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Figure 1.
Excess 30-Day Readmission vs Excess 30-Day Combined Mortality and Readmission
Excess 30-Day Readmission vs Excess 30-Day Combined Mortality and Readmission

The top left quadrant represents a low readmission, high combined mortality and readmission population (ERRAgg <1, ECORAgg >1); the bottom right quadrant represents a high readmission, low combined mortality and readmission population (ERRAgg >1, ECORAgg <1). ECORAgg indicates excess combined outcome ratio; ERRAgg, excess readmission ratio.

Figure 2.
Readmission Penalty vs Concordance of Readmission and Combined Mortality and Readmission
Readmission Penalty vs Concordance of Readmission and Combined Mortality and Readmission

For the readmission low, high combined mortality and readmission population, the ERRAgg was less than 1 and the ECORAgg was more than 1. For the readmission high, low combined mortality and readmission population, the ERRAgg was greater than 1 and the ECORAgg was less than 1. DRG indicates diagnosis-related group; ECORAgg, excess combined outcome ratio; ERRAgg, excess readmission ratio.

1.
Krumholz  HM, Lin  Z, Keenan  PS,  et al.  Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia.  JAMA. 2013;309(6):587-593.PubMedGoogle ScholarCrossref
2.
Medicare.gov. Hospital Compare Datasets. https://data.medicare.gov//data/hospital-compare. Accessed April 17, 2016.
3.
Medicare Readmission Penalties by Hospital. Kaiser Health News. https://kaiserhealthnews.files.wordpress.com/2013/08/readmission-year-2-data.csv. Accessed April 17, 2016.
4.
Medicare.gov. Hospital Compare. Linking quality to payment. https://www.medicare.gov/hospitalcompare/linking-quality-to-payment.html. Accessed July 8, 2016.
5.
Centers for Medicare and Medicaid Services. Hospital Quality Initiative Measure Methodology. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology.html. Accessed July 15, 2016.
6.
McCrum  ML, Joynt  KE, Orav  EJ, Gawande  AA, Jha  AK.  Mortality for publicly reported conditions and overall hospital mortality rates.  JAMA Intern Med. 2013;173(14):1351-1357.PubMedGoogle ScholarCrossref
7.
Joynt  KE, Jha  AK.  Thirty-day readmissions—truth and consequences.  N Engl J Med. 2012;366(15):1366-1369.PubMedGoogle ScholarCrossref
8.
Bradley  EH, Curry  L, Horwitz  LI,  et al.  Contemporary evidence about hospital strategies for reducing 30-day readmissions: a national study.  J Am Coll Cardiol. 2012;60(7):607-614.PubMedGoogle ScholarCrossref
9.
Vidic  A, Chibnall  JT, Hauptman  PJ.  Heart failure is a major contributor to hospital readmission penalties.  J Card Fail. 2015;21(2):134-137.PubMedGoogle ScholarCrossref
10.
US Department of Health & Human Services. New HHS Data Shows Major Strides Made in Patient Safety, Leading to Improved Care and Savings. https://innovation.cms.gov/Files/reports/patient-safety-results.pdf. Accessed July 8, 2016.
11.
van Walraven  C, Bennett  C, Jennings  A, Austin  PC, Forster  AJ.  Proportion of hospital readmissions deemed avoidable: a systematic review.  CMAJ. 2011;183(7):E391-E402.PubMedGoogle ScholarCrossref
12.
Gorodeski  EZ, Starling  RC, Blackstone  EH.  Are all readmissions bad readmissions?  N Engl J Med. 2010;363(3):297-298.PubMedGoogle ScholarCrossref
13.
Centers for Disease Control and Prevention. Recent Trends in Heart Failure-related Mortality: United States, 2000–2014. http://www.cdc.gov/nchs/products/databriefs/db231.htm. Accessed July 8, 2016.
14.
Fonarow  GC, Alberts  MJ, Broderick  JP,  et al.  Stroke outcomes measures must be appropriately risk adjusted to ensure quality care of patients: a presidential advisory from the American Heart Association/American Stroke Association.  Stroke. 2014;45(5):1589-1601.PubMedGoogle ScholarCrossref
15.
Feemster  LC, Au  DH.  Penalizing hospitals for chronic obstructive pulmonary disease readmissions.  Am J Respir Crit Care Med. 2014;189(6):634-639.PubMedGoogle ScholarCrossref
16.
Göhler  A, Geisler  BP, Manne  JM,  et al.  Utility estimates for decision-analytic modeling in chronic heart failure--health states based on New York Heart Association classes and number of rehospitalizations.  Value Health. 2009;12(1):185-187.PubMedGoogle ScholarCrossref
Brief Report
February 2017

Association Between Medicare Hospital Readmission Penalties and 30-Day Combined Excess Readmission and Mortality

Author Affiliations
  • 1University of Michigan Health System, Ann Arbor
  • 2Ann Arbor Veterans Affairs Health System, Ann Arbor, Michigan
JAMA Cardiol. 2017;2(2):200-203. doi:10.1001/jamacardio.2016.3704
Key Points

Question  How would Medicare financial penalties for US hospitals change if policy equally weighted 30-day readmissions and mortality, rather than 10-fold in favor of preventing readmissions?

Findings  Using publicly available hospital data, penalties for one-third of hospitals would have substantially changed if 30-day readmissions and mortality were weighted equally.

Meaning  Current Medicare policy does not meet the goals of aligning incentives and fairly reimbursing hospitals for patient-centered outcomes.

Abstract

Importance  US hospitals receive financial penalties for excess risk–standardized 30-day readmissions and mortality in Medicare patients. Under current policy, readmission prevention is incentivized over 10-fold more than mortality reduction.

Objective  To determine how penalties for US hospitals would change if policy equally weighted 30-day readmissions and mortality.

Design, Setting, and Participants  Publicly available hospital-level data for fiscal year 2014 was obtained, including excess readmission ratio (ERR; risk-standardized predicted over expected 30-day readmissions) and 30-day mortality rates for heart failure, pneumonia, and acute myocardial infarction, as well as readmission penalties (as percent of Medicare Diagnosis Group payments). An excess mortality ratio (EMR) was calculated by dividing the risk-standardized predicted mortality by the national average mortality. Case-weighted aggregate ERR (ERRAGG) and EMR (EMRAGG) were calculated, and an excess combined outcome ratio (ECORAGG) was created by averaging ERRAGG and EMRAGG. We examined associations between readmission penalties, ERRAGG, EMRAGG, and ECORAGG. Analysis of variance was used to compare readmission penalties in hospitals with concordant (both ratios >1 or <1) and discordant performance by ERRAGG and ECORAGG.

Main Outcomes and Measures  The primary outcome investigated was the association between readmission penalties and the calculated excess combined outcome ratio (ECORAGG).

Results  In 1963 US hospitals with complete data, readmission penalties closely tracked excess readmissions (r = 0.81; P < .001), but were minimally and inversely related with excess mortality (r = −0.12; P < .001) and only modestly correlated with excess combined readmission and mortality (r = 0.36; P < .001). Using hospitals with concordant ERRAGG and ECORAGG as the reference group, 17% of hospitals had an ECORAGG ratio less than 1 (ie, superior combined mortality/readmission outcome) with an ERRAGG ratio greater than 1, and received higher mean (SD) readmission penalties (0.41% [0.28%] vs 0.29% [0.37%]; P < .001); 16% of US hospitals had an ECORAGG ratio of greater than 1 (ie, inferior combined mortality/readmission outcome) with an ERRAGG ratio less than 1, and received minimal mean (SD) readmission penalties (0.08% [0.12%]; P < .001 for comparison with reference).

Conclusions and Relevance  In fiscal year 2014, financial penalties for one-third of US hospitals would have been substantially altered if 30-day readmission and mortality were considered equally important. Under most circumstances, patients would rather avoid death than rehospitalization. Current Medicare financial penalties do not meet the goals of aligning incentives and fairly reimbursing hospitals for patient-centered outcomes.

Introduction

In fiscal year (FY) 2013, the Centers for Medicare & Medicaid Services (CMS) began penalizing hospitals in the United States under the Hospital Readmissions Reduction Program for excess risk–standardized 30-day readmissions in Medicare patients with heart failure (HF), pneumonia (PNA), and acute myocardial infarction (AMI). In FY 2014, the Hospital Value-Based Purchasing Program began penalizing hospitals with higher than expected risk–standardized 30-day mortality for these diagnoses. Readmission and death are competing but weakly correlated outcomes, leading to the belief that hospitals with low mortality are not more likely to receive readmission penalties.1

Currently, unbalanced incentives favor readmission prevention over mortality reduction. Patients who die within 30 days without rehospitalization are excluded from readmission metrics. Moreover, financial penalties are more than 10-fold greater for readmission than for mortality. We investigated how hospital penalties might change if 30-day readmission and mortality were weighted equally.

Methods

Hospital-level data for FY 2014 were obtained from Medicare.gov and Kaiser Health News.2,3 Data included excess readmission ratio (ERR; ratio of risk-standardized predicted to expected 30-day readmission) and 30-day risk-standardized mortality rates for HF, PNA, and AMI (each calculated by CMS as a 3-year average), and FY 2014 readmission penalties (as % diagnosis related group [DRG] payments). Fiscal year 2014 was chosen for analysis because additional diagnoses were added to FY 2015 and FY 2016 readmission metrics without corresponding penalties for mortality.

An aggregate ERR for each hospital, weighted by the number of cases, was calculated:

ERRAGG = [(#Cases HF × ERRHF) + (#Cases PNA × ERRPNA) + (#Cases AMI × ERRAMI)]/Total #Cases.

An excess mortality ratio (EMR) was calculated for each condition by dividing the risk-standardized predicted mortality by the national average mortality, with a case-weighted hospital aggregate EMR (EMRAGG) derived analogously to ERRAGG:

EMRAGG = [(#CasesHF × EMRHF) + (#CasesPNA × EMRPNA) + (#CasesAMI × EMRAMI)]/Total #Cases.

We created an excess combined outcome ratio (ECORAGG) by averaging excess readmission and mortality:

ECORAGG = (ERRAGG + EMRAGG)/2.

We examined associations between readmission penalties, ERRAGG, EMRAGG, and ECORAGG. We used analysis of variance to compare readmission penalties in hospitals with concordant and discordant performance by ERRAGG and ECORAGG. Because the decision to equally weight mortality and readmission was arbitrary (and conservative), we performed sensitivity analyses with mortality weighted 2 and 5 times as much as readmission. Analyses were performed using Stata version 10.0 (StataCorp LP).

Results

In 1963 US hospitals with complete data for analysis, FY 2014 readmission penalties (0%-2% of DRG reimbursement) closely tracked excess readmissions (r = 0.81; P < .001), but were minimally and inversely associated with excess mortality (r = −0.12; P < .001) and only modestly correlated with excess combined readmission and mortality (r = 0.36; P < .001). The associations between condition-specific excess readmission and mortality were weak (HF: r = –0.21; P < .001) or absent (PNA: r < 0.001; P = .99 and AMI: r = 0.002; P = .94) and similar to those reported in previous Medicare cohorts for these diagnoses.1

As shown in both Figure 1 and Figure 2, 17% of hospitals had an ERRAGG ratio greater than 1 and received readmission penalties, yet had an ECORAGG ratio of less than 1 (ie, superior combined outcomes if mortality and readmission were considered equally important). Conversely, 16% of hospitals had an ERRAGG ratio less than1 and received little or no readmission penalty, yet had an ECORAGG ratio greater than 1 (ie, inferior combined outcomes).

With mortality weighted twice as much as readmission, 21% of hospitals had an ERRAGG ratio greater than 1 and an ECORAGG ratio less than 1, and 23% of hospitals had an ERRAGG ratio less than 1 and an ECORAGG ratio greater than 1. With mortality weighted 5 times as much as readmission, 24% of hospitals had an ERRAGG ratio greater than 1 and an ECORAGG ratio less than 1, and 27% of hospitals had an ERRAGG ratio less than 1 and an ECORAGG ratio greater than 1. In other words, in FY 2014 more than half of US hospitals would have been misclassified for CMS penalties if death were considered 5 times more important than readmission.

Discussion

For FY 2014, CMS financial penalties for one-third of US hospitals would likely have been substantially different if the methodology equally weighted 30-day readmission and mortality. These discrepancies increased as mortality was weighted more heavily.

The Hospital Readmission Reduction Program, implemented in 2012 as part of the Patient Protection and Affordable Care Act, was designed to “improve quality of care… by incentivizing the reduction of hospital readmissions” and “reward hospitals for delivering services of higher value.”4 Mortality risk-standardization for HF, PNA, and MI is reasonably accurate,5 and publicly reported mortality for these conditions reflects hospital mortality rates for other diagnoses.6 By contrast, the discrimination of readmission risk models is low,5 and 30-day readmission rates reflect the sociodemographic composition of the population served more than the quality of care delivered or severity of illness.7

Financial penalties for excess 30-day readmissions, which cost some hospitals millions of dollars per year, clearly affect resource usage. Shortly after the advent of the Hospital Readmission Reduction Program, nearly 9 in 10 US hospitals reported creating quality improvement teams to reduce HF rehospitalizations,8 the primary driver of CMS readmission penalties.9 Some benefits have clearly accrued from this effort, as discharged patients now receive better coordination of care transitions, and 30-day readmissions have slightly decreased.10 Yet relatively few readmissions are truly preventable,11 and less attention has been paid the other side of the equation: What if some readmissions are appropriate and necessary to prevent deaths?12 While the rates are not directly comparable, it is interesting to note that, after more than a decade of steady decline, age-adjusted mortality rates for US patients with HF have recently increased at the same time 30-day readmission rates decreased.10,13

Health care performance metrics are proliferating rapidly and are increasingly used to incentivize provider and system behavior. This is occurring without full consideration of misaligned incentives and the potential for unintended consequences. In FY 2016 the maximum penalty for high 30-day mortality rates is 0.2% of DRG payments, compared with 3.0% maximum DRG penalty for excess 30-day readmissions. Chronic obstructive pulmonary disease and stroke now also appear on the list of nonsurgical diagnoses penalized for excess 30-day readmissions. Similar concerns have been raised related to insufficient risk standardization and lack of evidence on how to prevent readmissions for these conditions,14,15 and there are no corresponding penalties for excess 30-day mortality.

Conclusions

The concept of quality-adjusted life years is a widely accepted method of assessing and improving health care decision making, and is based on patient-derived health utility of various states compared with death or perfect wellness. Again considering the case of hospitalized patients with HF, even surviving 3 or more cardiovascular rehospitalizations has relatively little negative effect on reported health utility.16 Put another way, under most circumstances, hospitalized patients would much rather avoid death than readmission. In the coming years, hospital financial penalties for readmissions will continue to overshadow those for mortality. If the goal of federal regulation is to align incentives and fairly reimburse hospitals for patient-centered outcomes, current CMS policy does not reflect these aims.

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Article Information

Corresponding Author: Scott L. Hummel, MD, MS, University of Michigan Frankel Cardiovascular Center, 1500 E Medical Center Dr, SPC 5853, Ann Arbor, MI 48109-5853 (scothumm@med.umich.edu).

Accepted for Publication: August 8, 2016.

Published Online: October 26, 2016. doi:10.1001/jamacardio.2016.3704

Author Contributions: Dr Hummel had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Abdul-Aziz, Aaronson, Hummel.

Acquisition, analysis, or interpretation of data: All Authors.

Drafting of the manuscript: Abdul-Aziz, Hummel.

Critical revision of the manuscript for important intellectual content: All Authors.

Statistical analysis: Abdul-Aziz, Hayward, Hummel.

Study supervision: Abdul-Aziz, Aaronson.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and Dr Hummel received salary support from the National Institutes of Health National Heart, Lung, and Blood Institute (grant K23-109176). No other conflicts are reported.

Role of the Funder/Sponsor: The funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Krumholz  HM, Lin  Z, Keenan  PS,  et al.  Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia.  JAMA. 2013;309(6):587-593.PubMedGoogle ScholarCrossref
2.
Medicare.gov. Hospital Compare Datasets. https://data.medicare.gov//data/hospital-compare. Accessed April 17, 2016.
3.
Medicare Readmission Penalties by Hospital. Kaiser Health News. https://kaiserhealthnews.files.wordpress.com/2013/08/readmission-year-2-data.csv. Accessed April 17, 2016.
4.
Medicare.gov. Hospital Compare. Linking quality to payment. https://www.medicare.gov/hospitalcompare/linking-quality-to-payment.html. Accessed July 8, 2016.
5.
Centers for Medicare and Medicaid Services. Hospital Quality Initiative Measure Methodology. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology.html. Accessed July 15, 2016.
6.
McCrum  ML, Joynt  KE, Orav  EJ, Gawande  AA, Jha  AK.  Mortality for publicly reported conditions and overall hospital mortality rates.  JAMA Intern Med. 2013;173(14):1351-1357.PubMedGoogle ScholarCrossref
7.
Joynt  KE, Jha  AK.  Thirty-day readmissions—truth and consequences.  N Engl J Med. 2012;366(15):1366-1369.PubMedGoogle ScholarCrossref
8.
Bradley  EH, Curry  L, Horwitz  LI,  et al.  Contemporary evidence about hospital strategies for reducing 30-day readmissions: a national study.  J Am Coll Cardiol. 2012;60(7):607-614.PubMedGoogle ScholarCrossref
9.
Vidic  A, Chibnall  JT, Hauptman  PJ.  Heart failure is a major contributor to hospital readmission penalties.  J Card Fail. 2015;21(2):134-137.PubMedGoogle ScholarCrossref
10.
US Department of Health & Human Services. New HHS Data Shows Major Strides Made in Patient Safety, Leading to Improved Care and Savings. https://innovation.cms.gov/Files/reports/patient-safety-results.pdf. Accessed July 8, 2016.
11.
van Walraven  C, Bennett  C, Jennings  A, Austin  PC, Forster  AJ.  Proportion of hospital readmissions deemed avoidable: a systematic review.  CMAJ. 2011;183(7):E391-E402.PubMedGoogle ScholarCrossref
12.
Gorodeski  EZ, Starling  RC, Blackstone  EH.  Are all readmissions bad readmissions?  N Engl J Med. 2010;363(3):297-298.PubMedGoogle ScholarCrossref
13.
Centers for Disease Control and Prevention. Recent Trends in Heart Failure-related Mortality: United States, 2000–2014. http://www.cdc.gov/nchs/products/databriefs/db231.htm. Accessed July 8, 2016.
14.
Fonarow  GC, Alberts  MJ, Broderick  JP,  et al.  Stroke outcomes measures must be appropriately risk adjusted to ensure quality care of patients: a presidential advisory from the American Heart Association/American Stroke Association.  Stroke. 2014;45(5):1589-1601.PubMedGoogle ScholarCrossref
15.
Feemster  LC, Au  DH.  Penalizing hospitals for chronic obstructive pulmonary disease readmissions.  Am J Respir Crit Care Med. 2014;189(6):634-639.PubMedGoogle ScholarCrossref
16.
Göhler  A, Geisler  BP, Manne  JM,  et al.  Utility estimates for decision-analytic modeling in chronic heart failure--health states based on New York Heart Association classes and number of rehospitalizations.  Value Health. 2009;12(1):185-187.PubMedGoogle ScholarCrossref
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