Average Wait Time Research Articles

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach.

BackgroundWait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers.ObjectiveThe first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers).MethodsWe analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments.ResultsThe average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models.ConclusionsThe random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

TH2.3 Elective versus emergency management of gallstone disease at a tertiary referral centre amidst the COVID-19 pandemic - should the protocol be altered?

BackgroundGallstones are a common pathology affecting approximately 15% of the population in UK, 20% of which are symptomatic. It is suggested symptomatic patients undergo cholecystectomies. Guidelines recommend this is performed within one week of initial emergency presentation or 52 weeks for elective case. Surgical capacity to manage emergency cholecystectomies was limited due to the COVID pandemic. We assessed wait time discrepancy between elective and emergency cholecystectomies.MethodsA retrospective review of all patients undergoing cholecystectomies between January and November 2021 in a major tertiary referral centre in London was undertaken. Initial pathology at the time of presentation, elective vs emergency presentation, pre-surgical biliary complications along and wait times were reviewed.Results219 (74 elective, 145 emergency) patients underwent surgery, mean age 48 years (23% Male and 77% Female). Average wait times for elective cholecystectomies were 69.7 days (min 0, max 246) in comparison to 68.9 days (min 1, max 253) for emergency surgery. 22 (15%) of the patients of initial emergency presentation re-attended hospital and 6 (4%) had adverse events such as gallbladder perforation or pancreatitis due to delayed treatment. Comparatively, 9 (12%) elective patients attended A&E due to pain, with no adverse outcomes.ConclusionsOverall wait time of emergency vs elective cholecystectomies were similar. This review indicates emergency cases require prioritisation over elective cholecystectomies due to the higher number of re-attendance and adverse events. Emergency cases need to be prioritised to meet guidelines, which could also reduce complication rates whilst awaiting surgery and lead to fewer adverse outcomes.

TH2.12 Difficulties in managing acute gallstone disease at a Tertiary Referral Centre admist COVID-19

BackgroundAcute biliary disease, a surgical emergency, is predominantly treated conservatively initially. Specialist units aim to follow guidelines set by the Royal College of Surgeons and NICE to provide a cholecystectomy within a set time. Clinical practice at St Thomas’ Hospital was reviewed along with the difficulties during the COVID-19 pandemic.AimsReassess practice at a specialist unit failing to meet guidelines during the start of COVID-19. Prospective data collection, on patients booked for a laparoscopic cholecystectomy (LC) after emergency attendances.MethodInitial retrospective data analysis, reviewing pre-COVID (PC19) practice (03/19–02/20), initial COVID-19 (IC19) management (03/20–12/20). Prospective data (01/21–11/21) after implementing changes (AC19).Identifying demographics, pathology, length of stay during acute admission, average wait for surgery and readmission rate prior to surgery. Patients receiving surgery within 6 weeks, which has been set by our trust as an acceptable standard.ResultsPatients with acute presentation (acute cholecystitis, gallstone pancreatitis, cholangitis) 162 (PC19), 80 (IC19), 145 (AC19). Gender Ratio M:F 1:2 for all groups. Average wait to surgery 93 (PC19), 44 (IC19), 69 (AC19) days. Patients receiving surgery within 6 weeks 24.7% (PC19), 32.5% (IC19), 51.7% (AC19). Patients who were still awaiting surgery at the end of each time frame 49% (PC19), 51% (IC19), 48% (AC19). Mean length of surgical stay 1.75 (AC19) days.ConclusionsFurther changes are required, as guidelines are still not being met, with average wait times significantly above the recommended wait to undergo laparoscopic cholecystectomy.

BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems

This paper proposes an integrated dispatching framework for matching drivers with riders in ride-hailing systems. The goal is to compute matching solutions that maximize social welfare and benefit both sides of the market, such that the sustainable growth of the ride-hailing system is ensured. The proposed framework integrates data-driven proactive guidance strategies with batched matching optimization to increase social welfare, improve matching rate and reduce rider wait time. Proactive guidance strategies are computed by leveraging short-term demand forecasts based on historical data. Taken the resulting guidance strategies as inputs, the batched matching algorithm computes optimal bipartite matching between drivers and riders in a batch. Using New York City taxi data from 2016 March 1st to March 31st as input, we conduct a numerical study to evaluate the performance of the proposed framework and compare it with existing approaches in the literature. Our results show that the proposed framework improves social welfare for up to 50%. It also increases the matching rate by an average of 20% and reduces the average rider wait time by over 15%. This implies a strong potential for the proposed dispatching framework to improve service quality in ride-hailing systems.

Access to timely mental health care treatment initiation among Veterans Health Administration patients with and without serious mental illness.

Timely care initiation is a priority within the Veterans Health Administration (VHA). Patients with serious mental illnesses (SMI) are a group that benefits from timely care initiation due to elevated risks of negative outcomes with delayed care. However, no evaluation has assessed whether VHA SMI patients disproportionately experience delays in mental health care initiation. VHA administrative care data were used to compare delays in mental health care initiation for VHA patients with and without SMI who had newly identified mental health needs. Analyses assessed rates of delayed initial mental health appointments within five settings (General Mental Health [GMH], Primary Care Mental Health Integration [PC-MHI], Post-Traumatic Stress Disorder [PTSD], Substance Use Disorder [SUD], and Psychosocial Rehabilitation clinics [PSR]). SMI patients were more likely to receive delayed initial appointments in three of five clinical settings (PTSD, SUD, PSR) and had significantly longer average wait times for an initial appointment when referred to the PTSD clinic for an initial appointment. Overall, SMI patients were equally as likely to receive delayed initial appointments. While VHA SMI patients were not more likely to experience delayed mental health care initiation overall, they were more likely to experience delays within three of the five treatment settings. Findings suggest that the majority of VHA SMI patients experience equivalent timeliness, though those with more complex needs, and particularly those with trauma-related care needs, may be more likely to experience treatment initiation delays. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Implementing Lean Six Sigma Concepts in Audiology Practices

Implementing Lean Six Sigma Concepts in Audiology Practices

Serving Democracy: Evidence of Voting Resource Disparity in Florida

Florida, an important state in presidential elections in the United States, has received considerable media coverage in recent years for long lines to vote. Do some segments of the population receive a disproportionate share of the resources to serve the voting process, which could encourage some or dissuade others from voting? We conduct the first empirical panel data study to examine whether minority and Democrat voters in Florida experience lower poll worker staffing, which could lengthen the time to vote. We do not find evidence of a disparity directly due to race. Instead, we observe a political party effect—all else equal, a 1% increase in the percentage of voters registered as Democrat in a county increases the number of registered voters per poll worker by 3.5%. This effect appears to be meaningful—using a voting queue simulation, a 5% increase in voters registered as Democrat in a county could increase the average wait time to vote from 40 minutes (the approximate average wait time to vote in Florida in 2012 and the highest average wait time across all states in that election per the Cooperative Congressional Election Study) to about 115 minutes. This paper was accepted by Vishal Gaur, operations management.

Privacy Management in Service Systems

Problem definition: We study customer-centric privacy management in service systems. Academic/practical relevance: We explore the consequences of extended control over personal information by customers in such systems. Methodology: We adopt a stylized queueing model to capture a service environment that features a service provider and customers who are strategic in deciding whether to disclose personal information to the service provider—that is, customers’ privacy or information disclosure strategy. A customer’s service request can be one of two types, which affects service time but is unknown when customers commit to a privacy strategy. The service provider can discriminate among customers based on their disclosed information by offering different priorities. Results: Our analysis reveals that, when given control over their personal data, strategic customers do not always choose to withhold them. We find that control over information gives customers a tool they can use to hedge against the service provider’s will, which might not be aligned with the interests of customers. More importantly, we find that under certain conditions, giving customers full control over information (e.g., by introducing a privacy regulation) may not only distort already efficiently operating service system but might also backfire by leading to inferior system performance (i.e., longer average wait time), and it can hurt customers themselves. We demonstrate how a regulator can correct information disclosure inefficiencies through monetary incentives to customers and show that providing such incentives makes economic sense in some scenarios. Finally, the service provider itself can benefit from customers being in control of their personal information by enticing more customers joining the service. Managerial implications: Our findings yield insights into how customers’ individually rational actions concerning information disclosure (e.g., granted by a privacy regulation) can lead to market inefficiencies in the form of longer wait times for services. We provide actionable prescriptions, for both service providers and regulators, that can guide their choices of a privacy and information management approach based on giving customers the option of controlling their personal information.

UMMY SIAPKAN LULUSAN MEMILIKI SOFT SKILL DAN BERDAYA KOMPETITIF: PROGRAM BLPKL TRACER STUDY

BAN-PT has accredited Universitas Mahaputra Muhammad Yamin (UMMY) Solok, consisting of four faculties and twelve study programs. Ten study programs have received accreditation B, while two have received accreditation C. Since 1984, UMMY Solok has existed. There are 1,800 registered UMMY students until 2022. UMMY Solok graduates roughly 326 students each year for two types of graduations: odd and even semester graduations. It is vital to follow alumni systematically to respond to the growing number of graduates each year. This study attempts to determine the number of UMMY alumni employed, as well as if the knowledge learned during college applies to current jobs. This is geared toward bettering each study program's curriculum to meet the demands of the workplace. The alumni of 2017 graduates, a total of 381 graduates, will be the focus of the tracer study. Manual questionnaires were given to graduates who were not discovered through social media and online questionnaires. Meanwhile, for alumni discovered through social media (via Facebook, line, and WhatsApps) and IT-based, the questionnaires were given on http://www.ummy.ac.id. The questionnaires were given in a more focused and guided way. The establishment of an alumni database for the UMMY Solok tracer research can be accessed through lpkm.ummy@gmail.com. The following are the tracking results: (1) According to the survey results of the tracer study team, UMMY Solok alumni are looking for jobs before they graduate, and the average wait time for a job after graduation is five months. (2) UMMY Solok alumni who have worked in various countries or regions look for work in a variety of ways in order to find positions that match their disciplines and interests. (3) The topic of study is the variety of disciplines researched; the association between the field of study and alumni work is quite close (31,7%). (4) Almost all of the competencies held by alumni are good or very good.

Research on commuters’ carpooling behavior in the mobile internet context

The real-time sharing of information through mobile internet facilitates and increases demand for carpool commuting. In this study, information asymmetries are transformed into psychological costs to passengers and drivers; then, the psychological and other physical costs are integrated into a generalized travel cost. Logit and Nested logit models are used to analyze commuters' mode choices and to determine the densities of carpooling demand. The success rates of carpools across different demand densities are estimated using cooperative game theory, and carpool trips, driver pick-up distances, passenger wait times, and changes in the modal splits of commutes are calculated. Ningbo City, China is taken as an example for numerical analysis. Based on data from our survey and orders from more than ten OCH platforms, the growth trend of carpooling can be forecast., The results show that, due to mobile internet, the probabilities that drivers and passengers are willing to carpool increases from 19% and 15% to 28% and 20%, respectively. Simultaneously, the average pick-up distance of drivers declines to 2.9 km, and passengers' average wait time to 9 min and 24 s. The actual mode shares of carpooling for drivers and passengers increase to 20% and 17%, respectively. Carpool commuting thus exhibits a significant potential for growth through mobile internet.

Evaluating a general pediatric/adult genetic counseling clinic in a Midwest medical center.

In 2018, the Munroe-Meyer Institute for Genetics & Rehabilitation (MMI) at the University of Nebraska Medical Center (UNMC) in Omaha, NE created a genetic counseling clinic (GCC) to increase access to genetics services and decrease the time spent between a referral and being seen in a general genetics outpatient clinic. In the GCC, genetic counselors led patient encounters and geneticists served as advisors, rather than primary providers. We conducted a chart review of 109 patients seen in the GCC from November 1, 2018, to March 16, 2020, and obtained information regarding patient demographics, indications, and clinical recommendations as a result of the visit. Most patients seen in this clinic were female (65.1%) and aged 19 years of age or older (54.1%). The primary indications for patients in this clinic included review genetic test results (42.2%), coordination of genetic testing for a known familial variant (30.2%), and concerns for personal or family history suspicious of a genetic condition without dysmorphic features (24.8%). The average patient wait time between referral date and appointment date in the GCC was 49.8 days. The two most common clinical recommendations made by genetic counselors in the GCC were genetic testing (56.1%) and/or follow-up with specialist (26.5%). These specialists primarily included endocrinology (n=5), neurology (n=4), cardiology (n=4), ophthalmology (n=3), and audiology (n=3). We found that the GCC model may be appropriate for patients with (1) genetic test results requiring interpretation, (2) a known familial variant or (3) genetic testing recommended by a specialist physician. Descriptions of the indications and recommendations for patients seen in this GCC provide a framework for potential implementation of a GCC in other regions across the nation.

A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO2 (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.

COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

RETRACTED ARTICLE: Supply chain and logistics optimization management for international trading enterprises using IoT-based economic logistics model

Under the development trend of economic globalization, the financial difficulties of the supply chain may cause a serious crisis to the enterprises in the whole supply chain. It is necessary to alleviate the transformation pressure faced by international trading enterprises (ITE), to enhance the core competitiveness of ITE. This paper takes the SCLM (Supply Chain and Logistic Management) of ITE as the research object. Based on the analysis of the existing SCLM experience of ITE and the Internet of Things (IoT) technology, this paper puts forward the economic analysis model of the IoT-based SCLM. Specific data of some ITE prove the effectiveness of the proposed model. Further optimization is carried out through process analysis. The results show that the proposed SCLM economic analysis model based on IoT can effectively maximize the interests of enterprises and shorten the supply chain process by adding information groups. The introduced IoT-based method reduces the Average Queue Time (AQT) of vehicles by nearly 7 times and the Average Waiting Time (AWT) by nearly 4 times. After the IoT technology is applied to the SCLM of ITE, this paper significantly improves the cooperation efficiency of various departments, optimizes the relationship between customers and suppliers, and improves the core competitiveness of the enterprise. Therefore, this paper has important reference value for promoting the development of the small and micro machinery industry in China.

Proposed nonparametric runs rules Lepage and synthetic Lepage schemes

The average daily customer waiting time in a service centre is one of the most important metrics to measure performance or service quality. The average daily waiting time should meet a prespecified quality standard. Therefore, it is crucial to monitor such waiting times at regular intervals to maintain and improve the service quality. Similar problems arise in many time-between-event monitoring problems. This paper suggests two improved nonparametric Lepage schemes supplemented with runs rules, i.e., the 2-of-3 runs rules Lepage and the synthetic Lepage schemes. We show the real-life usage of the suggested schemes in monitoring the average daily customer waiting time of a service centre in Australia. The distribution-free nature of the suggested schemes allows broader applicability irrespective of the underlying process density. Moreover, the proposed schemes can jointly monitor the process location and scale parameters. We demonstrate the superiority of the suggested runs rules schemes to those without runs rules, i.e., the Shewhart-Lepage (SL) scheme through computer-intensive Monte-Carlo simulation. In general, we recommend the 2-of-3 runs rules Lepage scheme due to its efficiency and simplicity.

Emergency department childhood anaphylaxis presentations in regional/remote Australia.

AimExplore the prevalence of childhood anaphylaxis and clinical presentation of anaphylaxis in children across two regional emergency departments over a 7‐year period.MethodsRetrospective audit of all children (0–18 years) presenting to emergency from 1 January 2010 to 31 December 2016 with anaphylaxis, defined by Australasian Society of Clinical Immunology and Allergy definitions and doctor diagnosis.ResultsSeven hundred and twenty‐four patients were identified with allergic diagnosis, 60% were diagnosed with non‐anaphylaxis allergic reactions or unspecified urticaria and 40% with anaphylaxis (n = 286). Annual prevalence of anaphylaxis remained stable over the study period (M = 30.9/10 000 cases, range: 20.8–48.3/10 000). Gender distribution was equal, median age was 9.48 years (interquartile range = 4–15). Most (71%) arrived by private transport. 23% had a prior history of anaphylaxis. Food triggers (44%) were the most common cause of anaphylaxis. Insect bites/stings triggers occurred in 21%. Patients were promptly assessed (average wait time = 13 min), 16% received prior adrenaline injections. Adrenaline was administered in 26% and 20% were admitted to hospital. On discharge, 29% had a follow‐up plan, 9% received an allergy clinic referral, 6% anaphylaxis action plan, 26% adrenaline autoinjector prescriptions and allergy testing performed in 6%.ConclusionsWe found a relatively low prevalence of overall childhood anaphylaxis in a regional area. The two most common causes of anaphylaxis in this population (food and bites/stings) recorded increased prevalence providing an opportunity for further study. Significant gaps in evidence‐based care of anaphylaxis were noted, demonstrating the need for improved recognition and treatment guideline implementation in regional areas.

The Obstetric Response to the Opioid Epidemic [A218

INTRODUCTION: Daily, 136 Americans die from an opioid overdose. One of the areas hardest hit by this epidemic is the Appalachian region. To combat this crisis, Congress and the American College of Obstetricians and Gynecologists recommend a multifaceted approach in identifying, monitoring, and managing patients. Medication-assisted treatment (MAT) with methadone or buprenorphine is a cornerstone of therapy. Despite the acceptance of MAT as standard of care, opioid related maternal and neonatal morbidity rates continue to rise. METHODS: A telephone survey of obstetric care professionals in highly affected counties (as determined by Centers for Disease Control and Prevention analysis) in West Virginia, Kentucky, Ohio, Tennessee, Indiana, and South Carolina was performed. Providers were asked if they: 1. Prescribe methadone or buprenorphine; 2. Manage patients who obtain the medications from other prescribers; 3. Had a preferred provider where they referred these patients. Additional variables included average distance to the referral center and average wait time on phone. RESULTS: Our data show that pregnant women experience limited access as well as significant barriers in obtaining MAT compared to nonpregnant patients. Data analysis will be displayed graphically as well as illustratively using state mapping data highlighting the limited options that pregnant women face when seeking MAT. CONCLUSION: Our data show a wide gap in both the quantity and the accessibility of obstetric providers willing to prescribe MAT to pregnant patients. The burden of management has fallen disproportionately on academic centers and non-obstetric providers which makes accessing this potentially life-saving treatment in a timely manner out of reach in this high-risk population.

Influence of Socioeconomic Factors on Surgical Wait Times for Elective Surgeries in Benign Gynecology [A81

INTRODUCTION: In various disciplines, an association between patient outcomes and surgical wait times has been identified. This study provides the first investigation into whether practice setting influences wait times for elective surgeries in benign gynecology. METHODS: This retrospective study of patients at three Manhattan hospitals from October 2019 to February 2020 compared surgical wait times among patients seen in federally qualified health centers (FQHC) and private practice settings. Emergent surgeries, oncology cases, abortions, urogynecology procedures, and cases concurrently booked with another specialty were excluded. Surgical wait time was defined as the time (days) from the decision to operate to the day of procedure. A multivariable mixed model was used to model the log of surgical wait time by setting of care, adjusting for age, body mass index (BMI), race/ethnicity, insurance, need for medical clearance, and scheduled block time. RESULTS: A total of 540 patients were identified with a median age of 45.6 years (range 16-87 years). Average surgical wait time was 27 days (range 1-288 days). In multivariate analysis, longer surgical wait times were associated with the FQHC setting relative to private practice (70.9% longer, P=.006), and with needing medical clearance (36.8% longer, P=.02). Insurance type and race/ethnicity did not significantly impact surgical wait times in multivariate analysis. In univariate analysis, patients with public insurance had longer wait times compared with patients with commercial insurance (25% longer, P=.03). CONCLUSION: These results suggest that in benign gynecology, surgical wait times are significantly influenced by practice setting. Further research should investigate the reasons behind these preoperative delays with the goal of correcting inequities inherent in the medical system.

Computational Analysis of Average Waiting and Arrival Times

Over the years, the drift from public to private schools has overwhelmed parents' and guardians' imagination due to teachers' and pupils' waiting times and the influence of parents in private and public schools. The government has also slowed parental emotions to send their children to public schools due to the lack of basic teaching tools, well-trained teachers, and vital infrastructure in many states in Nigeria. As a result, private individuals with inadequate educational management backgrounds started establishing schools as a business concern without proper regulations on teachers' recruitment, vital and basic school facilities. The proliferation of substandard private and public schools has led to waiting time problems. This article describes novel computational procedures to compute the average waiting and arrival time. The procedures investigate teachers and pupils' average arrival and waiting times for public and private primary schools in each class in a local government area in Delta State, Nigeria. A simple random sampling procedure was applied. The study intends to determine whether the mean waiting or arrival time of teachers and pupils for each class for the period under review is equal. The data set was subjected to further mean analysis and analysis of variance. The F statistic was obtained and compared with the F critical value, which resulted in the rejection of the null hypothesis at a 5% level of significance. The analysis indicated that the average waiting time for teachers' arrival in each class is not equal and the average waiting time for pupils' arrival is not equal, hence the null hypothesis's rejection. The study revealed that the average waiting time for public and private school pupils in the schools investigated is 17 minutes. In comparison, the average waiting time of teachers for both public and private schools is approximately 6 minutes. This study affirmed that waiting time problems exist in public and private schools.

Multidisciplinary Lupus Nephritis Clinic Reduces Time to Renal Biopsy and Improves Care Quality.

ObjectivePatients with lupus nephritis (LN) have a 26‐fold higher mortality rate compared with their peers. Kidney biopsy, the gold standard diagnostic method for LN, may have an average wait time of more than 50 days. Other gaps in quality process measures during LN visits have also been reported. A subspecialty multidisciplinary clinic (MDC) can provide better care and quality in LN; therefore, we aimed to examine how an LN MDC impacted time to biopsy, time to treatment, and other quality measures.MethodsWe included all validated patients with LN who underwent diagnostic kidney biopsies between the 2011 to 2017 pre‐MDC period and the 2018 to 2020 post‐MDC period. We compared time to biopsy and treatment and quality measures between the two periods and examined factors associated with timely LN diagnosis, defined as a biopsy within 21 days.ResultsDuring the pre‐ and post‐MDC periods, 53 and 21 patients with LN underwent a diagnostic biopsy, respectively. We found a decrease in the median time to biopsy from 26 days to 16 days after starting the LN clinic (P = 0.014). Beyond clinical factors, the presence of social factors, such as being of a non‐White race and having food insecurity, were associated with 54% lower odds of timely diagnosis (adjusted Hazards Ratio [aHR] = 0.46; 95% confidence interval: 0.22‐0.93; P = 0.031). We found higher odds of quality measure performance during the post‐ versus pre‐MDC period.ConclusionWait times to diagnose LN decreased by 40% and higher quality measure performance was noted after establishing an LN MDC. Systemic and social barriers predicted delays in diagnosis that may be addressed by MDCs.