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The American Journal of Managed Care February 2012
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Hospital Readmission Rates in Medicare Advantage Plans
Jeff Lemieux, MA; Cary Sennett, MD; Ray Wang, MS; Teresa Mulligan, MHSA; and Jon Bumbaugh, MA
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Hospital Readmission Rates in Medicare Advantage Plans

Jeff Lemieux, MA; Cary Sennett, MD; Ray Wang, MS; Teresa Mulligan, MHSA; and Jon Bumbaugh, MA
Hospital readmission rates in Medicare Advantage plans are considerably lower than those in Medicare's traditional fee-for-service program, after accounting for differences in risk.
Our objectives in this study were to replicate the Jencks et al results1 for a large sample of MA patients and to examine the differences in readmission rates between MA and FFS. We estimated that the 30-day readmission rate among MA patients was about 14.5% in the 2006-2008 period and that risk-adjusted readmission rates were approximately 13% to 20% lower in MA patients than in FFS patients. However, the statistics we have compiled thus far do not in themselves explain why readmission rates are lower in MA patients.

There are several possible explanations. First, we cannot exclude the possibility that differences in readmission rates between the MA and FFS plans are the result of unobservable differences in the risk of readmission among those populations. Certainly, there are factors that are not accessible to our claims-based data perspective—like health behaviors and social supports—that could affect patients’ likelihood of readmission and were not captured by our DRG-based risk adjuster. That said, it is unclear whether those factors would vary systematically across MA and FFS populations. It is also unclear whether any enhancements in unobservable characteristics of patients (eg, family supports, health behaviors) could be an exogenous tendency or an aftereffect of MA enrollment. For example, suppose we hypothesize that MA enrollees had healthier behaviors and/or stronger support networks that were reducing their readmission rates. Do these behaviors and supports lead them to enroll in MA in the first place, or do MA enrollees have healthier behaviors and/or stronger support networks because MA plans have an incentive to ensure that they do, and provide programmatic interventions to increase the likelihood that they will? Certainly, the latter explanation—which could help to explain the differences we observe—is a tenable one; further research would be important to test this hypothesis.

Likewise, we are not yet able to discern whether network effects—such as steering patients to high-performing hospitals— are more important than other interventions such as transitional care efforts. Preliminary data from Medicare’s FFS Care Transitions Project indicate that the sorts of transitional care programs often used by MA plans4 can work in FFS settings. These efforts, which are sponsored by several Quality Improvement Organizations across the country, indicate that improving communication among healthcare providers and using proven transitional care interventions can play a key role in reducing readmission rates. For example, in one part of western Pennsylvania, FFS readmission rates were reduced from 18% to 14% using staff of the local Area Agencies on Aging trained in the Coleman health coaching model (personal communication with Dr David Wenner, November 30, 2010). Dr Wenner’s preliminary results from the Care Transitions Project in Pennsylvania were first presented at the conference Optimizing Home Health in Care Transitions, 2010 Summit, October 26, 2010, Philadelphia, Pennsylvania. Slides from that conference are available from the authors or from Dr Wenner.

Preliminary results from similar interventions provided to FFS Medicare patients in Colorado are also encouraging.16 Early results and observations from the Care Transitions Project were also cited by CMS at a conference on Medicare readmissions in June 201117 and results associated with 14 implementation sites were described in more detail by Dr Jane Brock at a conference on consumer-centric care in October 2011.18 Measurements such as those presented in this report can help track progress following the introduction of these and other interventions intended to help reduce readmission rates in both MA and FFS.

Acknowledgment

The authors would like to thank Drs Gerard Anderson and Stephen Jencks for helpful comments throughout the process of computing these data. This research was supported by the authors’ employers, MedAssurant Inc, and America’s Health Insurance Plans (AHIP), under a collaborative research agreement. AHIP is a national trade association for health insurance, including Medicare Advantage plans.


Author Affiliations: From Center for Policy and Research (JL, TM), America’s Health Insurance Plans, Washington, DC; MedAssurant Inc (CS, RW, JB), Bowie, MD.


Funding Source: None.


Author Disclosures: Dr Lemieux and Ms Mulligan report employment with AHIP, a national trade association for health plans, including Medicare Advantage Plans. Dr Sennett reports that at the time of the study he was employed by and a paid advisor to MedAssurant Inc. He is now with IMPAQ International, Columbia, MD. The other authors (RW, JB) report no relationship or financial interest with any entity that would pose a confl ict of interest with the subject matter of this article.


Authorship Information: Concept and design (JL, CS, RW, TM); acquisition of data (JL, TM, JB); analysis and interpretation of data (JL, CS, RW, TM, JB); drafting of the manuscript (JL, CS); critical revision of the manuscript for important intellectual content (JL, CS, JB); statistical analysis (JL, RW, TM, JB); provision of study materials or patients (JL); administrative, technical, or logistic support (RW, JB); and supervision (JL, CS, JB).


Address correspondence to: Jeff Lemieux, MA, SVP, Center for Policy and Research, AHIP, 601 Pennsylvania Ave, NW, South Bldg, Ste 500, Washington, DC 20004. E-mail: jlemieux@ahip.org.
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2. Anderson GF, Steinberg EP. Hospital readmissions in the Medicare population. N Engl J Med. 1984;311(21):1349-1353.


3. Boutwell A, Hwu S. Effective Interventions to Reduce Rehospitalizations: A Survey of the Published Evidence. Cambridge, MA: Institute for Healthcare Improvement; 2009. http://ah.cms-plus.com/fi les/STAAR_A_Survey_of_the_Published_Evidence.pdf. Accessed February 1, 2012.


4. Bayer E; for America’s Health Insurance Plans (AHIP) Center for Policy and Research. Innovations in Reducing Preventable Hospital Admissions, Readmissions, and Emergency Room Use. An Update on Health Plan Initiatives to Address National Health Care Priorities. http://www.ahipresearch.org/pdfs/innovations2010.pdf. Accessed February 1, 2012. Washington, DC: AHIP; June 2010.


5. HealthCare.gov. Partnership for patients: better care, lower costs. http://www.healthcare.gov/center/programs/partnership/index.html. Updated December 14, 2011. Accessed February 1, 2012. Published April 12, 2011.


6. America’s Health Insurance Plans (AHIP) Center for Policy and Research. Using AHRQ’s ‘Revisit’ Data to Estimate 30-Day Readmission Rates in Medicare Advantage and the Traditional Fee-for-Service Program. http://www.ahipresearch.org/pdfs/AHRQ_revisit_readmission_ rates_10-12-10.pdf. Accessed February 1, 2012. Washington, DC: AHIP; October 2010.


7. Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (H-CUP). http://www.ahrq.gov/data/hcup/. Accessed February 1, 2012.


8. America’s Health Insurance Plans (AHIP) Center for Policy and Research. Working Paper: Using State Hospital Discharge Data to Compare Readmission Rates in Medicare Advantage and Medicare’s Traditional Fee-for-Service Program. http://www.ahipresearch.org/pdfs/ 9State-Readmits.pdf. Accessed February 1, 2012. Washington, DC: AHIP; May 2010.


9. MedAssurant. MedAssurant to assist NCQA in the development of healthcare quality measures. PressReleasePoint. http://www.pressreleasepoint.com/medassurant assist-ncqa-development-healthcarequality-measures. Published August 12, 2010. Accessed February 1, 2012.


10. Elliott M, Haviland AM, Orr N, Hambarsoomian K, Cleary PD. How do experiences of Medicare benefi ciary subgroups differ between managed care and original Medicare? Health Serv Res. 2011;46(4): 1039-1058.


11. Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305(7):675-681.


12. Basu J. Medicare managed care and primary care quality: examining racial/ethnic effects across states. Health Care Manag Sci [published online ahead of print September 3, 2011]. Accessed February 1, 2012.


13. Basu J, Mobley L. Do HMOs reduce preventable hospitalizations for Medicare benefi ciaries? Med Care Res Rev. 2007;64(5):544-567.


14. Wier LM, Barrett M, Steiner C, Jiang HJ. All-Cause Readmissions by Payer and Age, 2008. HCUP Statistical Brief #115. http://www.hcupus.ahrq.gov/reports/statbrief/sb115.pdf. Accessed February 1, 2012.Rockville, MD: Agency for Healthcare Research and Quality; June 2011.


15. Goodman DC, Fisher ES, Chang CH; for The Dartmouth Institute for Health Policy & Clinical Practice. After Hospitalizations: A Dartmouth Atlas Report on Post-Acute Care for Medicare Benefi ciaries.http://www.dartmouthatlas.org/downloads/reports/Post_discharge_events_092811.pdf. Published September 2011. Accessed February 1, 2012.


16. Booth M. Medicare spreads savings from Denver program. Denver Post. December 13, 2010. http://www.denverpost.com/news/ci_16843482. Accessed February 1, 2012.


17. Norman J. The Commonwealth Fund. Washington health policy week in review: hope emerges on slowing preventable hospital readmissions. http://www.commonwealthfund.org/Newsletters/Washington-Health-Policy-in-Review/2011/Jun/June-20-2011/Hope-Emerges.aspx. Published June 14, 2011. Accessed February 2, 2012.


18. Brock J. Health Innoventions: Consumer-Centric Health Model for Change ‘11. Lecture presented at: Sustainable Behavior Change conference; October 12-13, 2011; Seattle, WA. http://www.slideshare.net/HealthInnoventions/jane-brock-at-consumer-centric-health-models-forchange-11/. Accessed February 2, 2012.
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