Queuing Theory to Guide the Implementation of a Heart Failure Inpatient Registry Program
- Adrian H Zai, MD, PhD, MPHa,
- Kit M Farr, MDd,
- Richard W Grant, MD, MPHe,
- Elizabeth Mort, MD, MPHb,c,f,
- Timothy G Ferris, MD, MPHb,f,
- Henry C Chueh, MD, MSa,c
- aLaboratory of Computer Science, Massachusetts General Hospital, Boston, MA
- bMassachusetts General Physicians Organization, Massachusetts General Hospital, Boston, MA
- cCenter for Quality and Safety, Massachusetts General Hospital, Boston, MA
- dCardiovascular Health Center, Newton-Wellesley Hospital, Boston, MA
- eLaboratory of Computer Science, Boston, MA
- fPartners Healthcare, Boston, MA
- Correspondence: Adrian H. Zai, M.D., Ph.D., M.P.H., Laboratory of Computer Science, Massachusetts General Hospital, 50 Staniford Street, Suite 750, Boston, MA 02114; e-mail: azai{at}partners.org
- Received 24 August 2008
- Accepted 2 March 2009
Abstract
Objective The authors previously implemented an electronic heart failure registry at a large academic hospital to identify heart failure patients and to connect these patients with appropriate discharge services. Despite significant improvements in patient identification and connection rates, time to connection remained high, with an average delay of 3.2 days from the time patients were admitted to the time connections were made. Our objective for this current study was to determine the most effective solution to minimize time to connection.
Design We used a queuing theory model to simulate 3 different potential solutions to decrease the delay from patient identification to connection with discharge services.
Measurements The measures included average rate at which patients were being connected to the post discharge heart failure services program, average number of patients in line, and average patient waiting time.
Results Using queuing theory model simulations, we were able to estimate for our current system the minimum rate at which patients need to be connected (262 patients/mo), the ideal patient arrival rate (174 patients/mo) and the maximal patient arrival rate that could be achieved by adding 1 extra nurse (348 patients/mo).
Conclusions Our modeling approach was instrumental in helping us characterize key process parameters and estimate the impact of adding staff on the time between identifying patients with heart failure and connecting them with appropriate discharge services.
Footnotes
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Funding: Internal.
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Competing interests: None.
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Meetings: Portions of this work were presented in abstract form at the American Medical Informatics Association Spring Congress in Phoenix, Arizona, on May 30, 2008 (poster presentation).
