7-Day Readmissions: Better Indicators of Patient Care

Nicola M. Parry, DVM

October 04, 2016

Seven-day readmission rates may be a better indicator of hospital quality, than the typical 30-day measure often used, according to a study published online October 4 in Health Affairs.

"The hospital-level quality signal captured in readmission risk was highest on the first day after discharge and declined rapidly until it reached a nadir at seven days," write David L. Chin, PhD, from the University of California, Davis, in Sacramento, California, and colleagues. "Shorter intervals of seven or fewer days might improve the accuracy and equity of readmissions as a measure of hospital quality for public accountability."

In the United States, healthcare systems have adopted 30-day readmission rates as indicators of quality of patient care in hospitals. However, limited evidence exists to support this particular interval, and many experts remain skeptical about its use for all conditions and procedures.

Dr Chin and colleagues therefore conducted a study to determine how the variation in readmission risk at the hospital level changes over time after discharge, independent of patient characteristics. They calculated the intracluster correlation coefficient (ICC) as a measure of the hospital quality signal across a range of postdischarge time intervals and conditions.

The researchers used data from State Inpatient Databases and State Emergency Department Databases from Arizona (2005 - 2007), California (2005 - 2011), Florida (2005 - 2011), and New York (2006 - 2011).

They included patients discharged from any nonfederal hospital in these four states, evaluating data for three hospital encounter-based cohorts (acute myocardial infarction [AMI], heart failure [HF], and pneumonia) and a composite of five hospital-wide, all-cause, unplanned readmission cohorts (medicine, surgery and gynecology, cardiorespiratory, cardiovascular, and neurology).

Hospital encounters were eligible for inclusion in the study if the patient were discharged alive at age 65 years or older. Encounters that occurred in psychiatric, rehabilitation, long-term care, or prospective payment system–exempt cancer hospitals were excluded.

The researchers evaluated 15,212,575 index hospitalizations for 6,768,057 unique patients at 910 hospitals. With respect to all hospital-wide all-cause, unplanned readmission measures, each hospital had a median of 10,942 encounters (interquartile range, 2904 - 24,796). According to Dr Chin and colleagues, similar patterns were found across all states and diagnoses.

Unplanned readmissions accounted for 90.1% of all-cause hospital readmissions, and 30-day unplanned readmission rates were 17.5% for AMI, 23.6% for HF, 17.6% for pneumonia, and 15.5% for hospital-wide, all-cause, unplanned readmissions. Considering the unplanned readmissions, 30-day unplanned readmission rates were 17.1% for medicine, 11.3% for surgery and gynecology, 20.6% for cardiorespiratory, 12.5% for cardiovascular, and 13.7% for neurology.

For the three specific conditions, the researchers found that the ICC decreased rapidly, from 2.7% (AMI), 1.6% (HF), and 3.2% (pneumonia) on day 1 postdischarge to less than 1.0% in all three cohorts by day 4, finally reaching a minimum of 0.8% or less by day 7 postdischarge. Across these measures, the hospital quality signal decreased substantially by day 7 postdischarge: from day 1 to day 7 postdischarge, the ICC decreased by 78%, 49%, and 76% among patients admitted with AMI, HF, and pneumonia, respectively.

At the traditional 30-day postdischarge point, hospital-level variation was low for all patient cohorts, ranging from 1.1% for surgery to 0.8% for pneumonia. Hospital-level variation was also consistently and markedly higher within the first few days postdischarge (as high as up to 3.2% for the pneumonia cohort on day 1) and reached a nadir at 7 days.

The optimal interval for capturing hospital-level variation in the risk for readmission also varied across conditions, with the AMI cohort showing the greatest increase in hospital-level variation after day 10 postdischarge.

In addition, using current risk-standardized readmission models developed by the Centers for Medicare & Medicaid Services, which adjust only for patient age, sex, and clinical characteristics, the investigators found increasing hospital ICCs with longer ascertainment periods from day 7 to day 90 postdischarge. However, this effect substantially decreased after adjusting for factors such as state, rural–urban designation, and median household income.

The rapid dissipation in the hospital quality signal suggests that most readmissions after day 7 postdischarge were a result of community- and household-level factors outside the control of hospitals, the authors explain.

According to the authors, "[o]ur finding that the hospital quality signal is higher in the first five days after discharge than at longer time periods, such as thirty days, suggests that hospitals' practices with respect to care coordination and postdischarge follow-up could have the greatest impact within the first few days after discharge."

They note that this is consistent with previous studies showing that clinical stability on discharge is a major predictor of early readmissions after medical hospitalizations, and that early follow-up appointments after discharge might reduce the readmission risk.

"If the goal of current public policy is to encourage hospitals to assume responsibility for postdischarge adherence and primary care follow-up, then penalties assessed for readmissions within thirty days or longer periods might align appropriately," the authors write. "However, if the goal is empowering patients and families to make health care choices informed by true differences in hospital performance, then a readmission interval of seven days or fewer might be more accurate and equitable."

One of the authors is supported by the Agency for Healthcare Research and Quality. Another author is partially supported by the National Center for Advancing Translational Sciences, National Institutes of Health.

Health Aff. Published online October 4, 2016. Abstract

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