Queuing Theory to Guide the Implementation of a Heart Failure Inpatient Registry Program

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.