Research Details

Abstract

Timely follow-up in clinic after heart failure hospitalization represents an evidence-based intervention associated with reduced rehospitalization.1 Major cardiovascular societies endorse a 7-day follow-up visit as an appropriate target for quality.2 Yet the rate of scheduled follow-up visits remains relatively low, at ?65% in 2012 by registry data.3 Even more striking is the rate of arrived follow-up visits: 30% in 7 days.3 This represents a substantial missed opportunity to address and a likely explanation for ongoing avoidable readmissions. We took an unconventional approach to improving clinic scheduling policies by collaborating with our colleagues at the Northwestern University Kellogg School of Management to implement queuing theory as a novel approach to address a previously unyielding problem. In 2015, as part of a multidisciplinary intervention to improve outcomes for hospitalized heart failure patients, we systematically identified all patients within our hospital at risk for heart failure-related readmissions through a daily enterprise data warehouse screen4; developed a multidisciplinary bridge and transition team to engage patients during the index hospitalization; and then deployed queuing theory to first assess and then improve clinic follow-up. Queuing theory is the mathematical study of waiting times.5,6 With roots in the telecommunications field, it has widespread applications in several processes such as understanding supermarket lines and managing factory inventory. A particularly powerful insight arising from queuing theory is the notion that extra capacity, or a capacity buffer, is necessary to ensure system performance when variable demand arises, such as for a hospital discharge clinic. We opted to use queuing theory to analyze hospital discharge load and understand the capacity needed in clinic to reduce wait times and improve access. Here, we provide our mathematical analysis based on real-world practice; the results of our intervention; and an online calculator (http://www.hfresearch.org) for other groups seeking to redesign access using our model.

Type

Article

Author(s)

R Kannan Mutharasan, Itai Gurvich, Jan A. Van Mieghem, Clyde Yanci, Faraz S Ahmad

Date Published

2018

Citations

Mutharasan, R Kannan, Itai Gurvich, Jan A. Van Mieghem, Clyde Yanci, and Faraz S Ahmad. 2018. Reducing 30-day Heart Failure Readmissions--a Novel Solution Using Queuing Theory: "Buffer or Suffer.". Circulation: Cardiovascular Quality and Outcomes.(7): 1-5.