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Opportunities and Challenges for Reducing Hospital RevisitsOpportunities and Challenges for Reducing Hospital Revisits

Kumar Dharmarajan, MD, MBA; and Harlan M. Krumholz, MD, SM
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From Yale School of Medicine, New Haven, Connecticut.

Disclaimer: The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health.

Grant Support: By the National Institute on Aging and the American Federation for Aging Research through the Paul B. Beeson Career Development Awards in Aging Research Program (grant K23AG048331-01; Dr. Dharmarajan) and the National Heart, Lung, and Blood Institute (grant 1U01HL105270-05; Center for Cardiovascular Outcomes Research at Yale University [Dr. Krumholz]).

Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-0878.

Requests for Single Reprints: Harlan Krumholz, MD, SM, Center for Outcomes Research and Evaluation, 1 Church Street, Suite 200, New Haven, CT 06510; e-mail, harlan.krumholz@yale.edu.

Current Author Addresses: Drs. Dharmarajan and Krumholz: Center for Outcomes Research and Evaluation, 1 Church Street, Suite 200, New Haven, CT 06510.

Ann Intern Med. 2015;162(11):793-794. doi:10.7326/M15-0878
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Two articles in this issue explore the clinical epidemiology and predictors of hospital revisits. The editorialists raise questions about the preventability of revisits from Duseja and colleagues' research and note limitations of Graham and colleagues' study. They assert that incorporating knowledge of patients' underlying vulnerabilities after discharge is critical to reducing revisits.



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Authors' Response
Posted on June 2, 2015
Kelly L. Graham, MD, MPH, Michael D. Howell, MD, MPH, Edward R. Marcantonio, MD, SM
From the Divisions of General Medicine and Primary Care (Graham and Marcantonio) and Gerontology (Marcantonio), Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Bo
Conflict of Interest: None Declared
We appreciate Drs. Dharmarajan’s and Krumholz’s thought-provoking comments about our paper, and its implications for patients and hospitals. In response, we would like to emphasize a few points. For several years, investigators have criticized the 30-day window used to determine hospital readmissions penalties, [1] suggesting that shorter windows reflect “more preventable” readmissions.[2] Our approach was different. We have learned in many diseases that the causes of poor outcomes differ over time, and correspondingly that treatments for these diseases need to change over time too. For example, early treatment for stroke differs profoundly from later treatment. Early causes of death from myocardial infarction differ from those that are later. Why should readmissions be any different? Could the pathophysiology of early and late readmissions differ? Thus, the over-arching goal of this study was to explore whether a shift occurs in predictors of readmission over the 30-day period following hospital discharge. Despite the limitations acknowledged in our article and in the accompanying editorial, we believe our data makes this case convincingly.

We chose to use data from a single medical center because it allowed us to address our study question with more clinical detail than through large administrative databases. This enabled us to examine new variables not previously considered in readmission analyses. For example, as the editorial points out, intensive care unit (ICU) stay was not an independent predictor of early readmission in our multivariable models. However, triggering of a rapid response team during hospitalization was associated with early readmission. This variable likely subsumed ICU admission, plus included the important population of unstable patients who were never admitted to the ICU. Notably, this variable incorporates the patient’s acute physiological derangement, and the clinicians’ thought process, and likely provides a better measure of vulnerability than ICU admission alone. Our approach also enabled us to examine actionable variables such as suboptimal discharge time, which was a significant predictor of early readmission and provides a direct opportunity for process improvement. We agree that other variables, such as the physician having access to the hospital electronic medical record being a predictor of readmission, are more difficult to explain. Perhaps physicians who are linked to the hospital system simply recognize the need for readmission sooner, and so accelerate the time course of unpreventable readmissions. Our study does not answer this question, but generates new, testable questions.

While we recognize that our single center study design is a limitation, we describe in detail the thought processes behind this approach in our paper. Moreover, we limited our analysis to patients who received primary care within this system to limit “leakage” of re-admissions to outside hospitals. It would have been very difficult to achieve our study goals in a multi-center study, where access to highly detailed variables would introduce problems with heterogeneity and internal validity. We are currently in the process of addressing our question using prospective data collected uniformly from multiple hospitals.

In conclusion, the concept of 30-day unplanned readmission has become established as a standard metric of inpatient quality in the U.S. healthcare system. Yet, this long time window encompasses a broad target for quality improvement, and elucidating how risk factors for readmission change over time can provide insights as to how to intervene most effectively throughout this time period. We agree that simply dividing the 30 day window is not the solution. Rather, our paper represents a start to understanding how patients’ vulnerability changes over time. Just as the pathophysiology of disease can differ in early and late periods, our study provides evidence that the pathophysiology of early and late readmissions is different.

1. Joynt KE, Jha AK. Thirty-day readmissions--truth and consequences. N Engl J Med. 2012;366(15):1366-1369.
2. van Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions. CMAJ. 2011;183(14):E1067-1072.
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