Transient Modeling in Simulation of Hospital Operations for Emergency Response

2006 ◽  
Vol 21 (4) ◽  
pp. 223-236 ◽  
Author(s):  
Jomon Aliyas Paul ◽  
Santhosh K. George ◽  
Pengfei Yi ◽  
Li Lin
Keyword(s):  
Waiting Times ◽  
Exponential Model ◽  
Hospital Capacity ◽  
Modeling Approach ◽  
System A ◽  
Hospital Operations ◽  
Transient Modeling ◽  
Patient Waiting Time ◽  
The Impact ◽  
Patient Waiting

AbstractRapid estimates of hospital capacity after an event that may cause a disaster can assist disaster-relief efforts. Due to the dynamics of hospitals, following such an event, it is necessary to accurately model the behavior of the system. A transient modeling approach using simulation and exponential functions is presented, along with its applications in an earthquake situation. The parameters of the exponential model are regressed using outputs from designed simulation experiments. The developed model is capable of representing transient, patient waiting times during a disaster. Most importantly, the modeling approach allows real-time capacity estimation of hospitals of various sizes and capabilities. Further, this research is an analysis of the effects of priority-based routing of patients within the hospital and the effects on patient waiting times determined using various patient mixes. The model guides the patients based on the severity of injuries and queues the patients requiring critical care depending on their remaining survivability time. The model also accounts the impact of prehospital transport time on patient waiting time.

scholarly journals The Impact of Longer Consultation Time: A Simulation-Based Approach

2020 ◽  
Vol 11 (05) ◽  
pp. 857-864
Author(s):  
Abdulrahman M. Jabour
Keyword(s):  
Waiting Time ◽  
Quality Care ◽  
Working Hours ◽  
Discrete Events ◽  
Simulation Based ◽  
Patient Waiting Time ◽  
Free Environment ◽  
The Impact ◽  
Consultation Time ◽  
Patient Waiting

Abstract Background Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients' order of arrival than those with a shorter consultation time. Conclusion By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients' order of arrival than those with a shorter consultation time.

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

Impact of Facility Damages on Hospital Capacities for Decision Support in Disaster Response Planning for an Earthquake

2009 ◽  
Vol 24 (4) ◽  
pp. 333-341 ◽  
Author(s):  
Jomon Aliyas Paul ◽  
Li Lin
Keyword(s):  
Disaster Response ◽  
Waiting Times ◽  
Parametric Models ◽  
Emergency Relief ◽  
Response Planning ◽  
Hospital Operations ◽  
Emergency Assistance ◽  
Life Saving ◽  
Inaccurate Estimation ◽  
The Impact

AbstractHospitals provide life-saving functions and emergency assistance to communities when disaster strikes. Any damage to hospitals from a disaster, either structural and non-structural, can impair these capabilities. In addition, an inaccurate estimation of the treatment capacities available at hospitals in a disaster-affected region can severely affect the success of emergency relief plans. In this paper, the impact of facility damage on hospital operations is estimated using a generic simulation model. From the simulation results, parametric models are developed for estimating hospitals' capacities and patient waiting times that could be used by emergency response teams in making casualty dispatching/routing decisions.

scholarly journals Factors Associated With Patient Waiting Time at a Medical Outpatient Clinic: A Case Study of University of Nairobi Health Services

2021 ◽  
Vol 6 (12) ◽  
pp. 915-918
Author(s):  
Rebecca Bisanju Wafula (BSCN, MSCHSM) ◽  
Dr. Richard Ayah (MBCHB, MSC, PHD)
Keyword(s):  
Health Services ◽  
Health Care Providers ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Health Workers ◽  
Waiting Times ◽  
Care Providers ◽  
Cross Sectional ◽  
Patient Waiting Time ◽  
Patient Waiting

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.

scholarly journals Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency

2017 ◽  
Vol 13 (6) ◽  
pp. e530-e537 ◽  
Author(s):  
Samuel Suss ◽  
Nadia Bhuiyan ◽  
Kudret Demirli ◽  
Gerald Batist
Keyword(s):  
Complex Systems ◽  
Cancer Treatment ◽  
Patient Flow ◽  
Waiting Times ◽  
Value Added ◽  
Treatment Center ◽  
Quality Patient Care ◽  
Metropolitan Hospital ◽  
Patient Waiting Time ◽  
Patient Waiting

Outpatient cancer treatment centers can be considered as complex systems in which several types of medical professionals and administrative staff must coordinate their work to achieve the overall goals of providing quality patient care within budgetary constraints. In this article, we use analytical methods that have been successfully employed for other complex systems to show how a clinic can simultaneously reduce patient waiting times and non-value added staff work in a process that has a series of steps, more than one of which involves a scarce resource. The article describes the system model and the key elements in the operation that lead to staff rework and patient queuing. We propose solutions to the problems and provide a framework to evaluate clinic performance. At the time of this report, the proposals are in the process of implementation at a cancer treatment clinic in a major metropolitan hospital in Montreal, Canada.

The Impact of Lean Management Implementation on Waiting Time and Satisfaction of Patients and Staff at an Outpatient Pharmacy of a Comprehensive Cancer Center in Jordan

2020 ◽  
pp. 001857872095414
Author(s):  
Suzan Hammoudeh ◽  
Abdullah Amireh ◽  
Saad Jaddoua ◽  
Lama Nazer ◽  
Enas Jazairy ◽  
...  
Keyword(s):  
Patient Satisfaction ◽  
Waiting Time ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Lean Management ◽  
Pharmacy Staff ◽  
Full Implementation ◽  
Patient Waiting Time ◽  
The Impact ◽  
Patient Waiting

Background: Patient satisfaction with outpatient pharmacy services at our institution was below the target level, due mainly to long waiting times. A lean management strategy to reduce patient waiting time and increase the satisfaction of both patients and staff was developed and implemented. Methods: The project was conducted in the outpatient pharmacy of a comprehensive cancer center in Amman, Jordan. The process started with formation of a multidisciplinary team and A3 problem-solving, which is a 10-step scientific method with measurable patient-centered outcomes. Average patient waiting time and level of patient satisfaction were compared before and after full implementation of the process. In addition, a survey was conducted among the pharmacy staff who worked in the outpatient pharmacy during the process to determine its impact on staff satisfaction. Results: Patient waiting time for prescriptions of fewer than 3 medications and of 3 medications or more decreased significantly (22.3 minutes vs 8.1 minutes, P < .001, and 31.8 minutes vs 16.1 minutes, P < .002, respectively), and patient satisfaction increased (62% vs 69%; P = .005) after full implementation of the project. The majority of the pharmacy staff reported that the process motivated them in their work and that both their jobs and their relationships with their managers and colleagues had improved. Conclusion: Application of lean management in an outpatient pharmacy was effective in reducing patient waiting time and improving the satisfaction of both patients and employees.

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