Emergency Department Logistic Optimization Using Design of Experiments

2017 ◽  
pp. 386-400
Author(s):  
Jose Antonio Vazquez-Ibarra ◽  
Rodolfo Rafael Medina-Ramirez ◽  
Irma Jimenez-Saucedo
Keyword(s):  
Emergency Department ◽  
Design Of Experiments ◽  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Time Factors ◽  
Healthcare Services ◽  
Public Healthcare ◽  
Response Variable ◽  
Main Entrance

Public healthcare services face a growing demand and Emergency department is the main entrance to these services. Waiting times at Emergency departments are increasing at risky levels, causing that people die in wait rooms due to a lack of staff to serve timely every patient. Present chapter describes one research project conducted in a mexican public hospital which was in the process of adopting a triage systems in order to reach the goal of a maximum wait time in department. Design of experiments is the tool proposed to analyze waiting time factors and define the best levels to reduce the response variable value.

Emergency Department Logistic Optimization Using Design of Experiments

2016 ◽  
pp. 293-308
Author(s):  
Jose Antonio Vazquez-Ibarra ◽  
Rodolfo Rafael Medina-Ramirez ◽  
Irma Jimenez-Saucedo
Keyword(s):  
Emergency Department ◽  
Design Of Experiments ◽  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Time Factors ◽  
Healthcare Services ◽  
Public Healthcare ◽  
Response Variable ◽  
Main Entrance

Public healthcare services face a growing demand and Emergency department is the main entrance to these services. Waiting times at Emergency departments are increasing at risky levels, causing that people die in wait rooms due to a lack of staff to serve timely every patient. Present chapter describes one research project conducted in a mexican public hospital which was in the process of adopting a triage systems in order to reach the goal of a maximum wait time in department. Design of experiments is the tool proposed to analyze waiting time factors and define the best levels to reduce the response variable value.

scholarly journals Measuring and Analyzing Waiting Time Indicators of Patients’ Admitted in Emergency Department: A Case Study

2015 ◽  
Vol 8 (1) ◽  
pp. 143 ◽  
Author(s):  
Saeed Amina ◽  
Ahmad Barrati ◽  
Jamil Sadeghifar ◽  
Marzeyh Sharifi ◽  
Zahra Toulideh ◽  
...  
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Waiting Times ◽  
Hospital Data ◽  
Governance System ◽  
Cross Sectional ◽  
Average Waiting Time ◽  
Study Population

<p><strong>BACKGROUND</strong><strong> </strong><strong>&amp;</strong><strong> </strong><strong>AIMS:</strong> Measuring and analyzing of provided services times in Emergency Department is the way to improves quality of hospital services. The present study was conducted with aim measuring and analyzing patients waiting time indicators in Emergency Department in a general hospital in Iran.</p> <p><strong>MATERIAL</strong><strong> </strong><strong>&amp;</strong><strong> </strong><strong>METHODS:</strong> This cross-sectional, observational study was conducted during April to September 2012. The study population consisted of 72 patients admitted to the Emergency Department at Baharlo hospital. Data collection was carried out by workflow forms. Data were analyzed by t.<strong> </strong>test and ANOVA.</p> <p><strong>RESULTS:</strong> The average waiting time for patients from admission to enter the triage 5 minutes, the average time from triage to physician visit 6 minute and the average time between examinations to leave ED was estimated 180 minutes. The total waiting time in the emergency department was estimated at about 210 minutes. The significant<strong> </strong>correlation between marital status of patients (P=0.03), way of arrive to ED (P=0.02) and type of shift work (P=0.01) with studied time indicators were observed.</p> <p><strong>CONCLUSION:</strong> According to results and comparing with similar studies, the average waiting time of patients admitted to the studied hospital is appropriate. Factors such as: Utilizing clinical governance system and attendance of resident Emergency Medicine Specialist have performed an important role in reducing of waiting times in ED.</p>

scholarly journals Impact of streaming "fast track" emergency department patients

2006 ◽  
Vol 30 (4) ◽  
pp. 525 ◽  
Author(s):  
Debra O'Brien ◽  
Aled Williams ◽  
Kerrianne Blondell ◽  
George A Jelinek
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Waiting Time ◽  
Fast Track ◽  
Average Length ◽  
Waiting Times ◽  
Relative Reduction ◽  
Average Waiting Time ◽  
Discharged Patients ◽  
Track Area

Objective: Fast track systems to stream emergency department (ED) patients with low acuity conditions have been introduced widely, resulting in reduced waiting times and lengths of stay for these patients. We aimed to prospectively assess the impact on patient flows of a fast track system implemented in the emergency department of an Australian tertiary adult teaching hospital which deals with relatively few low acuity patients. Methods: During the 12-week trial period, patients in Australasian Triage Scale (ATS) categories 3, 4 and 5 who were likely to be discharged were identified at triage and assessed and treated in a separate fast track area by ED medical and nursing staff rostered to work exclusively in the area. Results: The fast track area managed 21.6% of all patients presenting during its hours of operation. There was a 20.3% (?18 min; 95%CI, ?26 min to ?10 min) relative reduction in the average waiting time and an 18.0% (?41 min; 95%CI, ?52 min to ?30 min) relative reduction in the average length of stay for all discharged patients compared with the same period the previous year. Compared with the 12-week period before the fast track trial, there was a 3.4% (?2.1 min; 95%CI, ?8 min to 4 min) relative reduction in the average waiting time and a 9.7% (?20 min; 95%CI, ?31 min to ?9 min) relative reduction in the average length of stay for all discharged patients. There was no increase in the average waiting time for admitted patients. This was despite major increases in throughput and access block in the study period. Conclusion: Streaming fast track patients in the emergency department of an Australian tertiary adult teaching hospital can reduce waiting times and length of stay for discharged patients without increasing waiting times for admitted patients, even in an ED with few low acuity patients.

scholarly journals Reducing overcrowding in an emergency department: a pilot study

2019 ◽  
Vol 65 (12) ◽  
pp. 1476-1481
Author(s):  
Fábio Ferreira Amorim ◽  
Karlo Jozefo Quadros de Almeida ◽  
Sanderson Cesar Macedo Barbalho ◽  
Vanessa de Amorim Teixeira Balieiro ◽  
Arnaldo Machado Neto ◽  
...  
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Federal District ◽  
Arrival Times ◽  
Process Maps ◽  
Manchester Triage System ◽  
Simulation Scenario ◽  
Reduce Wait Time ◽  
Capacity Data

SUMMARY OBJECTIVE Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times. RESULTS Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients’ wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients. CONCLUSION Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs’ can be used to generate and test solutions to decrease overcrowding.

The role of regional health systems on the waiting time inequalities in health care services: Evidences from Italy

2020 ◽  
pp. 095148482092830
Author(s):  
Stefano Landi ◽  
Enrico Ivaldi ◽  
Angela Testi
Keyword(s):  
Health Care ◽  
Health Systems ◽  
Waiting Time ◽  
Health Care Access ◽  
Health Care Services ◽  
Waiting Times ◽  
Healthcare Services ◽  
Regional Health ◽  
Health Authorities ◽  
Regional Health Authorities

Inequalities in effective access to healthcare are present among countries and within the same country. Despite in Italy exist the principle of equity in access to health system, there are evidence of different access rates in the form of unequal waiting time within the country. Waiting times are an instruments to ration healthcare services dealing with resource scarsity. Theoretically, it is a fair tool because waiting times should depend only on health needs and not on the ability to pay. However, a growing literature has pointed out that belonging to a particular socioeconomic status leads to waiting times inequalities for healthcare services. Many countries have socioeconomic disparities among regions, and healthcare organizations need to take into account these differences. The increasing power of Regional Health Authorities in decentralized health systems, as in the case of Italy, has generated different organizational ways to provide health care, possibly leading to different access rates in the form of unequal waiting time within the country. This paper aims to understand if the administrative area (Regional Health Authorities) in charge of health services affects waiting times lowering or strengthening health care access inequalities. Using a series of logistic regression models, this work suggests the presence of two vectors: socioeconomic inequalities and regional inequalities. Health organizations need to implement different kinds of answers for each vectors of inequalities.

scholarly journals Children Who Leave the Emergency Department Without Being Seen: Why Did They Leave and What Would Make Them Stay?

2018 ◽  
Vol 31 (2) ◽  
pp. 109 ◽  
Author(s):  
Rodrigo Sousa ◽  
Cátia Correia ◽  
Rita Valsassina ◽  
Sofia Moeda ◽  
Teresa Paínho ◽  
...  
Keyword(s):  
Emergency Department ◽  
Medical Care ◽  
Patient Outcomes ◽  
Waiting Time ◽  
Waiting Times ◽  
Adverse Outcomes ◽  
Decision To Leave ◽  
Left Without Being Seen ◽  
Number Of Patients ◽  
Control Study

Introduction: Children who visit emergency departments and leave without being seen represent a multifactorial problem. We aimed to compare the sociodemographic characteristics of children who left and of those who did not leave, as well as to evaluate parental reasoning, subsequent use of medical care and patient outcome.Material and Methods: This was a prospective case-control study of a random sample of children who left without being seen and their matched controls from an emergency department during a three-month period. We performed a phone questionnaire to obtain information concerning reasons for leaving, patient outcomes and general feedback.Results: During the study period, 18 200 patients presented to the emergency department, of whom 92 (0.5%) left without being seen. Fifty-five (59.8%) completed the questionnaire and there were 82 controls. The most common reasons for leaving were ‘excessive waiting time’ (92.7%) and ‘problem could wait’ (21.8%). A significantly higher number of patients who left sought further medical care (78.2% vs 11%) but they did not experience higher levels of unfavourable outcomes.Discussion: The waiting time seems to be the major factor that drives the decision to leave. The fact that parents felt safe in leaving and the low level of adverse outcomes highlights the low-acuity nature of the majority of patients who leave.Conclusion: Reducing the waiting times may be the logical strategic mean to decrease the rates of patients who leave without being seen. However, our data seems to indicate that the concerns surrounding clinical outcome after leaving may be partly unwarranted.

scholarly journals Emergency department waiting times: do the raw data tell the whole story?

2014 ◽  
Vol 38 (1) ◽  
pp. 65 ◽  
Author(s):  
Janette Green ◽  
James Dawber ◽  
Malcolm Masso ◽  
Kathy Eagar
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Peer Group ◽  
Waiting Times ◽  
Geographic Location ◽  
Cross Sectional ◽  
Raw Data ◽  
Performance Targets ◽  
Triage Category ◽  
Sectional Analysis

Objective To determine whether there are real differences in emergency department (ED) performance between Australian states and territories. Methods Cross-sectional analysis of 2009−10 attendances at an ED contributing to the Australian non-admitted patient ED care database. The main outcome measure was difference in waiting time across triage categories. Results There were more than 5.8 million ED attendances. Raw ED waiting times varied by a range of factors including jurisdiction, triage category, geographic location and hospital peer group. All variables were significant in a model designed to test the effect of jurisdiction on ED waiting times, including triage category, hospital peer group, patient socioeconomic status and patient remoteness. When the interaction between triage category and jurisdiction entered the model, it was found to have a significant effect on ED waiting times (P < 0.001) and triage was also significant (P < 0.001). Jurisdiction was no longer statistically significant (P = 0.248 using all triage categories and 0.063 using only Australian Triage Scale 2 and 3). Conclusions Although the Council of Australian Governments has adopted raw measures for its key ED performance indicators, raw waiting time statistics are misleading. There are no consistent differences in ED waiting times between states and territories after other factors are accounted for. What is known about the topic? The length of time patients wait to be treated after presenting at an ED is routinely used to measure ED performance. In national health agreements with the federal government, each state and territory in Australia is expected to meet waiting time performance targets for the five ED triage categories. The raw data indicate differences in performance between states and territories. What does this paper add? Measuring ED performance using raw data gives misleading results. There are no consistent differences in ED waiting times between the states and territories after other factors are taken into account. What are the implications for practitioners? Judgements regarding differences in performance across states and territories for triage waiting times need to take into account the mix of patients and the mix of hospitals.

scholarly journals The Violence of Uncertainty: empirical evidence on how asylum waiting time undermines refugees’ health

2021 ◽  
Author(s):  
Jenny Phillimore ◽  
Sin Yi Cheung
Keyword(s):  
Empirical Evidence ◽  
Waiting Time ◽  
Emotional Health ◽  
Waiting Times ◽  
Healthcare Services ◽  
Health Problems ◽  
Baseline Survey ◽  
Refugee Status ◽  
Negative Effect ◽  
Reported Health

Grace and colleagues (2018) introduced the idea of violent uncertainty making claims about the deleterious impacts of insecure immigration status on the health of migrants. Policies of uncertainty are said to directly and indirectly create harm by impacting on individual’s health via detention and public degradation and undermining healthcare services. We offer original empirical evidence indicating an association with uncertainty, in the form of asylum waiting times, on refugees’ self-reported health. We devise four hypotheses that: long waiting time for asylum decisions increases likelihood of self-reported health problems and the effect persists overtime, that female refugees report higher levels of health problems and religion moderates the association between health and uncertainty. We use data from the UK longitudinal Survey of New Refugees wherein all new refugees were sent a baseline survey immediately after receiving refugee status and then follow-up surveys 21 months later. The findings show longer asylum waiting time is associated with poor health. Female refugees were more likely to report poor emotional and physical health. The negative effect of asylum waiting time on emotional health persists 21 months post settlement with hypotheses about the ameliorating effect of religion only partially supported. Our findings supports existing theory and findings from qualitative studies about the deleterious effects of using policies of waiting-related uncertainty for managing migration. Given the wide use of such policies in the Global North, our work is suggestive of likely generalisability. Thus, countries with large refugee populations might want to consider our findings when developing asylum policy which minimises impact on refugee health.

scholarly journals Predicting Patient Wait Times in the Phlebotomy Unit

2020 ◽  
pp. 1-5
Author(s):  
Dilek Orbatu ◽  
Oktay Yıldırım ◽  
Eminullah Yaşar ◽  
Ali Rıza Şişman ◽  
Süleyman Sevinç
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
High Accuracy ◽  
Wait Times ◽  
Blood Collection ◽  
Time Prediction ◽  
Number Of Patients ◽  
Patient Will ◽  
Patient Waiting Time

Patients frequently complain of long waiting times in phlebotomy units. Patients try to predict how long they will stay in the phlebotomy unit according to the number of patients in front of them. If it is not known how fast the queue is progressing, it is not possible to predict how long a patient will wait. The number of prior patients who will come to the phlebotomy unit is another important factor that changes the waiting time prediction. We developed an artificial intelligence (AI)-based system that predicts patient waiting time in the phlebotomy unit. The system can predict the waiting time with high accuracy by considering all the variables that may affect the waiting time. In this study, the blood collection performance of phlebotomists, the duration of the phlebotomy in front of the patient, and the number of prior patients who could come to the phlebotomy unit was determined as the main parameters affecting the waiting time. For two months, actual wait times and predicted wait times were compared. The wait time for 95 percent of the patients was predicted with a variance of ± 2 minutes. An AI-based system helps patients make predictions with high accuracy, and patient satisfaction can be increased.

scholarly journals “Is that for here or to go?” Drive Through Pediatric Vaccine Clinic as a Novel Approach during a global pandemic

2021 ◽  
pp. 1-17
Author(s):  
Sowmya Patil ◽  
Karalyn Kerby ◽  
Amy Ramick ◽  
Justin H. Criddle
Keyword(s):  
Disaster Preparedness ◽  
Underserved Populations ◽  
Wait Time ◽  
Healthcare Services ◽  
Vaccination Rate ◽  
Childhood Vaccination ◽  
Public Healthcare ◽  
Vaccine Administration ◽  
Global Pandemic ◽  
Novel Approach

ABSTRACT Objective: Routine childhood vaccination and well-child visits are essential for pediatric patients’ preventative and public healthcare services. The COVID-19 pandemic had an immediate and significant decline in well-child visits and vaccine administration. A one-of-a-kind’ Drive Through Vaccine Clinic’ was established to improve the vaccination rate and alleviate parental anxiety about being exposed to COVID-19 infection. Methods: Our initial focus was on children between 18 months – 4 years of age at the start of the pandemic, and then slowly extended this to the back-to-school vaccines and the Influenza vaccines. Results: The Drive-Through Immunization Station provided 745 vaccines to 415 patients between April and September 2020. The median wait time involved from patient arrival to completion of vaccine administration was five minutes at the Drive-Through location. Patient and parent feedback was positive. The addition of Drive Through Clinic helped significantly increase the total number of vaccines administered compared to the previous year. Conclusion: In a global pandemic, innovative ideas to increase access to preventive healthcare should be a priority. In the future, this method of nontraditional vaccine administration will allow for improved outreach efforts to underserved populations in our communities and better disaster preparedness.

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