Reducing Waiting Time in the General Pediatric Clinic at Al Wakra Hospital, Qatar

Background: Waiting times for clinic appointments constitute a key indicator of an outpatient department performance for access to care and patient satisfaction. This is particularly relevant for pediatric population. The Ministry of Public Health in Qatar set a waiting time of 28 days for patients to get new appointment in General Outpatient Department (GOPD). The current average waiting time to get a new appointment in the general pediatric clinic (GPC) at AWH is 57 days. Aim: Decrease the average waiting time to get a new clinic appointment from 57 days to 28 days by the end of December 2018, and to meet the national targets set by the Ministry of Public Health. Methodology: This is a Quality Improvement (QI) project using the Model for Improvement (MFI). The MFI framework is designed to support organizations answering fundamental questions before agreeing on drivers for change. The implementation of change was be facilitated by the Plan-Do-Study-Act (PDSA) cycles methodology. The QI project team performed a root cause analysis using the Ishikawa diagram and identified the key contributing factors to the long waiting times to get a new appointment. Twenty-seven PDSA cycle ramps were designed with support of predictive tool to test innovative changes in current operational processes in an attempt to improve waiting time in the general pediatric clinic at Al Wakra Hospital. Results: The monthly average number of referrals for GPC increased by 200% between the pre and post implementation periods. The average triage waiting time improved from 6 to 2.6 days in 2018 and the average become 1 day in 2019. Post-implementation the average waiting time for patients to get new appointment improved from 57 days to 28 days in 2018 and the average waiting time improved to 16 days in 2019. Conclusion: The quality improvement project for the AWH general pediatric clinic demonstrates significant improvement in waiting times for new appointments, the recommendation for the hospital leadership would be to rollout the improvement methodology to other clinics that suffer from similar challenges.

COMPARISON OF TWO FORMS OF ERLANGIAN DISTRIBUTION LAW IN QUEUING THEORY

Context. For modeling various data transmission systems, queuing systems G/G/1 are in demand, this is especially important because there is no final solution for them in the general case. The problem of the derivation in closed form of the solution for the average waiting time in the queue for ordinary system with erlangian input distributions of the second order and for the same system with shifted to the right distributions is considered. Objective. Obtaining a solution for the main system characteristic – the average waiting time for queue requirements for three types of queuing systems of type G/G/1 with usual and shifted erlangian input distributions. Method. To solve this problem, we used the classical method of spectral decomposition of the solution of Lindley integral equation, which allows one to obtain a solution for average the waiting time for systems under consideration in a closed form. For the practical application of the results obtained, the well-known method of moments of the theory of probability was used. Results. For the first time, spectral expansions of the solution of the Lindley integral equation for systems with ordinary and shifted Erlang distributions are obtained, with the help of which the calculation formulas for the average waiting time in the queue for the above systems in closed form are derived. Conclusions. The difference between the usual and normalized distribution is that the normalized distribution has a mathematical expectation independent of the order of the distribution k, therefore, the normalized and normal Erlang distributions differ in numerical characteristics. The introduction of the time shift parameter in the laws of input flow distribution and service time for the systems under consideration turns them into systems with a delay with a shorter waiting time. This is because the time shift operation reduces the coefficient of variation in the intervals between the receipts of the requirements and their service time, and as is known from queuing theory, the average wait time of requirements is related to these coefficients of variation by a quadratic dependence. The system with usual erlangian input distributions of the second order is applicable only at a certain point value of the coefficients of variation of the intervals between the receipts of the requirements and their service time. The same system with shifted distributions allows us to operate with interval values of coefficients of variations, which expands the scope of these systems. This approach allows us to calculate the average delay for these systems in mathematical packages for a wide range of traffic parameters.

EP.WE.727Upper GI Surgery Clinic Referrals; Can We Improve the Patient Experience?

Aim To review the new referrals to the Upper GI surgery clinic for appropriateness, investigations requested, and waiting times and to identify potential pathways to reduce waiting times and improve the patient experience. Method Patients who attended the UGI clinic over 2 months period were identified. Data were collected from GP referrals and Electronic Patient Records. Follow up, post-discharge appointments, and Did Not Attends were excluded. Data collected included time from referral to first clinic, symptoms, investigations requested, suitability for a pathway, and appropriateness of referral. A first clinic outcome was concluded from reading the GP referral, there were 5 outcomes to choose from; direct to another specialty, discharge back to GP, clinic, surgery, pre-investigate and clinic. Results 147 referrals were analysed. The average waiting time from referral to the first clinic was 51 days (range 7-119 days). 73% of the referrals were GP referrals and 27% from other specialties. The most common referral was for gallstones and the most common 2 outcomes were Pre-investigate and surgery. Conclusion Most of the investigations and outcomes suggested from the project were the same as those from clinic letters. The following pathways can be developed to cut waiting times and costs for the trust:

EP.WE.338Ambulatory Ultrasound Scans Reduce Inpatient Admissions During COVID-19: A Need for the Expansion of Ambulatory Services

Aim With the increased need to manage patients out of hospital during COVID-19, it was anticipated that need for ambulatory imaging would increase. This study aimed to assess the demand for ambulatory ultrasounds (US) during the COVID-19 pandemic and the impact on inpatient admissions. Methods A retrospective review of patients presenting to the Emergency Department (ED) between 12th July – 23rd August 2020 who required an US as first line imaging. Electronic Care Records were used to collect data regarding type of US i.e., inpatient, or ambulatory, time taken for ambulatory US and outcome after imaging. The same period in 2019 was assessed for comparison. Results In 2020, 100 patients required an US compared to 88 in 2019. 37% (37/100) of which were discharged for an ambulatory US, compared to 14.8% (13/88) in 2019 (p = 0.006). The average waiting time for an ambulatory US in 2019 was 2 days, this increased to 7 days in 2020. Following ambulatory US in 2020 43.2% (16/37) required further outpatient imaging or assessment; similar outcomes were seen in 2019 with 46.2% (6/13). Overall, there was a 150% increase in the use of ambulatory US, with a 26% decrease in admissions in 2020 vs. 2019. Conclusions There was a significant increase in the number of patients discharged from ED to undergo an ambulatory US resulting in reduced inpatient admissions. This increase in demand is reflected by the prolonged waiting time highlighting the requirement for expansion of ambulatory services to meet this clinical need.

Spectral expansion method for analysis of a system with shifted Erlang and hyper-Erlang distributions

In this paper, we obtained a spectral expansion of the solution to the Lindley integral equation for a queuing system with a shifted Erlang input flow of customers and a hyper-Erlang distribution of the service time. On its basis, a calculation formula is derived for the average waiting time in the queue for this system in a closed form. As you know, all other characteristics of the queuing system are derivatives of the average waiting time. The resulting calculation formula complements and expands the well-known unfinished formula for the average waiting time in queue in queuing theory for G/G/1 systems. In the theory of queuing, studies of private systems of the G/G/1 type are relevant due to the fact that they are actively used in the modern theory of teletraffic, as well as in the design and modeling of various data transmission systems.

805 A Dangerous Wait: The Impact of Prolonged Waiting Times in Endocrine Surgery

Aim In Northern Ireland on 31/12/19 90,514 patients were awaiting admission/day case procedure. The 2019/2020 Ministerial waiting time target states that by March 2020, 55% of patients should not wait longer than 13 weeks for inpatient/day case treatment, and no patient should wait longer than 52 weeks. This audit investigates the impact of long waiting times in endocrine surgery and how they impact patient safety. Method Data was collected from the endocrine surgery waiting list in the Royal Victoria Hospital, Belfast, up to 6/2/20. Number of days spent on the waiting list, disease complications and the number of days before they occurred were collated. Results 118 patients were awaiting endocrine surgery. The average waiting time was 533 days. 21 patients experience 27 complications related to their endocrine disease whilst waiting for surgery. The average duration before complications was 490 days; 4 required admission, 11 required medical intervention and 3 required a surgical intervention. Conclusions The average waiting time for endocrine surgery is greater than 52 weeks. In Northern Ireland no one should be waiting more than 52 weeks. The length of the waiting list has resulted in 1 in 5 experiencing complications and prolonged suffering from under-treated disease. This is a significant patient safety concern. Urgent action to address waiting lists is required and the disruption caused by COVID-19 should be used as a catalyst for reform.

Active Signage of Pedestrian Crossings as a Tool in Road Safety Management

The main objective of the study was to verify the effectiveness of active pedestrian crossings equipped with flashing lights activated automatically by detected pedestrians. A pilot study was conducted in two sites, where speed profiles of vehicles over the distance of 30 m before the crossing were analyzed. The study produced promising results in terms of reducing vehicle speeds so the next study investigated four other unsignalized pedestrian crossings. They were video-recorded for 48 h each, before, after and a year after installation. The ANOVA test was used to check the statistical significance of changes in selected indicators. Even after a year from the installation, the effect of the active signage remained significant. The average percentage of drivers yielding to pedestrians was 77.4% higher and the average waiting time 25.2% lower than before the installation. The average speeds of vehicles were 3.53 km/h lower on collector and 2.60 km/h lower on arterial streets. A decline in the probability of a pedestrian being killed or severely injured (KSI) ranged from 6.3 pp (9.4%) on the arterial streets immediately after the installation up to 12.9 pp (31.7%) on the collector streets one year after.

Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular Ad-Hoc Networks (VANETS)

In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.

AN ANALYSES ON PATIENTS’ QUEUING SYSTEM AT MUHAMMAD ABDULLAHI WASE SPECIALIST HOSPITAL, KANO

A major cause for concern in hospitals is congestion, which brings about untoward hardship to patients due to long queues and delay in service delivery. This paper seeks to minimize the waiting time of patients by comparing the performance indicators of a single server and multi-server model at the Paediatrics Department of Muhammad Abdullahi Wase Specialist Hospital Kano (MAWSHK). In order to achieve this, primary data was obtained through direct observation which in turn is subjected to the test of goodness of fit to ascertain the distribution that best describes the data. The performance indicators comprising utilization factor, average number of patients in the queue, average number of patients in the system, average waiting time in queue and average waiting time in system for a single server and multi-server model were computed and analyzed respectively. Our findings indicate that the G/G/4 model performs better compared to the G/G/1 model as it minimizes the waiting time of patients

An Improved Queuing Model for Reducing Average Waiting Time of Emergency Surgical Patients Using Preemptive Priority Scheduling

Our research objective is to reduce the Average Waiting Time for patients in an Emergency Department of public sector hospital. We have based our model on M/M/s Queuing System, our study revealssignificant findings on arrival rate of patients. During this simulation, we have used a preemptive priority scheduling model. In our practice, the arrival rate followed a Poisson distribution, averaging 30 patients per hour, with the Mean Service time of1.5 hours and Average Waiting Time recorded around 12.13 minutes. This research offersvaluable help to achieve better time management in emergency departments of high-density medical facilities.