Solving the negative impact of congestion in the postanesthesia care unit: a cost of opportunity analysis Alejandro Ruiz-Patin˜ o, MD, a,b, * Laura Elena Acosta-Ospina, MD, b and Juan-David Rueda, MD, MS, PhD(c) c a Department of Pharmaceutical Health Services Research, Hospital Universitario San Ignacio, Bogota´, Colombia b Research Department, Pontificia Universidad Javeriana, Bogota´, Colombia c University of Maryland School of Pharmacy, Baltimore, Maryland article info Article history: Received 31 January 2016 Received in revised form 23 October 2016 Accepted 2 November 2016 Available online 10 November 2016 Keywords: Health services Optimization Cost of opportunity abstract Background: Congestion in the postanesthesia care unit (PACU) leads to the formation of waiting queues for patients being transferred after surgery, negatively affecting hospital resources. As patients recover in the operating room, incoming surgeries are delayed. The purpose of this study was to establish the impact of this phenomenon in multiple settings. Methods: An operational mathematical study based on the queuing theory was performed. Average queue length, average queue waiting time, and daily queue waiting time were evaluated. Calculations were based on the mean patient daily flow, PACU length of stay, occupation, and current number of beds. Data was prospectively collected during a period of 2 months, and the entry and exit time was recorded for each patient taken to the PACU. Data was imputed in a computational model made with MS Excel. To account for data uncertainty, deterministic and probabilistic sensitivity analyses for all dependent variables were performed. Results: With a mean patient daily flow of 40.3 and an average PACU length of stay of 4 hours, average total lost surgical opportunity time was estimated at 2.36 hours (95% CI: 0.36-4.74 hours). Cost of opportunity was calculated at $1592 per lost hour. Sensitivity analysis showed that an increase of two beds is required to solve the queue formation. Conclusions: When congestion has a negative impact on cost of opportunity in the surgical setting, queuing analysis grants definitive actions to solve the problem, improving quality of service and resource utilization. ª 2016 Elsevier Inc. All rights reserved. Introduction Patient flow is one of the keystones of hospital function. It is determined by overall patient admission and discharge rates, staff availability, patient transporters, and service complexity. 1,2 As a determinant of hospital revenues and service quality, inefficient flow is one of the most important issues to correct. The setting for this study is the Hospital Universitario San Ignacio, a third level university hospital located in Bogota Colombia. Hospital Universitario San Ignacio serves as teaching facility for students, both in medical school and * Corresponding author. Department of Pharmaceutical Health Services Research, Hospital Universitario San Ignacio, Carrera 7 Nr. 40-62, Bogota´ , Colombia. Tel.: þ57 1 3208320x2808; fax: þ57 1 3208320x2809. E-mail address: Alejandro.ruiz.pat@gmail.com (A. Ruiz-Patin˜ o). Available online at www.sciencedirect.com ScienceDirect journal homepage: www.JournalofSurgicalResearch.com journal of surgical research  april 2017 (210) 86 e91 0022-4804/$ e see front matter ª 2016 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2016.11.003