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.

scholarly journals Assessment of patients waiting and service times in the ophthalmology clinic of a public tertiary hospital in Nigeria

2020 ◽  
Vol 54 (4) ◽  
pp. 231-237
Author(s):  
Lateefat B. Olokoba ◽  
Kabir A. Durowade ◽  
Feyi G. Adepoju ◽  
Abdulfatai B. Olokoba
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Sampling Technique ◽  
Tertiary Hospital ◽  
Cross Sectional Study ◽  
Ethical Review ◽  
Cross Sectional ◽  
Arrival Times ◽  
The Mean ◽  
Service Times

Introduction: Long waiting time in the out-patient clinic is a major cause of dissatisfaction in Eye care services. This study aimed to assess patients’ waiting and service times in the out-patient Ophthalmology clinic of UITH. Methods: This was a descriptive cross-sectional study conducted in March and April 2019. A multi-staged sampling technique was used. A timing chart was used to record the time in and out of each service station. An experience based exit survey form was used to assess patients’ experience at the clinic. The frequency and mean of variables were generated. Student t-test and Pearson’s correlation were used to establish the association and relationship between the total clinic, service, waiting, and clinic arrival times. Ethical approval was granted by the Ethical Review Board of the UITH. Result: Two hundred and twenty-six patients were sampled. The mean total waiting time was 180.3± 84.3 minutes, while the mean total service time was 63.3±52.0 minutes. Patient’s average total clinic time was 243.7±93.6 minutes. Patients’ total clinic time was determined by the patients’ clinic status and clinic arrival time. Majority of the patients (46.5%) described the time spent in the clinic as long but more than half (53.0%) expressed satisfaction at the total time spent at the clinic. Conclusion: Patients’ clinic and waiting times were long, however, patients expressed satisfaction with the clinic times.

scholarly journals Data Analysis of Ambient Intelligence in a Healthcare Simulation System: A Pilot Study in High-end Health Screening Process Improvement

2020 ◽  
Author(s):  
Ming-Shu Chen ◽  
Kun-Chih Wu ◽  
Yu-Ling Tsai ◽  
Bernard C. Jiang
Keyword(s):  
Waiting Time ◽  
Wait Time ◽  
Tertiary Hospital ◽  
Health Screening ◽  
Health Examination ◽  
Screening Process ◽  
Arrival Times ◽  
System A ◽  
The Cost ◽  
Bottleneck Resources

Abstract This study aimed to reduce the total waiting time for high-end health screening processes. The subjects of this study were recruited from a health screening center in a tertiary hospital in northern Taiwan from September 2016 to February 2017 and a total of 2,342 high-end customers were collected. Arena software was used to simulate the examination process. We presented the simulation results of three different policies and compared those results to the current state. The first policy presented a predetermined proportion of customer types, in which the total waiting time was increased from 72.29 to 83.04 mins. The second policy was based on increased bottleneck resources and provided significant improvement, with the total waiting time was decreased from 72.29 to 28.39 mins. However, this policy also caused the cost to increase dramatically, while lowering the utilization of this exam station. The third policy was adjusting customer arrival times, which reduced the waiting time significantly, as the total waiting time was reduced from 72.29 to 55.02 mins. Although the wait time of this policy was slightly longer than that of the second policy, the additional cost was much lower than the second plan. Scheduled arrival intervals could help to reduce customer waiting time in the health screening department base on FIFO rule. The simulation model of this study could be utilized and the parameters could be modified to comply with different health examination centers to improve process and service quality.

Improving process quality for pediatric emergency department

2014 ◽  
Vol 27 (4) ◽  
pp. 336-346 ◽  
Author(s):  
Byungjoon B.J. Kim ◽  
Theodore R. Delbridge ◽  
Dawn B. Kendrick
Keyword(s):  
Flow Control ◽  
Waiting Time ◽  
Pediatric Patients ◽  
Waiting Times ◽  
Discrete Event ◽  
Process Quality ◽  
Content Type ◽  
Arrival Times ◽  
Number Of Patients ◽  
Control Management

Purpose – Overcrowding in emergency departments (EDs) leads to longer waiting times and results in higher number of patients leaving the ED without being seen by a physician. EDs need to improve quality for patients’ waiting time and length of stay (LoS) from the perspective of process and flow control management. The paper aims to discuss these issues. Design/methodology/approach – The retrospective case study was performed using the computerized ED patient time logs from arrival to discharge between July 1, 2009 and June 30, 2010. Patients were divided into two groups either adult or pediatric with a cutoff age of 18. Patients’ characteristics were measured by arrival time periods, waiting times before being seen by a physician, total LoS and acuity levels. A discrete event simulation was applied to the comparison of quality performance measures. Findings – Statistically significant differences were found between the two groups in terms of arrival times, acuity levels, waiting time stratified for various arrival times and acuity levels. The process quality for pediatric patients could be improved by redesign of patient flow management and medical resource. Research limitations/implications – The results are limited to a case of one community and ED. This study did not analyze the characteristic of leaving the ED without being seen by a physician. Practical implications – Separation of pediatric patients from adult patients in an ED can reduce the waiting time before being seen by a physician and the total staying time in the ED for pediatric patients. It can also lessen the chances for pediatric patients to leave the ED without being seen by a physician. Originality/value – A process and flow control management scheme based on patient group characteristics may improve service quality and lead to a better patient satisfaction in ED.

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.

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 How does clinical space utilisation impact patient flow?

2019 ◽  
Vol 8 (3) ◽  
pp. e000542 ◽  
Author(s):  
Alexandra von Guionneau ◽  
Charlotte M Burford
Keyword(s):  
Quality Improvement ◽  
Waiting Time ◽  
Primary Outcome ◽  
Patient Flow ◽  
Waiting Times ◽  
Wait Time ◽  
Limiting Factor ◽  
Electronic Systems ◽  
Improvement Project ◽  
Medical Wards

BackgroundLong waiting times in accident and emergency (A&E) departments remain one of the largest barriers to the timely assessment of critically unwell patients. In order to reduce the burden on A&Es, some trusts have introduced ambulatory care areas (ACAs) which provide acute assessment for general practitioner referrals. However, ACAs are often based on already busy acute medical wards and the availability of clinical space for clerking patients means that these patients often face long waiting times too. A cheap and sustainable method to reducing waiting times is to evaluate current space utilisation with the view to making use of underutilised workspace. The aim of this quality improvement project was to improve accessibility to pre-existing clinical spaces, and in doing so, reduce waiting times in acute admissions.MethodsData were collected retrospectively from electronic systems and used to establish a baseline wait time from arrival to having blood taken (primary outcome). Quality improvement methods were used to identify potential implementations to reduce waiting time, by increasing access to clinical space, with serial measurements of the primary outcome being used to monitor change.ResultsData were collected over 54 consecutive days. The median wait time increased by 55 min during the project period. However, this difference in waiting time was not deemed significant between the three PDSA cycles (p=0.419, p=0.270 and p=0.350, Mann-Whitney U). Run chart analysis confirmed no significant changes occurred.ConclusionIn acute services, one limiting factor to seeing patients quickly is room availability. Quality improvement projects, such as this, should consider facilitating better use of available space and creating new clinical workspaces. This offers the possibility of reducing waiting times for both staff and patients alike. We recommend future projects focus efforts on integration of their interventions to generate significant improvements.

scholarly journals Determinants of hospital waiting time for outpatient care in India: how demographic characteristics, hospital ownership, and ambulance arrival affect waiting tim

2018 ◽  
Vol 5 (7) ◽  
pp. 2692
Author(s):  
Shyamkumar Sriram ◽  
Rakchanok Noochpoung
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Study Data ◽  
Outpatient Clinics ◽  
Related Factors ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
For Profit ◽  
Hospital Revenue

Background: Waiting time in hospital outpatient clinics affects patient satisfaction, access to care, health outcomes, trust, willingness to return and hospital revenue. Only a few studies have explored length and variability of waiting times among patients. This study is an attempt to understand factors affecting waiting time experienced by patients in outpatient clinics.Methods: For this study, data were collected in 2012 from 830 patients seeking care from outpatient clinics located in 30 randomly selected hospitals in the district of Nellore, India. Linear regression and logistic regression models have been used to identify the effect of various determinants on hospital waiting times.Results: The average waiting time in government hospitals was 20.3 minutes compared to 15.5 minutes in private hospitals and 39.71 minutes in voluntary hospitals. Waiting time of men was about six minutes lower than women. After controlling for other patient related and hospital related factors, median wait time was 19% lower for male patients compared to females. Length of waiting declines with patient's age. Patients arriving by ambulance waited 64% less that patients not arriving by ambulance but this pattern was not valid for public hospitals.Conclusions: Significant gender bias was present in all facility-types implying that policy and legal interventions would be required. For-profit hospitals had lower waiting time of patients to ensure higher demand for their services by the economically better-off sections of the population. The results highlight the importance of lowering the waiting time in public sector hospitals, especially for patients arriving in ambulances.

scholarly journals Managing waiting times to predict no-shows and cancelations at a children’s hospital

2016 ◽  
Vol 9 (5) ◽  
pp. 1107 ◽  
Author(s):  
Miguel Rodríguez-García ◽  
Aldo A McLean-Carranza ◽  
J. Carlos Prado-Prado ◽  
Pablo Domínguez-Caamaño
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Hospital Performance ◽  
Strong Impact ◽  
Distribution Analysis ◽  
Missed Appointments ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Appointment Keeping

Purpose: Since long waits in hospitals have been found to be related to high rates of no-shows and cancelations, managing waiting times should be considered as an important tool that hospitals can use to reduce missed appointments. The aim of this study is to analyze patients’ behavior in order to predict no-show and cancelation rates correlated to waiting times.Design/methodology/approach: This study is based on the data from a US children’s hospital, which includes all the appointments registered during one year of observation. We used the call-appointment interval to establish the wait time to get an appointment. Four different types of appointment-keeping behavior and two types of patients were distinguished: arrival, no-show, cancelation with no reschedule, and cancelation with reschedule; and new and established patients.Findings: Results confirmed a strong impact of long waiting times on patients’ appointment-keeping behavior, and the logarithmic regression was found as the best-fit function for the correlation between variables in all cases. The correlation analysis showed that new patients tend to miss appointments more often than established patients when the waiting time increases. It was also found that, depending on the patients’ appointment distribution, it might get more complicated for hospitals to reduce missed appointments as the waiting time is reduced.Originality/value: The methodology applied in our study, which combines the use of regression analysis and patients’ appointment distribution analysis, would help health care managers to understand the initial implications of long waiting times and to address improvement related to patient satisfaction and hospital performance.

scholarly journals Robotic Pharmacy Implementation and Outcomes in Saudi Arabia. A Twenty-One Month Review. (Preprint)

2021 ◽  
Author(s):  
James Waterson ◽  
Hisham Momattin ◽  
Shokry Arafa ◽  
Shahad Momattin ◽  
Rayan Rahal
Keyword(s):  
Saudi Arabia ◽  
Patient Satisfaction ◽  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Pharmacy Services ◽  
Patient Wait Time ◽  
Implementation Data ◽  
Dispensing Error ◽  
Dispensing Errors

BACKGROUND We describe the introduction, use and evaluation of an automation and integration pharmacy development program in a private facility in Saudi Arabia. The project was undertaken to meet specific challenges of increasing throughput, reducing medication dispensing error, increasing patient satisfaction, and freeing up pharmacists’ time for increased face-to-face consultations with patients. OBJECTIVE To reduce outpatient waiting times for dispensing of medications, to help to free up time to meet patient expectations for pharmacy services including medication education, to reduce the volume of non-value-added pharmacist tasks, to reduce dispensing error rates, and to aid with the rapid development of a reputation in the served community for patient-centred care for a new facility. METHODS Pre-implementation data for patient wait-time for dispensing of prescribed medications as one measure of patient satisfaction, pharmacist activity and productivity in terms of patient interaction time were gathered. Reported and discovered dispensing errors per 1,000 prescriptions were also aggregated. All pre-implementation data was gathered over an eleven- month period. Initial project goals were set as a 50% reduction in the average patient wait-time, a 15% increase in patient satisfaction regarding pharmacy waiting time and pharmacy services, a 25% increase in pharmacist productivity and zero dispensing errors. This was expected to be achieved within ten months of go-live. RESULTS From go-live, data was gathered on the above metrics in one-month increments. At the 10-month point there had been a 53% reduction in the average waiting time, a 20% increase in patient satisfaction regarding pharmacy waiting time, with a 22% increase in overall patient satisfaction regarding pharmacy services, and a 33% increase in pharmacist productivity. There was a zero-rate dispensing error reported. CONCLUSIONS The robotic pharmacy solution studied was highly effective, but upstream supply chain is vital to throughput maintenance, particularly when automated filling is planned. The automation solution must also be seamlessly and completely integrated into the facility’s software systems for appointments, medication records and prescription in order to garner its full benefits. Patient overall satisfaction with pharmacy services is strongly influenced by waiting time and follow up studies ae required to identify how to use this positive effect and how to optimally use ‘freed-up’ pharmacist time. The extra time spent with patients by pharmacists, and the complete overview of the patient’s medication history, that full integration gives, creates opportunities for tackling challenging issues such as medication nonadherence. Reduced waiting times may also allow for smaller prescription fill volumes, and more frequent outpatient department visits, allowing increased contact time with pharmacists.

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