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Medicare Readmissions Policy Threatens Vulnerable Communities And Patients

While well-intended, recent Medicare regulations that penalize hospitals with high readmission rates could have the unintended consequence of increasing health disparities. While treatment failures in general must be addressed, the reasons for readmissions are complex, often out of a hospital’s control, and reflect the community and its population.

According to research, differences in hospital readmission rates are more closely linked to the patient socio-demographic and community factors than to hospital performance. Implementing the policy the Centers for Medicare & Medicaid Services (CMS) has included in the proposed inpatient payment rule, which is based on a measure that does not sufficiently account for the major socio-demographic factors contributing to readmissions, would disproportionately penalize certain at-risk communities and exert additional financial burdens to already stressed local healthcare systems.

High readmissions rates are associated with areas of poverty and are heavily impacted by race, ethnicity other demographics

  • Abundant evidence exists that poverty is strongly associated with poor health status and more hospital readmissions. These areas are often urban environments with complex populations and dense zones of poverty, or broad regions of poverty, both with extremely high healthcare needs.1,2,3
  • Among many other studies with similar findings, the Agency for Healthcare Research and Quality (AHRQ) analyzed national rates of preventable hospitalizations and found that rates were 27 percent higher among residents in poorer large urban communities than those living in wealthier urban communities.4
  • CMS’ own analysis shows that black, Hispanic, Asian/Pacific Islander and American Indian patients have higher readmission rates than white patients.5
  • Numerous studies show that in addition to income and race, other socioeconomic variables including education, preferred language, living status, and insurance are statistically and significantly associated with healthcare utilization-including readmissions – and outcomes.6,7
  • Readmissions occur as a result of the totality of the healthcare system and hospitals are able to impact only part of that continuum. Outside of the hospital setting, healthcare service provider accessibility and utilization may factor into determining readmissions, including patients’ use of primary care and post-acute care services. Other factors driving readmissions include social support, transportation, and medication compliance.

A good proxy for socioeconomic status is dual-eligible status and disproportionate hospital share (DSH) status

  • According to a CMS study on national Medicare readmission trends, the readmission rate between 2007 and 2010 for patients who are dually eligible for both Medicare and Medicaid was 23.8 percent while the readmission rate for non-dual-eligible patients was 17.3 percent.8
  • An analysis of the impact of the proposed readmission reductions on hospital cohorts conducted by the Association of American Medical Colleges found there is a demarcation line between the sixth and seventh deciles showing a significant increase in the number of high DSH hospitals that will have the maximum payment reduction under CMS’ proposed approach compared to low DSH hospitals.9

A better policy is needed that encourages quality without harming communities and patients

  • Since ample evidence exists that readmissions rates are higher for dual-eligible patients than for the general patient population, we believe incorporating dual-eligible status into CMS’ calculation for the readmission payment is a good proxy for the socioeconomic factors that influence readmissions. CMS would simply calculate the rates for dual-eligibles separately than for non-dual eligibles and then blend the two rates.
  • If CMS is unable to make an adjustment at the patient level for fiscal year 2013, it should make a hospital-level adjustment by segmenting facilities based on the DSH patient percentages. CMS should therefore make adjustments to its proposal to prevent safety-net hospitals, which are high DSH and serve a large volume of dual-eligible patients, from being disproportionately impacted by the readmissions policy.
  • Ultimately, a readmissions policy should be integrated into larger reforms such as value-based purchasing, accountable care organizations and bundling to incentivize the systematic reduction of preventable readmissions, similar to recommendations by the Medicare Payment Advisory Commission.

What’s being said?

“Although a focus on readmissions may have good face validity, we believe that policymakers’ emphasis on 30-day readmissions is misguided, for three reasons. First, the metric itself is problematic: only a small proportion of readmissions at 30 days after initial discharge are probably preventable, and much of what drives hospital readmission rates are patient- and community-level factors that are well outside the hospital’s control. Furthermore, it is unclear whether readmissions always reflect poor quality: high readmission rates can be the result of low mortality rates or good access to hospital care.” – Ashish K. Jha, M.D., New England Journal of Medicine


1 Buz Cooper, MD. “Geography, Poverty and Health Care,” October 24, 2009. Physicians and Health Care Reform Commentaries and Controversies.

2 Rathore SS, Masoudi FA, Wang Y, Curtis JP, Foody JM, Havranek EP, et al. Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure: findings from the National Heart Failure Project. Am Heart J. 2006; 152:371-8.

3 Buz Cooper, MD. January 5, 2010. “Readmission Legislation is the Wrong Answer to Health Care quality and Cost.”

4 Jiang, H. Joanna, Allison Russo, and Marguerite Barrett. Nationwide Frequency and Costs of Potentially Preventable Hospitalizations, 2006. Statistical Brief #72. Agency for Healthcare Research and Quality.

5 Brennan, Niall, Centers for Medicare & Medicaid Services, “National Medicare Readmission Findings: Recent Data and Trends Office of Information Products and Data Analytics” (presented at AcademyHealth 2011 Annual Research Meeting, Orland, FL, June 24-16, 2012).

6 Joseph S. Ross; Gregory K. Mulvey; Brett Stauffer; Vishnu Patlolla; Susannah M. Bernheim; Patricia S. Keenan; Harlan M. Krumholz. Statistical Models and Patient Predictors of Readmission for Heart Failure: A Systematic Review. Arch Intern Med, Jul 2008; 168: 1371 – 1386.

7 Bhalla, Rohit. “Could Medicare Readmission Policy Exacerbate Health Care System Inequity?” Annals of Internal Medicine (2009). 30 Nov. 2009.

8 Centers for Medicare & Medicaid Services, National Medicare Readmission Findings: Recent Data and Trends Office of Information Products and Data Analytics. Presented at AcademyHealth 2011 Annual Research Meeting.

9 Association of American Medical Colleges, May 11, 2012, comments to the “Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Fiscal Year 2013 Rates; Hospitals’ Resident Caps for Graduate Medical Education Payment Purposes…77 Fed. Reg. 27870,” June 25, 2012.