Hospital readmission as an accountability measure.

IN JUNE 2009, THE CENTERS FOR MEDICARE & MEDICAID Services (CMS) began publicly reporting on its Hospital Compare Web site all hospitals’ 30-day readmission rates for patients hospitalized and discharged with pneumonia, acute myocardial infarction (AMI), or heart failure (HF). This “shaming” of some hospitals and research showing that about 1 in 5 Medicare patients is readmitted within 30 days after hospital discharge highlighted the problem of hospital care transitions. With passage of the Patient Protection and AffordableCareAct,CMSwillbeginholdinghospitalsaccountable for their readmission rates and adjusting payments to hospitals in 2013 according to their rate of “excess” vs “expected” Medicare readmissions for pneumonia, AMI, and HF. Hospitals will now be subject to both the “shame” of public reporting and the likely more potent “stick” of decreased reimbursement. It is essential to consider these new policies in light of their likelihoodof successandtheirpotential fornegativeunintended effects as the health care industry attempts to address presumably elevated hospital readmission rates. The Medicare Payment Advisory Commission identified pneumonia, HF, and AMI as common, costly causes of hospitalization, with a high proportion of potentially preventable readmissions, and recommended that Congress and CMS implement policies to address high rates. Proposed solutions included differential reimbursement for lowvs highperforming facilities and the bundling of inpatient and outpatient payments in an effort to better align performance incentives. These efforts led to inclusion of mandates in the Patient Protection and Affordable Care Act to reduce hospital readmissions by using risk-adjusted measures currently endorsed by the National Quality Forum and CMS. CMS aims to apply the rates from these 3 conditions to payments for all diagnosis related groups and expand the measurement to include chronic obstructive pulmonary disease, coronary artery bypass graft, percutaneous transluminal coronary angioplasty, and other vascular conditions in 2015. Hospital readmissions and their preventability have been the subject of intense study, and their utility as a performance measure is often debated. One early study using chart review estimated that 1 of 7 readmissions for patients with diabetes, 1 of 5 readmissions for patients with HF, and 1 of 12 readmissions for patients with obstructive lung disease were attributable to substandard care. According to a systematic review of 7 studies, low-quality inpatient care was associated with a 55% increase in risk for early hospital readmission. However, these analyses lack precision. Different reviewers have generated widely varying estimates of care quality, with estimates of readmission preventability ranging from 9% to 48%. Even different analysts examining the same data sets have reached differing conclusions. Such wide variability in estimates undermines the potential validity of using readmission rates in determining hospital reimbursement. Accountability measures should have a strong evidence base for their validity, should accurately measure whether high-quality care has been provided, and should have a low risk for unintended consequences. Some have observed that high readmission rates may be associated with reduced mortality and reflect severity of illness and actually high acuity of care. Several groups, including the developers of the CMS models, have used regression techniques with complex analyses of administrative data to estimate the proportion of potentially avoidable hospital readmissions. Using 2005 Medicare data, the Medicare Payment Advisory Commission estimated that 13.3% of 30-day hospital readmissions were potentially preventable and cost an additional $12 billion. However, the proprietary nature of this particular methodology prevents others from assessing its validity. The nonproprietary CMS algorithms involve hierarchical logistic regression modeling to calculate hospital risk-standardized 30-day allcause readmission rates. These models include 2 demographic variables (age, sex) and use more than 20 International Classification of Diseases, Ninth Revision codes to adjust for baseline differences in severity of illness at hospital presentation that might influence readmission risk. Although the CMS model has been validated against chart-review methods and compares favorably, both sets of methods fail to account for a large degree of the variance observed in hospital readmissions. For example, the C statistic for the HF model is 0.6, only modestly better than chance. This makes sense, given the number of conceptual factors influencing readmission risk