Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

Analysis of the implementation of an innovative IT solution to improve waiting times, communication with primary care and efficiency in Rheumatology

Objective To describe in detail an innovative program based on telemedicine for semi-automated prioritization of referrals from Primary Care (PC) to Rheumatology, for reproducibility purposes, and to present the results of the implementation study. Methods The context and situation were carefully analyzed, paying attention to all processes in place, referral numbers, waiting times, and number of complementary tests prior to discharge from Rheumatology. The composition of the team, aims, users, scope, and implementation phases were defined. Eight process indicators were established and measured before and 32 months after the program implementation. Results The program, which includes IT circuits, algorithms based on response to specific guideline-based checklists, e-consultation, and appointments based on priority, was fully implemented in our health area after a pilot study in two PC centers. After implementation, 6185 rheumatology referrals showed an e-consultation response delay of 8.95 days, and to first face-to-face visit (after e-consultation) of 12.6 (previous delay before program implementation was 83.1 days). Resolution by e-consultation reached 20% (1195 patients did not need seeing the rheumatologist to have the problem solved), and 1369 patients (32%) were discharged after the first visit. The overall resolution rate was 44.0% (2564 discharges/5830 e-consultations). From a random sample of 100 visits, only 10% of patients needed additional complementary tests to make a diagnosis and decision by Rheumatology (20.9% decrease from previous period). Conclusion A careful analysis of the situation and processes, with implementation of simple IT circuits, allows for the improvement of the efficiency and resolution of problems in Rheumatology.

Prioritizing Elective Surgery During the COVID-19 Pandemic Has Caused Age-Related Inequality: a Multicenter Study

The concern has been that this prioritization has resulted in age-related inequality between patients, with the older population suffering the most. The aim of this multicenter study was to examine the differences in incidence and waiting times of elective surgeries by age during the SARS-CoV-2 coronavirus disease (COVID-19) pandemic in Finland. Data on elective surgery (88 716 operations) were gathered from three Finnish public hospitals for the years 2017–2020. Surgery incidence and waiting times stratified by age groups (younger than 18, 18 to 49, 50 to 69, and 70 or older) 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 first COVID-19 wave decreased surgery incidence most prominently in patients younger than 18 (incidence rate ratio [IRR] 0.64, CI 0.60–0.68) and 70 or older (IRR 0.68, CI 0.66–0.70). After the first wave, the incidence increased in patients aged 50 to 69 and 70 or older by 22% and 29%, respectively. Among patients younger than 18, the incidence in 2020 was 15% lower. In patients younger than 18, waiting times were at mean of 43% longer in June to December compared to the reference years. In patients aged 18 to 49, 50 to 69, and 70 or older, waiting times increased in May but recovered to normal level during fall 2020. COVID-19 decreased the incidence of surgery and led to increased waiting times. Clearing of the treatment backlog started with older patients which resulted in prolonged waiting times among pediatric patients.

Relationship Between Waiting Time on Patient Satisfaction at Baloi Permai Batam Puskesmas Year 2021

Background: Medical records are an important part in assisting the implementation of service delivery to patients in hospitals. This research aimsMethods: Quantitative with survey research, a quantitative approach is used to find out how effective the relationship between waiting time and patient satisfaction is at the Baloi Permai Health Center.Results: The results of the chi square statistical test showed that the p-value of 0.001 was less than 0.050, so it can be said that there is a significant relationship between waiting time and patient satisfaction. The odds ratio for the relationship between waiting time and patient satisfaction is 7.263 with 95% CI between 2.143- 24.614. Patients with long waiting times are 7,263 or 7 times more likely to have a low level of satisfaction compared to patients whose waiting times are not too long.Conclusions: Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients. Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients.

UPAYA PENERAPAN METODE LEAN THINKING PADA PROSES PELAYANAN FARMASI RAWAT JALAN

Pharmacy services in a hospital are an inaccessible part of the hospital services as a whole. The accumulation of prescriptions in the pharmacy will cause the prescription process to be long and long, which has an impact on customer waiting times, which of course will have a major impact on customer satisfaction. From the standard time set as Quality Indicators at Bali Jimbaran Hospital, namely: drug processing at the outpatient pharmacy of Bali Jimbaran Hospital is 60 minutes of concocted drugs, 30 minutes of non-concocted drugs. The purpose of this research is to identify activities starting from the input, process and results generated through the Lean approach. The method used is an observational action process research, using lean methods to photograph the outpatient pharmacy service process flow through document review, direct interviews, interviews. The result of the research is an improvement in waiting time, it can be seen that the NVA activities can be eliminated by 66% and VA activities show an increase of 44%. With the many activities that are VA and the elimination of NVA activities, it will accelerate the process of outpatient pharmacy services at the Bali Jimbaran Hospital and improve customer satisfaction, which can be seen from the decrease in customer complaints against outpatient pharmacy services by up to 50%, which was previously 80%. Keywords: Lean, Pharmacist, Waiting time, Value Added, Non-Value Added

A single centre service evaluation of the pre-biopsy mpMRI pathway for prostate cancer diagnosis

Aim: To study the efficacy and impact of the local pre-biopsy multiparametric magnetic resonance imaging (mpMRI) pathway for prostate cancer diagnosis. Methods: In this tertiary centre, 570 patients had prostate mpMRI across a 6-month period in 2019. A total of 511 patients met inclusion criteria for retrospective analysis. MRI reporting used the Prostate Imaging-Reporting and Data System (PI-RADS) v2.1. These were assessed alongside histological outcomes and diagnostic times. PI-RADS ⩾ 3 were recommended for biopsy consideration. Gleason scoring ⩾ 3 + 4 and 3 + 3 were used to define clinically and non-clinically significant prostate cancer (csPCa and nsPCa), respectively. Results: Overall prostate cancer prevalence was 40% (204/511, csPCa in 31.1%) with an overall biopsy avoidance of 32.1% (164/511). Around 69.7% (356/511) scored PI-RADS ⩾ 3 and 30.3% (155/511) scored PI-RADS 1–2. About 22.6% (35/155) of PI-RADS 1–2 patients proceeded to biopsy, demonstrating a negative predictive value of 91.43% for csPCa. For PI-RADS ⩾ 3 patients, 63.4% (197/312) of those biopsied had cancer (Gleason ⩾ 3 + 3), with 50% (156/312) demonstrating csPCa. Around 76.7% (102/133) of PI-RADS 5, 35.3% (48/136) of PI-RADS 4, 14.0% (6/43) of PI-RADS 3 and 8.6% (3/35) of PI-RADS 1–2 scores demonstrated csPCa. Overall median prostate-specific antigen (PSA) density was 0.15 ng/mL2 (IQR: 0.10–0.27/mL2). PSA density were significantly different across PI-RADS cohorts ( H = 118.8, p < 0.0001) and across all three biopsy outcomes ( H = 99.72, p < 0.0001). Only 34.3% (119/347) of biopsied patients met the NHS 28-day standard. MRI acquisition and reporting met the 14-day local standard in 96.1% (491/511). The biopsy was the most delayed component with a median of 20 days (IQR: 8–43). Conclusion: Pre-biopsy mpMRI with PI-RADS scoring safely avoided biopsy in almost one-third (32.1%) of patients. The use of PSA-density in risk stratifying PI-RADS 3 lesions has informed local practice in the period 2020–2021, with implementation of a PSA-density threshold of 0.12 ng/mL2. Biopsy scheduling issues and anaesthetic requirements need to be overcome to improve diagnostic waiting times. Level of evidence: 2

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.

The Important Role of Time Limits When Consumers Choose Their Time in Service

A main challenge that service providers face when managing service systems is how to generate value and regulate congestion at the same time. To this end, classical queueing models suggest managers charge per-use fees and invest in capacity to speed up the service. However, in discretionary services, in which consumers value time in service and choose how long to stay, per-use fees result in suboptimal performance and speeding up does not apply. We study a queueing model of a service provider and rational consumers who are heterogenous in their requirements for service duration. Consumers incur disutility from waiting and choose whether to join and how long to spend in service. We consider time limits as a novel mechanism that may help in controlling congestion. Time limits put a cap on the maximum time that customers can spend in service. We analyze their effectiveness when combined with two price schemes: per-use fees and price rates. Time limits are effective because they reduce time in service and impact waiting times and joining behavior. Revenue maximizing firms and social planners who maximize social welfare benefit from implementing time limits in addition to price rates. Social planners who seek to maximize consumer welfare, however, focus on regulating congestion and should, therefore, offer the service for free but implement time limits if congestion levels are high. The attractiveness of time limits goes further. We show that time limits are not only a useful lever that works well when combined with simple price mechanisms, but they are in fact optimal when congestion is high. Service providers can achieve the first-best outcome and extract all customer surplus by coupling a time limit with an optimal price mechanism. The attractiveness of time limits stems from their ability to reduce not only the average time spent in service, but also its variance. This is highly effective in settings in which customers’ service times impose externalities on others’ waiting times. Thus, we conclude that providers of discretionary services should set time limits when congestion is an issue. This paper was accepted by Vishal Gaur, operations management.

Enhancing Public Participation in Public Health Offerings: Patient Preferences for Facilities in the Western Cape Province Using a Discrete Choice Experiment

Understanding patients’ preferences for health facilities could help decision makers in designing patient-centered services. Therefore, this study aims to understand how patients’ willingness to trade for certain attributes affects the choice of public health facilities in the Western Cape province of Cape Town, South Africa. A discrete choice experiment was conducted in two community day centers (CDCs). Patients repetitively chose between two hypothetical health facilities that differed in six attributes: distance to facility, treatment by doctors vs. nurses, confidentiality during treatment, availability of medication, first visit (drop-in) waiting times, and appointment waiting times. The sample consisted of 463 participants. The findings showed that availability of medication (50.5%), appointment waiting times (19.5%), and first visit waiting times (10.2%) were the most important factors for patients when choosing a health facility. In addition, respondents preferred shorter appointment and first visit waiting times (<2 h). These results identified important characteristics in choosing public health facilities in Cape Town. These public health facilities could be improved by including patient voices to inform operational and policy decisions in a low-income setting.