Patient Wait Research Articles

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

The Impact of Health Administration on Healthcare Quality: A Systematic Review of Effective Management Practices

Health administration plays a crucial role in shaping the quality of healthcare delivery by implementing effective management practices that enhance patient outcomes and operational efficiency. This systematic review aims to examine the impact of key administrative practices on healthcare quality, focusing on strategies that improve patient satisfaction, reduce errors, and optimize resource utilization. A comprehensive literature search was conducted across databases including PubMed, Scopus, and Google Scholar, reviewing studies published from 2016 onwards. Inclusion criteria targeted research on management practices such as resource allocation, leadership models, operational efficiency, and quality assurance systems. Findings indicate that health administration practices, particularly in leadership and operational efficiency, have a significant positive effect on quality metrics, such as reduced patient wait times, lower readmission rates, and increased patient satisfaction. However, challenges like limited resources and resistance to change often hinder implementation. This review underscores the need for adopting evidence-based administrative strategies and suggests directions for future research to further enhance healthcare quality through effective management practices.

Healthcare Internet of Things system implementations for COVID-19 prevention

BackgroundIn response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services.Methods and FindingsTo address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children’s hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected.ConclusionIn conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation.

Using artificial intelligence and machine learning to reduce patient wait times and increase patient satisfaction.

Madam, Prolonged patient waiting time has long been a significant source of dissatisfaction among patients. Waiting time has been recognized by WHO as one of the key elements of a responsive healthcare system [1]. A study conducted by Bleustein et al. suggested that longer wait times negatively impacted the patient’s confidence in the care provider and the perceived quality of care.[2] Furthermore, in large hospital settings, patients are expected to queue up multiple times before meeting with their physician, which amplifies their stress. We would like to highlight that the incorporation of artificial intelligence (AI) in healthcare can help reduce patient and physician stress. Strategies such as telemedicine and online registration have been acquired in efforts to resolve this issue but these techniques may burden doctors with extra responsibilities. However, introducing an AI-based physician assistance programme, which was able to model itself on the decision-making requirements of the doctor significantly reduced the wait time and the burden on the hospital [3]. Xiaoqing Li et al developed an AI-based model that helped patients automatically order any investigations. Implementation of the model reduces waiting time while also playing a pivotal role in creating alternative methods to improve patient flow. [4] The results of these studies prove that implementation of an AI-based model in daily medicine has helped improve patient experience and minimize hospital burden. The implementation of AI in place of traditional methods, helps enhance patient satisfaction by not only significantly reducing waiting time but also ensuring that patients receive care appropriate to their condition. Moreover, it helps reduce, the workload on the physician and enables them to focus more on the patient. Being a new concept in healthcare AI may not be free of error. This might lead to inaccuracies in medication prescription, investigations, imaging, scheduling of appointments and diagnosis in atypical cases. Patient confidentiality is also an extremely important aspect of healthcare and in an attempt to protect the privacy of the patients, AI models may not have free reign over the patients’ data which may prevent them from assisting in medical practices. Furthermore, there are no organizations that customize the AI models according to the needs of the individual hospital or clinic. Moreover, human interaction is a critical aspect of healthcare which would be significantly impacted by the use of AI which could result in patients being unable to express themselves or feeling a sense of apathy. Furthermore, developing countries are not well equipped to implement AI in daily practice, hence it would not be a practical solution for them. [5] Further research needs to be conducted with regards to the use of AI models in hospitals to understand how they impact patient wait times and satisfaction, alongside their ability to optimize healthcare.

Improving Patient Wait Times on the First Day of Radiotherapy Treatment

Long wait times on starting day of radiotherapy (day 1) can cause dissatisfaction among both patients and healthcare providers. Reducing these wait times will decrease stress and decongest hospital facilities especially in current coronavirus disease 2019 times. A multidisciplinary core team was formed during the Stanford-India Collaborative Quality Improvement training to reduce the median wait times on day 1 of treatment from 6 to 4.5 hours (a 25% reduction). Several factors were identified on the fishbone diagram, and key causes were identified using a Pareto chart and action prioritization matrix. The Plan-Do-Study-Act Cycle strategy was undertaken for the identified interventions. The outcome measure was time from arrival at the hospital to entry into a treatment room. Data were obtained from time charts at various stations and electronic records. The secondary measures were visual analog scale (VAS) scores, 80th percentile wait times, and the day-2 delay percentage. The balancing measure was “new errors” due to interventions. The interventions included the completion of all administrative tasks not needing patients' presence on the day before day 1. Baseline data from 198 patients and postintervention data from 160 patients were compared and analyzed. The median wait time at baseline, which was 6 hours, was reduced to 4.2 hours. The VAS score showed 70.4, 67.7, and 71.9% satisfaction for the resident physician, therapists, and patients, respectively. The 80th percentile wait times reduced from 8 to 5.7 hours; and the day 2 starting rate decreased from 22.5 to 2.04%, with no new errors reported. Radiotherapy day 1 wait times can be safely decreased, leading to improved satisfaction among patients and healthcare providers, by utilizing classic quality improvement methods and tools.

HYBRID ARCHITECTURE WITH IMPROVED SCORE LEVEL FUSION FOR PATIENT WAITING TIME PREDICTION

Hospitals are overcrowded in proportion to the massive increase in population, making it difficult for hospitals to manage or arrange or shorten patient wait time during medical care. However, it is difficult to overstate the significance of patient waiting time management and predictability. In most of the hospitals, the clinical workflow is defined and driven by the patient queues. Predicting the incoming patients and waiting times has thus emerged as one of the most crucial clinical management techniques. Our proposed work aims to address the limitations of current waiting time prediction models by providing a more sophisticated, data-driven approach that can adapt to the complexities of healthcare environments. This work focuses on developing a new model for predicting the patient’s waiting time. The primary aim of this research is to create a reliable predictive model capable of accurately anticipating patient waiting times. This innovation aims to enhance both patient satisfaction and operational efficiency within healthcare settings. By offering healthcare administrators a tool to better manage patient flow, allocate resources efficiently, and minimize waiting times, this model seeks to optimize overall healthcare service delivery. The initial process carried out is pre-processing which includes data acquisition and improved [Formula: see text]-score normalization. Subsequently, entropy features, correlation features, and improved mutual information (MI) features are extracted. Then, prediction is carried out using hybrid classifier (HC) that involves a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Finally, improved score level fusion (ISLF) is introduced to get absolute outcomes on WTP. The analysis outcomes on varied error metrics show the efficacy of the proposed WTP model.

Outcomes of Locally Advanced Rectal Cancer Patients Following Complete Clinical Response After Neoadjuvant Treatment.

Background Watchful waiting and non-operative management of patients with complete clinical response to neoadjuvant treatment in locally advanced rectal cancers is an emerging practice now, especially in patients whose surgery would require permanent stoma. Methodology This is a retrospective study of patients presenting to the National Cancer Institute, Cairo University, Egypt from January 2005 to December 2019 with pathologically proven locally advanced rectal cancer who had a complete clinical response after neoadjuvant treatment. Patients who underwent surgery after achieving complete clinical response and patients who were kept under watchful waiting were compared in terms of overall survival (OS), disease-free survival (DFS), metastasis-free survival (MFS), and local recurrence-free survival (LRFS). Results Of the 51 patients identified, 31 (61%) went to surgery, and 20 (39%) were kept under watchful waiting. There was no difference in five-year OS between both groups (65% for both, p = 0.57). Five-year DFS for the watchful waiting group was 68% versus 62% for the surgery group (p = 0.75). Five-year LRFS was 100% in the watchful waiting group versus 83% in the surgery group (p = 0.15). Conclusions Watchful waiting after a complete clinical response to neoadjuvant treatment in locally advanced rectal cancer is feasible without compromising disease-related outcomes, especially in patients with distal rectal cancers requiring permanent stoma after surgery. However, further prospective validation is needed.

Comparative Effectiveness of Teledermatology Versus In-person Consultations: A Systematic Review of Diagnostic Information Elements

Context: Teledermatology is delivered through two primary methods: Live video conferencing, which enables real-time interaction between patients and doctors, and the store-and-forward technique, where images of skin lesions are sent over the internet for later examination. Objectives: This systematic review evaluates the concordance between in-person and telemedicine follow-up methods in dermatology. Methods: In February 2024, a comprehensive literature search was conducted across PubMed, Medline, ISI Web of Science, and the ACM Digital Library. Keywords such as "teledermatology," "telemedicine," and "mobile health" were used, following the PICOS criteria. Results: Initially, 716 articles were identified from the databases, along with four studies from the gray literature search. After removing duplicates, screening studies, and applying the inclusion and exclusion criteria, 21 studies were selected for the final review. These studies detailed follow-up procedures and assessed both direct and indirect costs and effectiveness. The consensus indicates that teledermatology reduces costs and satisfies both patients and healthcare professionals. Conclusions: Teledermatology has been shown to potentially increase the efficiency of follow-up care compared to traditional methods. However, further research is needed to develop standardized evaluation protocols and to more accurately measure and validate key variables. Publications suggest that teledermatology is cost-effective, reduces patient wait times, extends access to dermatological care in remote areas, and enhances satisfaction among patients, doctors, and nurses.

HARNESSING BIG DATA AND MACHINE LEARNING FOR TRANSFORMATIVE HEALTHCARE INFORMATION MANAGEMENT

This study explores the transformative impact of big data and machine learning (ML) on healthcare information management, drawing insights from a comprehensive review of 50 peer-reviewed articles published between 2010 and 2023. The analysis highlights significant advancements in predictive analytics, with ML models improving disease prediction accuracy by up to 40% compared to traditional methods, as demonstrated in multiple studies. Personalized medicine has also benefited, with ML-driven treatment plans showing a 25% improvement in chronic disease management and a 15% reduction in treatment failures, as reported in several case studies. Additionally, integrating big data and ML into healthcare operations has led to a 35% reduction in patient wait times and a 20% decrease in hospital readmissions. However, challenges such as data fragmentation, privacy concerns, and algorithmic bias were identified in over 60% of the reviewed studies, indicating the need for stronger regulatory frameworks. This review underscores the potential of big data and ML to revolutionize healthcare while also calling attention to the ethical and practical challenges that must be addressed to ensure equitable and transparent healthcare solutions.

Simulation-based emergency department staffing and scheduling optimization considering part-time work shifts

Emergency department (ED) physicians have long been in short supply, which causes serious overcrowding and the excessive waiting of patients in many countries. ED managers are under great pressure to make staffing and scheduling decisions. To address this problem, this paper introduces part-time work shifts with fixed starting times and durations in peak hours to increase the flexibility and reduce patients’ waiting times. Then, a two-stage framework is proposed to optimize the weekly staffing and scheduling decisions with both fixed and part-time work shifts. Stage I is a weekly staffing problem to determine the number of physicians for both types of work shifts. The weekly staffing problem is decomposed and then solved using a simulation optimization method. Specifically, an adaptive partitioning empirical stochastic branch and bound (ESB&B-AP) algorithm is proposed by employing adaptive cut-generation methods in the process of feasible region partitioning, which significantly improves the searching efficiency of the algorithm. Stage II is a scheduling problem to determine the work schedules of physicians based on a predetermined set of work patterns. Numerical experiments validate the advantages of part-time work shifts, as well as the effectiveness of the two-stage optimization framework and the ESB&B-AP algorithm.

Advanced practice providers in the medical genetics workforce: A nationwide survey

PurposeThis study characterizes the current landscape of genetics advanced practice providers (APPs) in the United States. MethodsA 35-question survey was emailed to the Genetics APP Listserv in the fall of 2023. Questions represented 5 domains: demographics, practice, onboarding, compensation, and perceptions. ResultsA total of 105 genetics APPs (93%) completed the survey. Genetics APPs evaluate various patient types and populations in multiple settings, working an average of 41.3 hours and seeing 15 patients weekly. Nearly all see new (96%) and follow-up (98%) patients and utilize telemedicine (93%). Half (51%) have only worked in the genetics specialty during their career. Overall, APPs are generally satisfied with their career as a genetics APP (98%) and work-life balance (86%), and most (86%) feel that they function at the top of their scope. ConclusionStudy findings elucidate the current state of genetics APPs. Results define the characteristics and role of an APP in the genetics specialty and will guide employers and genetics organizations to utilize APPs at the top of their scope and recruit new APPs to this exciting field. A collaborative effort is needed to increase the overall genetics workforce, decrease patient wait times, and increase access to genetics care.

Outpatient appointment systems: A new heuristic with patient classification

PurposeThis study aims to develop a heuristic for an outpatient appointment system considering patient classification. Design/methodology/approachThe proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. FindingsHaving Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46 % and 5.94 % among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. OriginalityThe single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts Practical implicationsThe proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Improving patient flow in free medical service by introducing a process map: a quality improvement study

Introduction: The tradition of charitable medical treatment can be drawn back to the early years of Christianity when the Church growth closely related to the care for the sick as well as other charitable activities. Though its purpose is charity, free medical service should also improve the quality of service. It can be achieved through the implementation of lean six sigma tools. One of its tools is a process map. Process map in Lean Six Sigma is commonly used as a diagnostic tool to determine the possible bottlenecks in the system. Methods: This is a brief pre and post quality improvement study. Before the implementation of a process map suggested by the researcher (period I), the patient flow was not properly organized. After the lunch break, the researcher introduced a new process map and briefed it to the committee. The patients flow in the afternoon (Period II) was using the process map. Patient numbers and wait times were measured within the time interval of this study. Results: Out of 159 patients who served during the period of this study, 63 (40%) were male and 96 (60%) were female. During Period I of the medical service, 50 patients (31%) were treated with average 17 patients per hour and approximate waiting time 36 minutes. After implementation of process map (Period II) the number of patients served were 109 (69%) with an average 36 patients per hour and an approximate waiting time of 17 minutes. Conclusion: The implementation of process map in free medical service may assist in increasing the number of patients served and reduce the waiting time as a result. Apart from reducing the confusion of where the patients were to go next, the process map also help the committee to identify possible hinderances and address the impediments to efficient charitable medical care.

Why do patients who are triaged as low-acuity visit the emergency department? – A Polish perspective

BackgroundEmergency departments (EDs) worldwide are dealing with overcrowding, system fragmentation, and coordination problems, which impact patient wait times, staff job satisfaction, and patient outcomes. Inappropriate ED visits, particularly those for low acuity conditions, exacerbate these challenges. However, the motivations behind these visits are poorly understood, with limited data from the patient perspective. This study investigated patient-reported motives behind ED visits triaged as low acuity in Poznan, Poland, to propose health care system flow enhancements. Material and methodsA cross-sectional survey and retrospective chart review were conducted in the ED of the Hipolit Cegielski Medical Center in Poznań, Poland, over three months in 2022–23. Patients who were triaged to have low acuity conditions were invited to participate in the survey. The data collected through the questionnaire included patient and ED visit characteristics. Additional information on the visits was extracted from the patient charts. Main resultsThis study involved 293 patients who underwent low-acuity triage. Among them, 58 % were deemed to have conditions that could have been treated in primary care. Most of the patients (74 %) visited the ED of their own volition due to concerns about their health. Other reasons for ED attendance were challenges accessing primary care or a specialist clinic (11 %), system navigation problems (5 %), or a lack of trust in their primary care provider (2 %). ConclusionsThis study showed that of the patients surveyed, the majority had conditions that could have been treated outside of the ED setting. We recommend prioritizing education, particularly among younger adults, to increase awareness about nonurgent care options while improving health care policies.

Patient-Centered Care Model’s Effectiveness in Reducing Patient Waiting Time in the Emergency Department: A Systematic Literature Review

<b><i>Introduction:</i></b> The emergency department (ED) is an essential component of any hospital that offers round-the-clock urgent treatment to patients in critical condition. However, lengthy waiting times are expected, which may negatively affect the patient’s health and overall experiences. This systematic review aims to assess the effectiveness of patient-centered care (PCC) models in reducing the time patients spend waiting in the emergency room by synthesizing the available evidence. This was conducted by following the following three research objectives: (i) to identify patient-centered care models implementation in hospital settings, (ii) to assess the effectiveness of patient-centered care models in improving patient outcomes in hospital settings, and(iii) to investigate the impact of prolonged waiting time on healthcare utilization in the ED. <b><i>Methods:</i></b> In this systematic review design to be included, an article had to be: full text, in English, peer-reviewed, address the evaluation of the effectiveness of the PCC model in reducing ED wait times, and the ED. Studies were excluded if they were abstracts, not fully available, published in any language other than English, were not peer-reviewed, did not meet the date of search, and were systematic reviews. The study was conducted by computerized databases which included PubMed, MEDLINE, Embase, Scopus, Web of Science, and CINHAL were searched between April 1, 2023–April 17, 2023. The study used the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement which has been updated to PRISMA 2020, a reporting guideline designed to address the issue of inadequate systematic review reporting. All the articles that satisfied the predefined inclusion criteria were independently assessed for risk of bias by two independent reviewer. <b><i>Results:</i></b> A systematic review design searched the literature on ED context with PCC as the intervention to reduce the waiting time. Two independent reviewers screened the articles (3,114) using the PRISMA and 27 articles were included. <b><i>Conclusion:</i></b> Long waiting times resulted in many adverse events for patients, such as delayed treatment and poorer clinical outcomes. PCC can exert a very impressive role contribution to EDs as patients should be integrated with health care providers in their health issues and treated from all perspectives.

The Proposed Model for Pharmacy Led Med-to-bed Discharge Service in Tertiary Care Hospital

Introduction: The pharmacy-led discharge medication delivery to bedside (Med-to-bed) service in a tertiary care Johns Hopkins Aramco Healthcare (JHAH) hospital aims to optimize medication management during patient transitions from admission to discharge, especially for those with chronic conditions. The service entails the direct delivery of discharge medications to patients at the pharmacy following their discharge from inpatient wards, accompanied by comprehensive medication counseling. The pharmacy-led Med-to-Bed service aims to reduce patient wait times and improve overall clinical outcomes. Methods: This article highlights common challenges in medication reconciliation and post-discharge prescription fulfillment, highlighting the need for standardized protocols and improved hospital adherence to ensure seamless care transitions. It also discusses future considerations for optimizing the Med-to-Bed service, including proposed pharmacy renovations, installing a dedicated robotic dispensing system, and providing private spaces for patient counseling during medication pickup. Additionally, the proposal suggests deploying more pharmacy staff to address delayed discharge orders and making systemic enhancements to the EHR system to streamline prescription pickup. Result: The successful implementation of the proposed pharmacy-led Med-to-Bed service demonstrates the efficacy of interdisciplinary collaboration among nurses, physicians, and pharmacists. Through this collaborative approach, the Med-to-Bed service has shown tangible improvements in operational efficiency and patient outcomes, underscoring its potential to transform healthcare delivery models. Conclusion: Overall, this proposed model provides valuable insights into the transformative impact of integrated care initiatives like the Med-to-bed service in tackling complex medication management and enhancing quality patient care in hospitals.

Health and Economic Outcomes of Pembrolizumab in the Treatment of Metastatic Non-small Cell Lung Cancer (mNSCLC) and Melanoma in Italy.

In Italy, the management of metastatic non-small cell lung cancer and melanoma leads to significant healthcare challenges, necessitating cost-effective treatment strategies and offering valuable insights for healthcare policymakers and stakeholders. This study was designed to assess the costs, quality-adjusted life-years (QALYs) and disability-adjusted life-years (DALYs) associated with the health and economic outcomes of (1) pembrolizumab-combined chemotherapy administered as a first-line treatment for metastatic non-squamous and squamous non-small cell lung cancer (NSCLC) where the tumour presents with a programmed death-ligand 1 expression level <50% and of (2) adjuvant pembrolizumab treatment for stage III melanoma. Three cost-effectiveness models developed by MSD were investigated for each treatment indication. A unique model was built to assess the overall effect of pembrolizumab versus chemotherapy or watchful waiting in patients with lung cancer or melanoma, respectively. Theoretical cohorts of patients with metastatic squamous and non-squamous NSCLC were followed over time using a partitioned survival model with weekly cycles. A weekly cycle Markov model was employed for melanoma. The analysis was conducted from the Italian National Health Service perspective, considering a time horizon of 40 years (lifetime). A single closed cohort of treatable patients was followed over time for each indication (4000, 7000 and 900 for NSCLC squamous, non-squamous and melanoma, respectively). The costs evaluated included those for adverse drug events, non-drug disease management, subsequent treatment and terminal care. Drug acquisition and administration costs were excluded. For each treatment indication assessed, pembrolizumab produced downstream direct cost offsets (- €122,498,568, -€133,369,076 and -€32,993,242 for NSCLC squamous, non-squamous and melanoma indications, respectively), increased quality of life (+2088, +5317 and +2307 QALYs for NSCLC squamous, non-squamous and melanoma indications, respectively) and reduced disability (-2658, -7202 and -3029 DALYs for NSCLC squamous, non-squamous and melanoma indications, respectively). Across indications, the total cost offsets of pembrolizumab were -€288,860,885, with 9712 QALYs gained and 12,889 DALYs avoided. The analysis demonstrated that, compared with chemotherapy, pembrolizumab is more cost effective in Italy as a first-line treatment in patients with metastatic squamous or non-squamous NSCLC and, if compared with watchful waiting, as adjuvant treatment in patients with stage III melanoma. The present analysis suggested that pembrolizumab use could lead to important health benefits for patients while offsetting a portion of cancer care costs.

A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system

Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations.An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance.The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency.The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance of effective scheduling in managing client waitlists during treatment in the healthcare sector, such as elective surgery, home healthcare, physical and mental therapies, and other service industries like education, public consultations, and government services. The IOB system can be integrated with Artificial Intelligence (AI)-based tools and machine learning methods and combined with other service software to optimize appointment scheduling and improve system performance.

Dual-agent dose-finding in Phase I clinical trial-An extension of rapid enrollment design.

Dual-agent treatment has become more and more popular in clinical trials. We have developed an approach called rapid enrollment dual-agent design (REDD) for dose-finding in Phase I clinical trials. This approach aims to administer treatment to patients using a dose combination that is highly probable to be the target dose combination. Unlike other non-model-based designs, rapid enrollment designs (RED and REDD) do not require waiting for all patients to complete an assessment before the assignment of the next participant. Simulations showed that across several scenarios, the average performance of REDD is comparable to that of the Bayesian optimal interval (BOIN) design and the partial order continual reassessment method (POCRM). The simulation results of REDD for late-onset toxicity assessments demonstrated that assigning patients to a dose combination as they are being enrolled, without waiting for the most recent cohort of patients to complete their follow-up, does not significantly compromise the quality of the maximum tolerated dose (MTD) estimation. Instead, it saves a considerable amount of time in clinical trial enrollment. User-friendly online applications have also been created to further facilitate the adoption of rapid enrollment designs in Phase I trials. In summary, being similar to BOIN and POCRM in performance, REDD is an approach that is easily comprehensible, straightforward to implement and offers an advantage of enrolling patients without having to wait for all current patients to complete their follow-ups for toxicity.

How do dental practices respond to changes in scope of practice regulations?

Regulations that restrict the tasks that credentialed workers are allowed to perform may affect a firm's input choices, output, and which part of the market the firm serves. Using dental practice survey data from 1989 to 2014 and a stacked difference-in-differences design, this paper examines the effects of state-level scope of practice regulations on the behavior of dental practices. Results suggest that scope of practice deregulation in regards to dental hygienists' ability to administer nitrous oxide or local anesthesia is associated with fewer dentist visits per week in the short-term, lower patient wait times, and an increased likelihood of treating lower revenue generating publicly insured patients. There is weak evidence that scope of practice deregulation alters a practice's labor inputs.