Hospital “Self-Prophylaxis” Strategies for Efficient Protection of the Workforce in the Face of Infectious Disease Threats

2007 ◽  
Vol 28 (05) ◽  
pp. 618-621 ◽  
Author(s):  
Wei Xiong ◽  
Eric Hollingsworth ◽  
Jack Muckstadt ◽  
Jaclyn Van Lieu Vorenkamp ◽  
Eliot J. Lazar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Waiting Time ◽  
Communicable Disease ◽  
Discrete Event ◽  
Management Strategies ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Hospital Preparedness ◽  
The Face ◽  
Efficient Protection

Hospital preparedness for nosocomial or community-wide outbreaks of communicable disease includes the capability for rapid, self-reliant administration of prophylaxis to its workforce, with the goal of minimal disruption of patient care, here called hospital “self-prophylaxis.” We created a new discrete-event simulation model of a hypothetical hospital wing to compare the operational charateristics of standard single-line, “first-come, first-served” dispensing clinics with those of 2 staff management strategies that can dramatically reduce staff waiting time while centralizing dispensing around existing pharmacy-distribution points.

Improving patient waiting time of centralized front office service in a regional hub hospital using the discrete event simulation model

2020 ◽  
Vol 28 (5) ◽  
pp. 487-494
Author(s):  
D. Cocchi ◽  
E. Ciagli ◽  
A. Ancora ◽  
P. Tortoli ◽  
C. Carpini ◽  
...  
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Waiting Times ◽  
Discrete Event ◽  
University Hospital ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Front Office ◽  
Patient Waiting

BACKGROUND: Today, hospital rankings are based not only on basic clinical indicators, but even on quality service indicators such as patient waiting times. Improving these indicators is a very important issue for hospital management, so finding a solution to achieve it in a simple and effective way is one of the greatest goals. OBJECTIVES: The aim of this article is to evaluate the use of a discrete event simulation model to improve healthcare processes and reduce waiting time of patients and hospital costs. METHODS: The case study proposed in this paper is the reorganization of non-clinical front office operation for the patients (i.e. booking of exams, delivering medical reports, etc.) of the Careggi University Hospital of Florence, to optimize the utilization of the human resources and to improve performances of the process. RESULTS: The development and validation of the model was made according to an analysis of real processes and data, pre and post implementation of model outcomes. The new organization shows a decrease of waiting times from an average value of 10 minutes and 37 seconds to 5 minutes and 57 seconds (-44%). CONCLUSIONS: This paper shows that discrete event simulation could be a precise, cost-limited tool to optimize hospital processes and performance.

A Discrete Event Simulation Model to Evaluate Operational Performance of a Colonoscopy Suite

2009 ◽  
Vol 30 (3) ◽  
pp. 380-387 ◽  
Author(s):  
Bjorn Berg ◽  
Brian Denton ◽  
Heidi Nelson ◽  
Hari Balasubramanian ◽  
Ahmed Rahman ◽  
...  
Keyword(s):  
Waiting Time ◽  
Discrete Event ◽  
Turnaround Time ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Number Of Patients ◽  
Patient Waiting Time ◽  
Patient Arrival ◽  
Key Resources ◽  
Procedure Room

Background and Aims. Colorectal cancer, a leading cause of cancer death, is preventable with colonoscopic screening. Colonoscopy cost is high, and optimizing resource utilization for colonoscopy is important. This study’s aim is to evaluate resource allocation for optimal use of facilities for colonoscopy screening. Method. The authors used data from a computerized colonoscopy database to develop a discrete event simulation model of a colonoscopy suite. Operational configurations were compared by varying the number of endoscopists, procedure rooms, the patient arrival times, and procedure room turnaround time. Performance measures included the number of patients served during the clinic day and utilization of key resources. Further analysis included considering patient waiting time tradeoffs as well as the sensitivity of the system to procedure room turnaround time. Results. The maximum number of patients served is linearly related to the number of procedure rooms in the colonoscopy suite, with a fixed room to endoscopist ratio. Utilization of intake and recovery resources becomes more efficient as the number of procedure rooms increases, indicating the potential benefits of large colonoscopy suites. Procedure room turnaround time has a significant influence on patient throughput, procedure room utilization, and endoscopist utilization for varying ratios between 1:1 and 2:1 rooms per endoscopist. Finally, changes in the patient arrival schedule can reduce patient waiting time while not requiring a longer clinic day. Conclusions. Suite managers should keep a procedure room to endoscopist ratio between 1:1 and 2:1 while considering the utilization of related key resources as a decision factor as well. The sensitivity of the system to processes such as turnaround time should be evaluated before improvement efforts are made.

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