Blockchain Technology for Enhancing Traceability and Efficiency in Automobile Supply Chain—A Case Study

A robust traceability system would help organizations in inventory optimization reduce lead time and improve customer service and quality which further enables the organizations to be a leader in their industry sector. This research study analyzes the challenges faced by the automotive industry in its supply chain operations. Further, the traceability issues and waiting time at different nodes of the supply chain are considered to be priority issues that affect the overall supply chain efficiency in the automotive supply chain. After studying the existing blockchain architectures and their implementation methodology, this study proposes a new blockchain-based architecture to improve traceability and reduce waiting time for the automotive supply chain. A hyper ledger fabric-based blockchain architecture is developed to track the ownership transfers in inbound and outbound logistics. The simulation results of the proposed hyper ledger fabric-based blockchain architecture show that there is an improvement in the traceability of items at different nodes of the supply chain that enhances the Inventory Quality Ratio (IQR) and the mean waiting time is reduced at the factory, wholesaler, and retailer, which thereby improves the overall supply chain efficiency. The blockchain embedded supply chain is more capable to eliminate the risks and uncertainties associated with the automotive supply chain. The benefits of adopting blockchain technology in the automotive supply chain are also described. The developed blockchain-based framework is capable to get more visibility into goods movement and inventory status in automotive supply chains.

Comparison of Visit Length and Waiting Time of Patients in Public and Private Clinics in the North of Iran

Background: The visit length is considered one of the indicators for assessing patients’ satisfaction. Factors such as waiting time for getting a visit affects the desirability of the visit. Objectives: This study aimed to investigate the visit length and waiting time of patients in public and private clinics in Tabriz. Methods: This is a descriptive-analytic study conducted in five clinics in 2018. A questionnaire-based survey was used to collect data from 386 participants recruited through simple random sampling. Mann-Whitney U and Kruskal-Wallis tests were applied to analyze the data using SPSS version 22.0. Results: Overall, the mean visit length was 25.5 and 25.4 min in public and private centers, respectively, while the mean waiting time was 141.2 and 156.4 min in public and private centers, respectively. There was no significant difference between public and private centers regarding the visit length (P > 0.05); however, there was a significant difference between public and private centers in terms of waiting time (P < 0.05). Conclusions: The waiting time was too much, especially in private clinics, which can negatively affect patient satisfaction. Therefore, suggested interventions may consist of using internet and telephone admission, scheduling a waiting list, and requiring physicians to be present on time.

EP.WE.844Two-week bowel cancer referrals during the COVID pandemic

Aims The aims were to assess the impact of the COVID-19 pandemic on the waiting times for patients referred via the two-week pathway for suspected colorectal cancer. We also examined the use of Faecal Immunochemical Test (FIT) alongside the presenting complaints in triaging/prioritising patients for further imaging and/or endoscopic investigations appropriately. Methods A list of all patients referred via the two-week pathway to the West Suffolk Hospital for suspected colorectal cancers from 30/01/2020 to 19/07/2020 was compiled. The main four red flag symptoms were change in bowel habit (CIBH), anorectal bleeding, anaemia and weight loss. A subset of 235 patients were closely examined regarding their presenting complaints, FIT, imaging and endoscopy results with analysis of outcomes. Results 127 male versus 108 female patients were included. 59.61% of patients who were eligible for the FIT test received one. Mean waiting time for FIT positive patients was 42.39 (95% CI) versus 61.10 (95% CI) for FIT negative patients. Patients with one or two red flags symptoms had a mean waiting time of 44.81 days (95% CI 35.79-53.82) and 47.91 days (95% CI 38.07-57.75) respectively. Patients with three red flag symptoms had a mean waiting time of 28.2 days (95% CI 17.94-38.39). There was a statistically significant difference in mean waiting time between patients having 1-2 symptoms and patients with three symptoms (p &lt; 0.005). Conclusions Despite delays during the COVID pandemic particularly for endoscopy, high risk and FIT positive patients were prioritised. Waiting times were still higher than advised national guidelines.

P042 Five-year review of a tertiary paediatric sleep service for complex patients

Introduction Our paediatric sleep unit commenced service for children with complex medical problems in July 2015. Service capacity includes 12 inpatient level 1 studies (two neonates) and one home study per week. FTE includes senior scientists 2.6, sleep technologists 1.7, administration 1.0, nursing 0.7 and medical 1.2. The primary aim of this study was to evaluate activity during the first 5-years. The secondary aim was to document the impact of the COVID-19 pandemic. Methods Sleep unit operational & diagnostic data were collected from sleep booking sheets, sleep study reports, electronic medical records. Descriptive statistics are presented. Results A total of 2186 sleep studies were performed (July 2015 to June 2020) with a range of 368–472 studies per annum. Overall, 61.7% were diagnostic studies, 20.8% titration studies (CPAP, oxygen, bi-level or invasive ventilation), 10% neonatal and 7.5% home studies. Between 2016–2020, the average waiting time (days) for a neonatal study was 16, a titration study was 106, a diagnostic study was 110 and a home study was 76. Further delays were caused by the COVID19 pandemic. Mean waiting time rose 229% from 108 days (Feb 2020) to 355 days (Feb 2021). Referrals for sleep studies have exceeded bed capacity since the beginning of the pandemic. Discussion This audit describes activity in a tertiary complex paediatric sleep service during the first 5 years. The service has struggled on current FTE and bed capacity to manage waiting times, exacerbated further by the COVID-19 pandemic. A new business and clinical model are warranted.

A Preliminary Study of the Treatment Outcomes of Paediatric Dental Patients Referred for General Anaesthesia or Sedation at a Regional Hospital in Trinidad

General anaesthesia and sedation are known to be useful adjuncts in the care of paediatric dental patients. There are several challenges that prevent patients from receiving care. Aim: To assess the treatment outcomes of paediatric dental patients seen at an emergency facility who were referred for treatment under sedation or general anaesthesia at a regional hospital in Trinidad. Methods: Records of patients seen at the Child Dental Health Unit Emergency clinic at the University of The West Indies Dental School from 2012 to 2017 were assessed. The parents of children referred for general anaesthesia or sedation at the regional hospital were then interviewed via telephone. Results: Most children (53.4%) were younger than 6 years old and the most common reasons for referral were the treatment of multiple carious teeth and behaviour management. Furthermore, 66.1% of cases did not receive treatment and had a mean waiting time of 4.7 years, and 61.7% of referred cases needed emergency care while awaiting general anaesthesia or sedation. Limited access to these services and the high cost of treatment were the main reasons for non-treatment. Conclusion: There is significant need for the timely treatment of paediatric dental patients referred for general anaesthesia or sedation. Improved availability and accessibility of these services could improve patients’ quality of life.

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

900 Effects Of COVID-19 Pandemic Restrictions on the 2ww Breast Referrals in A UK Specialist Breast Service

Aim Recommendations were issued to the hospital Trusts to configure service delivery to balance cancer care with patient and hospital staff safety during the COVID-19 pandemic. It was felt the service restrictions might lead to delays in diagnosis and treatment of cancer patients. We conducted an audit to compare 2ww breast referrals in our center between May to July of 2019 and 2020. Method We triaged all referrals to face-face consultation or telephone consultation in our center during the pandemic. Patients with suspicious symptoms were offered face-face consultation after the telephone triage. Results Data analysis showed that the referrals fell by 28.3% (N 1569 versus N 1125). The largest reduction was noted in May (34.4% versus 24.2%). Mean waiting time in 2019 was 19.86 (± 7.14) and 11.43 (± 3.48) in 2020. The proportion of patients referred for suspected breast cancers increased across all age groups in 2020 (range +10.4% to 16.2%). Significantly more breast cancers were diagnosed in 2020 (7.1% versus 5.1%). No breast cancer was diagnosed in under 25 patients. 29.1% of the 522 patients telephoned were discharged, and others were seen in the clinic. Conclusions The COVID-19 infection’s management caused a fall in 2ww referrals and shortened waiting times but increased breast cancer diagnosis. Many 2ww referrals during the COVID-19 infection were unnecessary. The telephone consultation reduced waiting times but may have deferred clinic visitation for most patients.

The Kumaraswamy Pareto IV Distribution

We introduce a new model named the Kumaraswamy Pareto IV distribution which extends the Pareto and Pareto IV distributions. The density function is very flexible and can be left-skewed, right-skewed and symmetrical shapes. It hasincreasing, decreasing, upside-down bathtub, bathtub, J and reversed-J shaped hazard rate shapes. Various structural properties are derived including explicit expressions for the quantile function, ordinary and incomplete moments,Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, probability weighted moments and generating function. We provide the density function of the order statistics and their moments. The Renyi and q entropies are also obtained. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The usefulness of the new model is illustrated by means of three real-life data sets. In fact, our proposed model provides a better fit to these data than the gamma-Pareto IV, gamma-Pareto, beta-Pareto,exponentiated Pareto and Pareto IV models.

The impact of the COVID-19 pandemic on waiting times for elective surgery patients: A multicenter study

Background A concern has been that health care reorganizations during the first COVID-19 wave have led to delays in elective surgeries, resulting in increased complications and even mortality. This multicenter study examined the changes in waiting times of elective surgeries during the COVID-19 pandemic in Finland. Methods Data on elective surgery were gathered from three Finnish public hospitals for years 2017–2020. Surgery incidence and waiting times were examined and the year 2020 was compared to the reference years 2017–2019. The mean annual, monthly, and weekly waiting times were calculated with 95% confidence intervals (CI). The most common diagnosis groups were examined separately. Findings A total of 88 693 surgeries were included during the study period. The mean waiting time in 2020 was 92.6 (CI 91.5–93.8) days, whereas the mean waiting time in the reference years was 85.8 (CI 85.1–86.5) days, resulting in an average 8% increase in waiting times in 2020. Elective procedure incidence decreased rapidly in the onset of the first COVID-19 wave in March 2020 but recovered in May and June, after which the surgery incidence was 22% higher than in the reference years and remained at this level until the end of the year. In May 2020 and thereafter until November, waiting times were longer with monthly increases varying between 7% and 34%. In gastrointestinal and genitourinary diseases and neoplasms, waiting times were longer in 2020. In cardiovascular and musculoskeletal diseases, waiting times were shorter in 2020. Conclusion The health care reorganizations due to the pandemic have increased elective surgery waiting times by as much as one-third, even though the elective surgery rate increased by one-fifth after the lockdown.

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.