplasty: creating value for TJR, quality and cost-effectiveness programs. J Arthroplasty. 2017;32:1717-1719.
10. Chen LM, Meara E, Birkmeyer JD. Medicare's bundled payments for care improvement initiative: expanding enrollment suggests potential for large impact. Am J Manag Care. 2015;21:814-820.
11. Curtin BM, Russell RD, Odum SM. Bundled Payments for Care Improvement: boom or bust? J Arthroplasty. 2017;32:2931-2934.
12. de Brantes F, Rosenthal MB, Painter M. Building a bridge from fragmentation to accountability--the Prometheus Payment model. N Engl J Med. 2009;361:1033-1036.
13. Doran JP, Beyer AH, Bosco J, Naas PL, Parsley BS, Slover J, Zabinski SJ, Zuckerman JD, Iorio R. Implementation of bundled payment initiatives for total joint arthroplasty: decreasing cost and increasing quality. Instr Course Lect. 2016;65:555-566.
14. Doran JP, Zabinski SJ. Bundled payment initiatives for Medicare and non-Medicare total joint arthroplasty patients at a community hospital: bundles in the real world. J Arthroplasty. 2015;30:353-355.
15. Dummit LA, Kahvecioglu D, Marrufo G, Rajkumar R, Marshall J, Tan E, Press MJ, Flood S, Muldoon LD, Gu Q, Hassol A, Bott DM, Bassano A, Conway PH. Association between hospital participation in a medicare bundled payment initiative and payments and quality outcomes for lower extremity joint replacement episodes. JAMA. 2016;316:1267-1278.
16. Dundon JM, Bosco J, Slover J, Yu S, Sayeed Y, Iorio R. Improvement in total joint replacement quality metrics: year one versus year three of the bundled payments for care improvement initiative. J Bone Joint Surg Am. 2016;98:1949-1953.
17. Edmonds C, Hallman GL; CardioVascular Care Providers. A pioneer in bundled services, shared risk, and single payment. Tex Heart Inst J. 1995;22:72-76.
18. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8-27.
19. Ellimoottil C, Ryan AM, Hou H, Dupree J, Hallstrom B, Miller DC. Medicare's new bundled payment for joint replacement may penalize hospitals that treat medically complex patients. Health Aff (Millwood). 2016;35:1651-1657.
20. Iorio R, Bosco J, Slover J, Sayeed Y, Zuckerman JD. Single institution early experience with the bundled payments for care improvement initiative. J Bone Joint Surg Am. 2017;99:e2.
21. Kamath AF, Courtney PM, Bozic KJ, Mehta S, Parsley BS, Froimson MI. Bundled payment in total joint care: survey of AAHKS membership attitudes and experience with alternative payment models. J Arthroplasty. 2015;30:2045-2056.
22. Kee JR, Edwards PK, Barnes CL. Effect of risk acceptance for bundled care payments on clinical outcomes in a high-volume total joint arthroplasty practice after implementation of a standardized clinical pathway. J Arthroplasty. 2017;32:2332-2338.
23. Kurtz SM, Lau EC, Ong KL, Adler EM, Kolisek FR, Manley MT. Has health care reform legislation reduced the economic burden of hospital readmissions following primary total joint arthroplasty? J Arthroplasty. 2017;32:3274-3285.
24. Lamo-Espinosa J, Pascual-Roquet Jalmar E. Hospital readmission rates following primary total hip arthroplasty: present and future in sight. Ann Transl Med. 2015;3:S38.
25. Lavernia CJ, Hernandez VH, Rossi MD. Payment analysis of total hip replacement. Curr Opin Orthop. 2007;18:23–27.
26. Li Y, Lu X, Wolf BR, Callaghan JJ, Cram P. Variation of Medicare payments for total knee arthroplasty. J Arthroplasty. 2013;28:1513-1520.
27. Mednick RE, Alvi HM, Krishnan V, Lovecchio F, Manning DW. Factors affecting readmission rates following primary total hip arthroplasty. J Bone Joint Surg Am. 2014;96:1201-1209.
28. Murphy SB, Bolz NJ, Terry DP, Talmo CT, Fehm MN. The physician as the provider at risk: rolling the dice. J Arthroplasty. 2016;31:938-944.
29. Navathe AS, Troxel AB, Liao JM, Nan N, Zhu J, Zhong W, Emanuel EJ. Cost of joint replacement using bundled payment methods. JAMA Intern Med. 2017;177:214-222.
30. Nichols CI, Vose JG. Clinical outcomes and costs within 90 days of primary or revision total joint arthroplasty. J Arthroplasty. 2016;31:1400-1406.e1403.
31. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130-1139.
32. Ramos NL, Wang EL, Karia RJ, Hutzler LH, Lajam CM, Bosco JA. Correlation between physician specific discharge costs, LOS, and 30-day readmission rates: an analysis of 1,831 cases. J Arthroplasty. 2014;29:1717-1722.
33. Siddiqi A, White PB, Mistry JB, Gwam CU, Nace J, Mont MA, Delanois RE. Effect of bundled payments and health care reform as alternative payment models in total joint arthroplasty: a clinical review. J Arthroplasty. 2017;32:2590-2597.
34. Slover JD, Mullaly KA, Payne A, Iorio R, Bosco J. What is the best strategy to minimize after-care costs for total joint arthroplasty in a bundled payment environment? J Arthroplasty. 2016;31:2710-2713.
35. The Medicare Payment Advisory Commission. Payment basics. 2017. Available at: http://www.medpac.gov/-documents-/payment-basics. Accessed September 2, 2017.
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