• Center on Health Equity and Access
  • Clinical
  • Health Care Cost
  • Health Care Delivery
  • Insurance
  • Policy
  • Technology
  • Value-Based Care

Post-SNF Outcomes and Cost Comparison: Medicare Advantage vs Traditional Medicare

The American Journal of Managed CareApril 2021
Volume 27
Issue 04

Patients enrolled in Medicare Advantage had better outcomes and lower cost following skilled nursing facility (SNF) discharge than patients enrolled in traditional fee-for-service Medicare.


Objectives: To compare outcomes and costs following skilled nursing facility (SNF) discharge for patients within a Medicare Advantage (MA) organization vs traditional Medicare (TM).

Study Design: Retrospective analysis of adults with a postacute SNF admission identified from MA claims (MA cohort: n = 56,228) and the Medicare 5% Limited Data Sets (TM cohort: n = 67,859).

Methods: Outcomes included hospitalization, proportion of days at home, and total medical costs during the 180 days post SNF discharge, and successful community discharge. Regression models accounted for patient characteristics and health care utilization in the 180 days prior to the proximal hospitalization and characteristics of the proximal hospitalization using backward variable selection and fixed effects for MA enrollment. To control for observable differences between individuals who selected MA vs TM, inverse probability of treatment weighting (IPTW) was conducted.

Results: The MA cohort was younger than the TM cohort (median age, 77 vs 81 years), more likely to have qualified for Medicare based on disability (29% vs 20%), and less likely to have dual Medicare/Medicaid eligibility (16% vs 23%). After adjustment, MA was associated with 22% decreased odds of hospitalization during the 180 days post SNF discharge, 19% increased odds of successful community discharge, a 4% increase in the proportion of days at home (equating to 6.7 additional days), and a 24% decrease in medical costs post SNF discharge. Results using IPTW were similar.

Conclusions: MA was associated with better outcomes and lower costs post SNF discharge, suggesting efficiencies in care for SNF patients with MA. Further research is needed to evaluate specific MA features that may lead to better value.

Am J Manag Care. 2021;27(4):140-146. https://doi.org/10.37765/ajmc.2021.88616


Takeaway Points

In this comparison of outcomes and costs following skilled nursing facility (SNF) discharge for patients with Medicare Advantage (MA) vs traditional Medicare (TM), we found that patients with MA had better outcomes and lower costs post SNF discharge than those with TM.

  • The current study supports findings from the existing literature by evaluating post–SNF discharge costs, encompassing more than only some specific diagnoses, and including patients discharged from a broader range of hospitals.
  • These findings suggest efficiencies in care for patients with MA based on better outcomes following an SNF stay combined with reduced costs.


Use of postacute care (PAC) is expected to increase with the growing elderly US population and their higher-acuity medical needs. PAC typically encompasses skilled care following a hospitalization that is delivered within a skilled nursing facility (SNF), long-term acute care hospital, inpatient rehabilitation facility, or patient’s home. In 2017, Medicare paid $58.9 billion for long-term hospitalization, rehabilitation, and SNF services, equivalent to 8.3% of its $710.2 billion yearly expenditures.1 SNF services alone cost $28.7 billion, amounting to nearly half of Medicare’s total bill for PAC.1

Established reimbursement mechanisms for traditional Medicare (TM), which reimburse providers for services rendered rather than linking payment to evidence of care quality or specific outcomes, do not strongly incentivize the provision of high-value PAC.1 Similarly, there are limited incentives within TM to reward investment in care transitions, potentially leading to more costly and lower-quality care.2 Maximizing the value of overall PAC, and specifically SNF services, is of particular interest for CMS and Congress. In fact, the Affordable Care Act required the development of an implementation plan for a value-based payment program for Medicare SNF payments.

In contrast to TM, Medicare Advantage (MA) health plans receive capitated payments for providing health care coverage to enrollees.1 MA payment arrangements, regulatory requirements, and the MA Star Rating systems were designed to incentivize proactive patient management in a clinically appropriate manner, reduce waste, and shift the focus from volume to value, unlike TM, which generally does not have such robust incentives. Findings of some studies suggest that this approach is associated with better outcomes and/or lower costs.3-7 Several studies have demonstrated decreases in health care utilization and improved outcomes post hospital discharge for MA compared with TM.3-9 However, a study by Panagiotou et al, using data from 2011 to 2014, found small but significant increases in admission rates for acute myocardial infarction (0.3%; P = .002), congestive heart failure (0.3%; P = .001), and pneumonia (0.5%; P < .001) with MA vs TM.10

The aforementioned studies focused on procedures only at hospitals that either receive medical education payments or are disproportionate share hospitals that serve a significantly disproportionate number of low-income or uninsured patients, as defined by Section 1886(d)(1)(B) of the Social Security Act. This is likely because required regulatory reporting for these hospitals provided the only publicly available MA claims data, until 2018 when 2015 MA encounter data from CMS first became available to researchers. By using multiple years of data from a large MA organization, our study expands on prior work by evaluating post–SNF discharge outcomes and costs, encompassing more than only some specific diagnoses, and including patients discharged from a broader range of hospitals. Our specific study objective was to compare outcomes and costs following SNF discharge between MA and TM cohorts.



This retrospective study used administrative claims data from June 1, 2014, to December 31, 2016, for MA enrollees identified from a national MA organization and TM enrollees identified using the Medicare 5% Limited Data Sets from CMS. The MA organization covers approximately 20% of all MA enrollees in the United States, with membership primarily in the South and Midwest. These 2 populations were considered separate cohorts because they were drawn from different data sources. Hereinafter, the cohort covered by the MA organization will be referred to as the “MA” cohort. The initial SNF stay was defined as the first SNF admission between January 1, 2015, and June 30, 2016, with an admission date within 1 day of an inpatient admission, which will be referred to as the proximal hospitalization. An Advarra institutional review board approved the study and granted a waiver of informed consent.

Study Population

Inclusion criteria were being 18 years or older and having continuous MA or TM enrollment during the 180 days before the proximal hospitalization and 180 days post SNF discharge (or until postdischarge initiation of hospice or death). Patients who died or elected hospice during the 30 days post SNF discharge were excluded to ensure adequate observation time. Patients with multiple diagnosis-related groups on the proximal hospitalization, which may reflect multiple sequential hospitalizations that appear as only 1 hospitalization in claims, were excluded. Patients who elected hospice before the proximal hospitalization or had end-stage renal disease, evidence of multiple insurers, payments made under a full or global capitated arrangement, or group MA coverage during the proximal hospitalization were excluded due to limited cost and utilization information in claims. To confirm an initial SNF stay vs SNF readmissions, patients with an SNF stay in the 180 days before the proximal hospitalization were excluded.

Variables and Outcomes Measures

Variables assessed before the proximal hospitalization included age, sex, race, geographic region, disability status (original reason for Medicare enrollment), and partial or full dual eligibility for Medicare and Medicaid. Determination of geographic region was based on patient place of residence according to the 4 Census regions.11 Data before the proximal hospitalization were used to calculate patients’ 180-day Elixhauser Comorbidity Index score. We reported only the comorbidities for which at least 10% prevalence was observed.12 Health care resource utilization before the proximal hospitalization included hospitalizations and length of stay (LOS), intensive care unit (ICU)/critical care unit (CCU) stays and LOS, emergency department (ED) visits, and total allowed medical costs. Comparisons between MA and TM cohorts included proximal hospitalization LOS and cost, ICU/CCU use and LOS during the proximal hospitalization, and initial SNF LOS and cost (based on fee-for-service allowed amounts). We also reported the percentage of patients with a proximal hospitalization LOS less than 3 days, because a hospital stay of at least 3 days is required for TM SNF admission. In the post–SNF discharge period, we evaluated hospitalizations, ED visits, and subsequent SNF stays, and the presence of at least 1 home health visit within 30 days post SNF discharge, mortality between 30 and 180 days post SNF discharge, election of hospice, and cost.

Outcome measures were hospitalization (same-day transfers from initial SNF and admissions post SNF discharge) and total allowed medical costs during the 180 days post SNF discharge, successful community discharge, and proportion of days at home.13 Successful community discharge was defined as being discharged to home or home health within 100 days of initial SNF admission and remaining alive without admission to any acute or postacute setting for at least 30 days.9 Proportion of days at home was defined as the total number of days that a patient did not spend in an inpatient facility divided by the total number of days the patient was alive in the post–SNF discharge period, with follow-up time censored at initiation of hospice.13 Post–SNF discharge cost estimates comprised mean costs in the 180 days post SNF discharge based on plan allowable amounts for each procedure or encounter and included patient- and plan-paid amounts.


We split the full data set into randomly selected training (80% of MA and TM enrollees) and validation/testing (remaining 20%) samples. Using the validation/testing sample, for each outcome measure, we used the following items for model validation and selection: correlation coefficient of actual vs predicted outcomes for the entire sample, and goodness-of-fit plots (actual vs predicted outcomes, by predicted rank cohorts). Further details can be found in the eAppendix (available at ajmc.com). We analyzed data using SAS Enterprise Guide 7.13 (SAS Institute Inc).

For each outcome measure, we developed separate regression models to quantify, and control for, beneficiary risk factors in comparisons between MA and TM cohorts. Analytical models adjusted for patient demographic and clinical characteristics and proximal hospitalization utilization. Regression models for hospitalization and cost outcomes also controlled for initial SNF LOS. However, because successful discharge to community was measured in the 100 days following SNF admission (in contrast to other outcomes that were measured post SNF discharge), the regression model for this outcome did not include the SNF LOS as a control variable because that might bias the outcome. Models used backward variable selection with age, sex, geographic region, race, and MA organization enrollment, as a binary fixed effect variable, forced into the model. Logistic regression was used to assess the likelihood of successful SNF discharge to the community and likelihood of 1 or more hospitalizations post SNF discharge. Proportion of days at home and total allowed medical costs post SNF discharge were evaluated using a generalized linear model with log-linear transformation. Determination of the most salient predictors of each outcome was validated using machine learning techniques. Detailed testing/validation methods are provided in the eAppendix.

To address potential bias associated with differences in patient characteristics between the MA and TM cohorts, a propensity score–based method known as inverse probability of treatment weighting (IPTW) was conducted as a sensitivity analysis. A logistic regression model was used to estimate a propensity score (eg, conditional probability of being in MA vs TM cohorts given observed baseline covariates) for each patient. Standardized mean differences (SMDs) were calculated for all baseline variables, and those with an SMD of at least 0.10 were included in the model.14 Pre- and post-IPTW adjusted SMD values between MA and TM cohorts for the baseline variables were calculated (eAppendix). MA patients were assigned a weight of the inverse of their propensity score, and OM patients were assigned a weight of the inverse of 1 minus their propensity score. The weights were included in the appropriate outcome models to balance the distribution of potential confounders across cohorts.


Study Cohorts

We identified 56,228 patients with MA and 67,859 patients with TM with a qualifying SNF stay (Table 1). Table 2 summarizes cohort demographics and baseline clinical characteristics. Compared with the TM cohort, the MA cohort was younger (median age, 77 vs 81 years), less likely to be female (61% vs 65%), and more likely to have qualified for Medicare based on disability (29% vs 20%). Dual Medicare/Medicaid eligibility was lower for MA (16% vs 23%). The MA and TM cohorts had similar median comorbidity scores and rates of hospitalization, ED visits, and ICU/CCU stays before the proximal hospitalization.

The MA cohort had longer mean proximal hospitalization LOS (8 vs 6 days) and mean ICU/CCU LOS (9 vs 7 days), and shorter mean initial SNF LOS (19 vs 30 days) compared with the TM cohort (Table 3). The MA cohort had similar mean proximal hospitalization costs ($16,444 vs $16,243) and lower mean initial SNF stay costs ($10,874 vs $17,141) compared with the TM cohort.

SNF Utilization and Outcomes

In unadjusted MA vs TM comparisons (Table 4), the MA cohort had lower rates of hospitalization during the initial SNF stay (9% vs 12%) and within 30 (11% vs 13%) and 180 (29% vs 32%) days post SNF discharge. ED visit rates during the initial SNF stay (7% vs 8%) and within 30 (12% vs 11%) and 180 (34% vs 33%) days post SNF discharge were similar for both cohorts. Within 30 and 180 days after SNF discharge, rates of subsequent SNF admission were lower with the MA cohort (30 days: 12% vs 18%; 180 days: 22% vs 29%).

Following the initial SNF stay, study cohorts had similar rates of patients discharged home (54% vs 53%). However, the MA cohort was more likely to initiate home health within 30 days of SNF discharge (60% vs 53%). The MA cohort had a higher rate of successful community discharge (64% vs 57%) and higher proportion of days at home in the 180 days post SNF discharge (91% vs 87%) compared with TM. Mortality rates were similar (11% vs 12%), but the MA cohort had lower mean total medical costs within 180 days post SNF discharge ($18,326 vs $24,314).

Risk-Adjusted Models Comparing SNF Outcomes and Cost

In non-IPTW adjusted models, the MA cohort was associated with 22% decreased odds of hospitalization during the 180 days post SNF discharge, 19% increased odds of successful community discharge, a 4% increase in proportion of days at home (6.7 additional days based on mean follow-up time), and a 24% decrease in medical costs post SNF discharge (P < .001 for all). As a sensitivity analysis, we excluded initial SNF LOS from models for hospitalization, days in the home, and cost; the directionality of results was the same. For each of the 4 selected models, ratios of actual to predicted outcome were reasonably close to 1, with credible sample size suggesting an unbiased model. The IPTW adjusted models yielded very similar results (Table 5).


We conducted a claims analysis of 2 large cohorts: one MA cohort (from 1 organization) and one TM cohort, both with a postacute SNF stay. The MA cohort had longer proximal hospitalization LOS, was more likely to have an ICU/CCU stay, and had longer ICU/CCU LOS compared with patients with TM. Unadjusted analyses indicated that the MA cohort had lower rates of hospitalization during the initial SNF stay and within 180 days post SNF discharge, a higher rate of successful community discharge, and lower post–SNF discharge medical costs. Adjusted models revealed significantly better post–SNF discharge outcomes and lower costs for MA patients. The MA cohort was more likely to initiate home health within 30 days post SNF discharge.

These findings are similar to those of other studies that compared SNF utilization and outcomes.8,9 Kumar et al reported shorter length of rehabilitation, higher likelihood of successful discharge, and lower 30-day hospital readmission rates among patients with MA vs TM, and Huckfeldt et al found that MA enrollees had a lower likelihood of inpatient rehabilitation facility admission and shorter SNF LOS compared with TM.8,9 However, unlike this analysis, these studies were limited to specific diagnoses, such as hip fracture and lower extremity joint replacement, stroke, and heart failure, and included only MA patients from disproportionate share and teaching hospitals.8,9 Additionally, previous studies did not have access to administrative claims for MA enrollees and were thus unable to control for pre-SNF characteristics and health care utilization or to report patient-level cost differences for MA and TM patients. Our study also considered outcomes over 180 days post SNF discharge, as opposed to the 90-day metric used in prior work. Our results confirm and expand upon previous findings by comparing outcomes and considering total health care costs.

Similar to other reports, MA was associated with shorter initial SNF LOS but improved outcomes in our study. Although this was not the focus of our study, the flexibility and incentives allowed within MA that help maintain a focus on value instead of volume may explain these findings.15,16 For instance, higher rates of home health initiation post SNF discharge in the MA cohort may suggest a shift to an appropriately lower-intensity setting. Likewise, innovative approaches to developing SNF networks may guide patients to the most appropriate SNF facility for their condition and may partially explain the observed lower initial SNF LOS and improved outcomes post SNF discharge. Although the MA cohort had significantly lower initial SNF-related costs, most likely related to differences in SNF LOS and contracted SNF rates, cost models only evaluated post–SNF discharge costs. In exploring the effect of referral concentration for SNFs, Huckfeldt et al found that referrals for MA enrollees were concentrated in fewer facilities compared with TM.8 However, Rahman et al found no relationship between SNF contract concentration and outcomes.17 Additionally, 2 studies that evaluated 5-star quality ratings highlighted concerns about the quality of SNF facilities within MA networks, but Neuman et al did not find a consistent association between readmission rates and SNF quality measures.7,18,19 In our study, MA was associated with better outcomes at lower cost.

The MA organization in this study utilizes systems to coordinate patient care at time of hospital discharge and during the SNF stay to ensure that patients are receiving appropriate and timely care. These care coordination activities can involve a variety of health care professionals, including those in value-based payment arrangements, and are designed to help expedite needed care, address gaps in care, and improve value. Telephonic outreach conducted post SNF discharge can identify patients needing clinical support, thereby preventing unnecessary hospitalizations or subsequent SNF stays. Additionally, MA plans within the MA organization can meet regularly with SNFs to review individual care plans and facility-level performance metrics. Although this study could not evaluate the extent to which these mechanisms accounted for the better outcomes and lower cost observed with MA, future research could explore the best practices that are most associated with superior outcomes.


Certain study limitations should be considered. The risk of misclassification and missing information is inherent to claims data. Evaluation of a single MA organization and lack of a randomized controlled design limits generalizability. Although propensity score methods were used to control for potential selection bias into MA or TM, additional bias may remain. We excluded patients with insufficient post–SNF discharge enrollment to ensure sufficient follow-up time to evaluate potential effects of initial SNF stay on outcomes and found that a higher proportion of the MA cohort met this exclusion criterion. Literature suggesting higher disenrollment among high-need patients within MA prompted us to further evaluate the excluded group.20,21 Total costs before the proximal hospitalization for excluded patients were higher for the TM vs MA cohort. Equally, the comorbidity level before the proximal hospitalization for excluded patients was similar to that for patients who remained in the study. Nonetheless, our findings should be interpreted in light of this limitation. Evaluation of patients excluded for election of hospice or dying within 30 days of SNF discharge revealed similar hospice initiation rates for MA and TM cohorts and a lower mortality rate within 30 days post SNF discharge in the MA cohort, suggesting that this exclusion did not introduce favorable selection bias. Results from these investigations align with those in a recent article showing that more costly patients with certain complex, chronic diagnoses switched into or remained in MA plans, highlighting the need for a more comprehensive understanding of changes in MA populations.22

Other studies’ findings suggest that regional variation has a larger effect on utilization than that of a patient’s payer.23 Although we controlled for the 4 Census regions, a nested model to account for market-level variation was not possible given limited geographic overlap between the 2 cohorts. Given the concentration of this MA plan’s enrollees in certain markets which relate to differential hospital, SNF, and home health utilization (eg, the South), additional work comparing utilization and outcomes among beneficiaries in the same markets is needed. Additionally, we could not account for differential selection of SNFs between MA and TM. Future work could incorporate facility-level fixed effects in an effort to compare outcomes for patients at the same SNF.9 It is possible that the lower initial SNF LOS resulted from increased stabilization during a lengthier proximal hospitalization. In addition, MA organization–specific home health and other care management services might have been leveraged post SNF discharge to avoid rehospitalization and improve patient outcomes. The continuum of PAC and home health utilization differences between MA and TM warrants further research.


PAC represents a major cost component in Medicare. Within MA, incentives encourage plans to better manage transitions, foster better care in the home, and reduce readmissions. Better outcomes following an SNF stay combined with reduced costs suggest efficiencies in care for patients with MA. Future research should explore the persistence of these findings in other MA cohorts.


The authors thank Bill Loyd and Jo Erwin for analytic and actuarial guidance and Mary Costantino for providing medical writing review. Monica Nicosia, PhD, Nicosia Medical Writer LLC, provided medical editing and writing support, funded by Humana Inc.

Author Affiliations: Humana Healthcare Research, Inc (AWC, RB, CL, CU, CRB), Louisville, KY; Humana, Inc (RS, HP, AC, AS, TC, CS, MN, PP, WS), Louisville, KY; US Department of Veterans Affairs Medical Center (KT), Providence, RI; School of Public Health, Brown University (KT), Providence, RI.

Source of Funding: None; this work was completed as part of normal work for Humana employees. Dr Thomas’ effort was supported by a US Department of Veterans Affairs Health Services Research & Development Career Development Award (CDA-14-42).

Author Disclosures: Dr Casebeer, Mr Schwartz, Mr Caplan, Dr Sharma, Dr Uribe, and Dr Brown are employed by and own stock in Humana, a Medicare Advantage organization. Mr Patel, Dr Bhattacharya, Dr Long, Dr Changamire, and Mr Newsom are employed by Humana. Dr Shrank is a board member of GetWell Network and is employed by and owns stock in Humana. Dr Stemple, Dr Thomas, and Dr Painter report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (AWC, AC, RB, CL, AS, TC, CS, MN, PP, WS, CRB); acquisition of data (RS, HP); analysis and interpretation of data (AWC, RS, RB, HP, AC, CL, AS, TC, CS, PP, WS, CRB); drafting of the manuscript (AWC, RS, RB, CL, CS, KT, MN); critical revision of the manuscript for important intellectual content (AWC, RS, HP, AC, CL, AS, TC, CU, CS, KT, MN, PP, WS, CRB); statistical analysis (RS); administrative, technical, or logistic support (CU, KT, MN, WS, CRB); supervision (AWC, CU, CS, MN, CRB); and peer review of statistical analysis and results (HP).

Address Correspondence to: Adrianne W. Casebeer, PhD, Humana Healthcare Research, Inc, 500 W Main St, Louisville, KY 40202. Email: acasebeer@humana.com.


1. Medicare Payment Advisory Commission. Report to the Congress: Medicare Payment Policy. Medicare Payment Advisory Commission; 2019. Accessed July 5, 2019. http://www.medpac.gov/docs/default-source/reports/mar19_medpac_entirereport_sec.pdf?sfvrsn=0

2. Mechanic R. Post-acute care—the next frontier for controlling Medicare spending. N Engl J Med. 2014;370(8):692-694. doi:10.1056/NEJMp1315607

3. Basu J, Mobley LR. Medicare managed care plan performance: a comparison across hospitalization types. Medicare Medicaid Res Rev. 2012;2(1):002.01.a02. doi:10.5600/mmrr.002.01.a02

4. Basu J. Medicare managed care and primary care quality: examining racial/ethnic effects across states. Health Care Manag Sci. 2012;15(1):15-28. doi:10.1007/s10729-011-9176-5

5. Beveridge RA, Mendes SM, Caplan A, et al. Mortality differences between traditional Medicare and Medicare Advantage: a risk-adjusted assessment using claims data. Inquiry. 2017;54:46958017709103. doi:10.1177/0046958017709103

6. Parashuram S, Kim S, Dowd B. Inappropriate utilization in fee-for-service Medicare and Medicare Advantage plans. Am J Med Qual. 2018;33(1):58-64. doi:10.1177/1062860617702240

7. Neuman MD, Wirtalla C, Werner RM. Association between skilled nursing facility quality and hospital readmissions. JAMA. 2014;312(15):1542-1551. doi:10.1001/jama.2014.13513

8. Huckfeldt PJ, Escarce JJ, Rabideau B, Karaca-Mandic, Sood N. Less intense postacute care, better outcomes for enrollees in Medicare Advantage than those in fee-for-service. Health Aff (Millwood). 2017;36(1):91-100. doi:10.1377/hlthaff.2016.1027

9. Kumar A, Rahman M, Trivedi AN, Resnik L, Gozalo P, Mor V. Comparing post-acute rehabilitation use, length of stay, and outcomes experienced by Medicare fee-for-service and Medicare Advantage beneficiaries with hip fracture in the United States: a secondary analysis of administrative data. PLoS Med. 2018;15(6):e1002592. doi:10.1371/journal.pmed.1002592

10. Panagiotou OA, Kumar A, Gutman R, et al. Hospital readmission rates in Medicare Advantage and traditional Medicare. Ann Intern Med. 2019;17(2):99-106. doi:10.7326/M18-1795

11. Census regions and divisions of the United States. United States Census Bureau. Accessed August 1, 2019. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf

12. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004

13. Lee H, Shi SM, Kim DH. Home time as a patient-centered outcome in administrative claims data. J Am Geriatr Soc. 2019;67(2):347-351. doi:10.1111/jgs.15705

14. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786

15. Tompkins C, Higgins A, Perloff J, Veselovskiy G. Population health management in Medicare Advantage. Health Affairs. April 2, 2013. Accessed August 1, 2019. https://www.healthaffairs.org/do/10.1377/hblog20130402.029363/full/

16. Cotton P, Newhouse JP, Volpp KG, et al. Medicare Advantage: issues, insights, and implications for the future. Popul Health Manag. 2016;19(S3):S1-S8. doi:10.1089/pop.2016.29013.pc

17. Rahman M, Meyers DJ, Mor V. The effects of Medicare Advantage contract concentration on patients’ nursing home outcomes. Health Serv Res. 2018;53(6):4087-4105. doi:10.1111/1475-6773.13073

18. Meyers DJ, Mor V, Rahman M. Medicare Advantage enrollees more likely to enter lower-quality nursing homes compared to fee-for-service enrollees. Health Aff (Millwood). 2018;37(1):78-85. doi:10.1377/hlthaff.2017.0714

19. Kimball CC, Nichols CI, Nunley RM, Vose JG, Stambough JB. Skilled nursing facility star rating, patient outcomes, and readmission risk after total joint arthroplasty. J Arthroplasty. 2018;33(10):3130-3137. doi:10.1016/j.arth.2018.06.020

20. Meyers DJ, Belanger E, Joyce N, McHugh J, Rahman M, Mor V. Analysis of drivers of disenrollment and plan switching among Medicare Advantage beneficiaries. JAMA Intern Med. 2019;179(4):524-532. doi:10.1001/jamainternmed.2018.7639

21. Riley GF. Impact of continued biased disenrollment from the Medicare Advantage Program to fee-for-service. Medicare Medicaid Res Rev. 2012;2(4):mmrr.002.04.a08. doi:10.5600/mmrr.002.04.a08

22. Park S, Fishman P, White L, Larson EB, Coe NB. Disease-specific plan switching between traditional Medicare and Medicare Advantage. Perm J. 2020;24:19.059. doi:10.7812/TPP/19.059

23. Waxman DA, Min L, Setodji CM, Hanson M, Wenger NS, Ganz DA. Does Medicare Advantage enrollment affect home healthcare use? Am J Manag Care. 2016;22(11):714-720.

Related Videos
Dr Lucy Langer
Lalan Wilfong, MD, in an interview on blue AJMC background
Miriam Atkins, MD, president of Community Oncology Alliance, during a video interview on blue AJMC background
Dr David Fajgenbaum | Image credit: The Castleman Disease Collaborative Network
Ted Okon, MBA, of Community Oncology Alliance, during a Zoom video interview
dr christina barrington
Kimberly Westrich, MA
Bincy Abraham, MD
Screenshot of Laura Wingate in a Zoom interview
Screenshot of Laura Wingate in a Zoom interview
Related Content
© 2024 MJH Life Sciences
All rights reserved.