Limited Service Capacity Research Articles

A feasibility trial of olanzapine for young people with Anorexia Nervosa (OPEN): clinicians’ perspectives

BackgroundThe OPEN feasibility trial testing olanzapine in anorexia nervosa (AN) in young people (YP) was not successful due to poor recruitment. This study aims to understand clinicians’ views and experiences of using olanzapine in AN and the challenges in implementing the trial in National Health Service (NHS) clinical settings.MethodsWe conducted qualitative interviews with eating disorders (ED) clinicians involved with the study (n = 11). Framework analysis was applied to qualitative data to identify barriers and facilitators to recruitment and study implementation. A web-based semi-structured Qualtrics survey was administered to ED clinicians (n = 24). Findings from the survey were used to corroborate and expand on the information derived from qualitative interviews.ResultsQualitative analysis identified four main themes: (1) Acknowledging Service User (SU) / Family Concerns, (2) Prioritising person-centred care, (3) Limited Service Capacity and (4) Study eligibility criteria. Subthemes are outlined accordingly. Clinicians appeared confident addressing SU concerns around olanzapine in clinical discussions, but timing was critical, and olanzapine was considered one aspect of treatment that needed to align with their holistic approach. Service pressures restricted opportunities for recruitment and the ability to offer regular review. At the same time, some YP were ineligible for the trial, as they were already taking olanzapine, or needed to be prescribed it more promptly than the study procedures allowed. Survey findings underlined confidence in prescribing and informing on olanzapine, the various possible benefits of olanzapine besides weight gain, and the importance of therapeutic alliances and informed consent. Both data sets highlight the need for further evidence on long-term safety, side effects and efficacy of olanzapine use for AN. Where clinical service capacity is at a premium, research implementation is not prioritised, particularly in intensive clinical settings.ConclusionsFindings provide first-hand insight into individual and systemic challenges with research implementation in the NHS, which need to be considered when designing future clinical research studies. We emphasise a person-centred approach when discussing olanzapine to consider a holistic recovery from AN beyond weight-gain as an isolated outcome for improvement.

Characteristics of quality improvement interventions to improve physical healthcare in mental health settings: a scoping review protocol

IntroductionMental health concerns globally impact millions of people, resulting in significant financial impact and adverse health outcomes. People living with mental health concerns are at higher risk of developing physical...

Health System and Individual Barriers to Supporting Healthy Gestational Weight Gain and Nutrition: A Qualitative Study of the Experiences of Midwives and Obstetricians in Publicly Funded Antenatal Care in Tasmania, Australia.

Individual and health system barriers can impede clinicians from supporting weight-related behaviour change for pregnant women, particularly in publicly funded antenatal care accessed by women from diverse socioeconomic backgrounds. The aim was to understand clinicians' experiences of supporting healthy gestational weight gain for pregnant women in a publicly funded antenatal setting. The work was undertaken to guide the implementation of systems changes, resource development, and workforce capacity building related to nutrition, physical activity, and gestational weight gain in the service. The qualitative descriptive study used purposive sampling and semi-structured interviews conducted between October 2019 and February 2020. Nine midwives and five obstetricians from a publicly funded hospital antenatal service in Tasmania, Australia participated. Interview transcripts were analysed using inductive thematic analysis. The three dominant themes were prioritising immediate needs, continuity of care support weight-related conversations, and limited service capacity for weight- and nutrition-related support. The subthemes were different practices for women according to weight and the need for appropriately tailored resources. Improving access to continuity of care and clinician training, and providing resources that appropriately consider women's socioeconomic circumstances and health literacy would enhance the ability and opportunities for clinicians to better support all women.

Mapping community-based youth mental health services in Tasmania, Australia.

Tasmania has the most rurally and remotely dispersed population of young people in Australia with high rates of youth experiencing mental ill health and socioeconomic disadvantage. Standard descriptions of mental healthcare provision are necessary for evidence-informed mental healthcare policy, planning and implementation. This systematic scoping study aimed to: (1) map and describe the characteristics of community-based youth mental health services (including substance-use) for young people in Tasmania, Australia and (2) identify gaps in service accessibility and provision. A list of eligible services was developed through a systematic search and consultation with key stakeholders. Data were collected from a representative from each eligible service via an interview or online survey. A standardised framework was used to classify, describe and map services. Thematic analysis was used to analyse service providers' perceived gaps to service access and provision. Twenty-eight community-based mental health services for youth were identified, predominantly located in the major city of Tasmania's three service regions. Service gaps include the 'missing middle', lack of integrated supports and limited service capacity. The findings highlight the limited availability, accessibility and capacity of youth mental health services across Tasmania. Recommendations focus on increasing accessibility of rural/regional supports, provision of assertive outreach, psychosocial support, integrated care and strengthening the rural mental health workforce. These findings may inform the (re)design/(re)development of community-based youth mental health services in Tasmania. The findings may also guide evidence-informed mental health service planning, decision-making, development and implementation of integrated models of youth mental health care across Australia.

“You're only a receptionist, what do you want to know for?”: Street-level bureaucracy on the front line of primary care in the United Kingdom

IntroductionIn care settings across the globe non-clinical staff are involved in filtering patients to the most appropriate source of care. This includes primary care where general practice receptionists are key in facilitating access to individual surgeries and the wider National Health Service. Despite the complexity and significance of their role little is known of how the decision-making behaviors of receptionists impact policy implementation and service delivery. By combining the agent-based implementation theory of street-level bureaucracy with a tri-level analytical framework this work acknowledges the impact of the decisions made by receptionists as street-level bureaucrats and demonstrates the benefits of using the novel framework to provide practical insight of the factors influencing those decisions. MethodsA secondary analysis of qualitative data gathered from a series of semi-structured interviews conducted with 19 receptionists in the United Kingdom in 2019 was used to populate a tri-level framework: the micro-level relates to influences on decision making acting at an individual level, the meso-level influences at group and organizational levels, and the macro–level influences at a societal or policy level. ResultsAt the micro-level we determined how receptionists are influenced by the level of rapport developed with patients and would use common sense to interpret urgency. At the meso-level, influences included their position at the forefront of premises, the culture of the workplace, and the processes and protocols used by their practice. At the macro-level, participants described the impact of limited health service capacity, the lack of mandatory training, and the growth in the use of digital technologies. ConclusionsStreet-level bureaucracy, complemented with a tri-level contextual analysis, is a useful theoretical framework to understand how health workers, such as receptionists, attempt to provide universality without sufficient resource, and could potentially be applied to other kinds of public service workers in this way. This theoretical framework also benefits from being an accessible foundation on which to base practice and policy changes.

Service System Analysis with Reneging at the General Clinic of Green Care

One of the problems often encountered in daily life is waiting lines in service systems. A long waiting line in a service system usually leads to customers leaving the system, resulting in losses for the service provider. The general clinic of Green Care is one of the clinics with a First Come First Served service discipline and a limited service capacity of 30 patient. The clinic often experiences patient congestion while waiting for service, which causes some patient to renege due to the long waiting time. This study discusses the service system at the Green Care General Clinic, considering impatient customers, specifically those who renege while waiting for service. The study was conducted by collecting data on arrival time, service time, and cancellation time while waiting for service. Based on the test results, the service model used in the general clinic is M/M/1/N. The research findings show that the service system at the general clinic has a high system utilization rate (ρ=80,84%), indicating a need for additional service facilities. The service model becomes M/M/2/N with the system utilization rate being ρ=40,42%.

Performance Under Limited Service Capacity for Load Balancing EV Charging Network

The economic benefit and energy security brought by renewable energy have greatly promoted the development of microgrid (MG). However, the load demand varies from area to area, resulting in the unbalanced load distribution among MGs. With the perfection of charging facilities, electric vehicle (EV) has attracted more consumers in the automobile market. A large number of EVs connected to MGs without coordination may threaten the safe operation of the power grid. Moreover, considering the constraints of economic factors, the number of charging piles in the MG is limited, i.e., the service capacity of MG is limited. Based on this, the impact of service capacity on the load balancing and average user time is studied. The load balancing matching strategy (LBMS) is adopted to lead the EV charging behavior. We find that the larger service capacity means that the load distribution is more balanced and the average user time is shorter. Furthermore, we find the optimal service capacity of the power grid. When the service capacity is more than that, it will no longer improve the system performance and cause the waste of charging facilities.

Rural communities face more than an opioid crisis: Reimagining funding assistance to address polysubstance use, associated health problems, and limited rural service capacity.

Rural communities in the United States face unique challenges related to the opioid epidemic. This paper explores the substances and substance-related health problems that pose the greatest concern to rural communities that received funding to address the opioid epidemic and examines their reported capacity to address these challenges. This paper analyzed data collected as part of quarterly progress reporting from multisector consortiums across 2 cohorts of grantees funded to reduce the morbidity and mortality of opioids. Consortium project directors ranked the top 3 issues in their community in each of the following categories: (1) drugs of concern; (2) drugs with the least capacity to address; (3) related problem areas of concern (eg, neonatal abstinence syndrome [NAS]); and (4) related problem areas with the least capacity to address. Methamphetamines, fentanyl, and alcohol were the substances rated as most problematic in rural communities funded to address the opioid epidemic across all reporting periods. Over 40% of respondents ranked methamphetamine as a top concern and the substance they had the least capacity to address. This was nearly double the percentage of the next highest-ranked substance (fentanyl). Overdoses, NAS, and viral hepatitis constituted the top-ranking related concerns, with limited capacity to address them. Multiple drug and concomitant problems coalesced on rural communities during the opioid epidemic. Funding communities to address substance use disorders and related problems of concern, rather than targeting funding toward a specific type of drug, may result in better health outcomes throughout the entire community.

Towards the sustainable development of logistics system model: A system dynamics approach.

The contradiction between the limited service capacity of system and the explosive growth of demand has hampered the sustainable development of logistics system. Taking into account the structure of logistics system, this study introduces a system dynamics approach to explore the complex correlation and coupling structure of system, analyzes the multiple feedback loops and design the different scenarios. Results show that the validity and rationality of logistics system model, and the error percentage of GDP and logistics demand factors less than 6%. The influence of the investment in reverse logistics, logistics management, information and organizational management factor on the service quality of logistics system increases in turn. Additionally, adjustment of industrial structure has a significant impact on the investment in information management factor, and highway transportation plays a key role in influencing logistics energy consumption and carbon emissions indexes. The findings can provide valuable references and methodologies, as well as support for decision-making in the sustainable development of logistics system.

Monitoring the impact of the COVID-19 pandemic on problematic gambling and gaming: an international key informant survey

ABSTRACT This paper reports a qualitative survey of experts in problem gaming and/or problem gambling from 19 countries worldwide on the impact of the COVID-19 pandemic on problematic gaming and gambling. Twenty-seven problem gaming experts reported (i) no licensing changes, (ii) few existing or newly introduced policies or regulations, (iii) overall increases in gaming, (iv) adverse health-related outcomes, (v) media coverage of gaming and eSports, (vi) limited health service capacity for treatment and prevention, (vii) difficulty in connecting with patients, and (viii) adaptations including transition to telemedicine. Twenty-eight problem gambling experts reported (i) no changes in policies or regulation, (ii) increased activity of off-shore betting companies, and (iii) increases in online gambling, though (iv) decreased expenditure due to closure of land-based gambling venues, (v) adverse health-related outcomes, (vi) some financial benefits from savings, (vii) few and poorly accessible treatment facilities and (viii) transition to telemedicine. There is a need for: guidelines on the prevention and management of excessive gaming and gambling; studies examining the public health consequences of eSports viewing and participation and closure of land-based gambling venues on other forms of gambling; and longitudinal studies in a number of different areas described herein.

Encouraging preventive services in primary care through payment schemes

Strengthening the use of preventive services in primary care has become the key to solving the problem of a global increase in healthcare expenditures caused by aging populations and chronic diseases. In practice, whether devoting effort to preventive services can be a strategic decision when the limited working time keeps a general practitioner (GP) busy with diagnosis and treatment. Based on the above motivations and challenges, we are the first to focus on the conflict between treatment services and preventive services of a GP with limited service capacity in the healthcare field. This paper considers a public health system consisting of a funder, a GP, a specialist, and a pool of delay-sensitive residents. Four payment schemes are investigated in terms of encouraging the GP to deliver preventive services: (i) fee for service (FFS), (ii) capitation, (iii) GP fundholding, and (iv) penalty scheme. The result shows that the GP fundholding scheme and the penalty scheme are more effective in encouraging preventive service provision in primary care, whereas the other two schemes may result in underinvestment in preventive services. Further, we compare the performance of the GP fundholding scheme and penalty scheme by applying the optimal reimbursement, which refers to the amount that maximizes social welfare. Compared with the GP fundholding scheme, the penalty scheme can incentivize preventive services with lower expenditures for the funder. There are two additional interesting findings: (1) A larger reimbursement discourages the GP from delivering preventive services. (2) An increased marginal treatment cost always incentivizes the GP to allocate more working time to preventive services, thereby reducing the referral rate and saving costs. Counterintuitively, increasing the marginal cost of preventive services may also encourage the GP to deliver preventive services. Our analysis sheds light on how to encourage preventive service provision in primary care through payment schemes.

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.

In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research.

Service Capacity and Price Promotion Wars

Firms often engage in price promotion wars to gain market share from their competitors. However, poor customer satisfaction as a result of limited service capacity may significantly impact such a pricing strategy. In this paper, we consider a two-firm price competition model in which customers’ purchase decisions are affected by their anticipation of a poor service encounter. Our equilibrium analysis reveals that firms would be less aggressive in engaging in price cutting when customers care more about service quality and when service capacity is relatively low. Interestingly, when service capacity is close (but not exact) to covering one half of the total market demand, firms would adopt a mixed strategy with randomized pricing, driven by unilateral motives to either capture more market share (by lowering prices) or increase profit margin (by raising prices). We further show that having a superior service capacity presents a competitive advantage for a firm and such advantage can be preemptive. In an extended two-period model that allows for customer switching after a poor service encounter, we find that when service capacity is relatively low, firms may offer deeper price discounts in the first period if customers are forward looking than if they are myopic. Our numerical study confirms that the main qualitative insights obtained in our base model continue to hold when the customer switching behavior is considered. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4325 .

Blockchain-Empowered Space-Air-Ground Integrated Networks: Opportunities, Challenges, and Solutions

The terrestrial networks face the challenges of severe cost inefficiency and low feasibility to provide seamless services anytime and anywhere, especially in the extreme or hotspot areas (e.g., disaster areas, mountains, and oceans) due to limited service coverage and capacity. The integration of multi-dimensional networks consisting of space, air, and ground layers is expected to provide solutions in delivering cost-effective and ubiquitous Internet of things (IoT) services for billions of users and interconnected smart devices. Autonomous data collection, exchange, and processing across different network segments with minimal human interventions in space-air-ground IoT (SAG-IoT) can bring great convenience to consumers, however, it also suffers new attacks from intruders. Severe privacy invasion, reliability issues, and security breaches of SAG-IoT can hinder its wide deployment. The emerging blockchain holds great potentials to address the security concerns in SAG-IoT, thanks to its prominent features of decentralization, transparency, immutability, traceability, and auditability. Despite of the benefits of blockchain-empowered SAG-IoT, there exist a series of fundamental challenges in terms of efficiency and regulation due to the intrinsic characteristics of SAG-IoT (e.g., heterogeneity, time-variability, and poor interoperability) and the limitations of existing blockchain approaches (e.g., capacity and scalability). This article presents a comprehensive survey of the integration of blockchain technologies for securing SAG-IoT applications. Specifically, we first discuss the architecture, characteristics, and security threats of SAG-IoT systems. Then, we concentrate on the promising blockchain-based solutions for SAG-IoT security. Next, we discuss the critical challenges when integrating blockchain in SAG-IoT security services and review the state-of-the-art solutions. We further investigate the opportunities of blockchain in artificial intelligence and beyond 5G networks and provide open research directions for building future blockchain-empowered SAG-IoT systems.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.

BackgroundAsthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.ObjectiveTo enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.MethodsWe will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.ResultsWe have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.ConclusionsOur results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.International Registered Report Identifier (IRRID)PRR1-10.2196/27065

Provider time allotment tracking tool to effectively manage assignment commitments

Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.

Service Capacity and Price Promotion Wars

Price promotion wars have been commonly observed in the online retail industry. One intuitive explanation is that firms cut their prices in the current period, hoping for locking in customers for future profit. While such practice is prevalent, our understanding of the role of service capacity in firms’ price promotion strategy is lacking, especially when customers’ purchase experience may be negatively affected by firms’ limited service capacity. In this paper, we revisit the classic symmetric two-firm, two-period price competition model with customer switching costs, making the following modification|depending on the first-period demand and available service capacity, customers may be either satisfied or unsatisfied, which, in turn, determines a high or low switching cost in the second period. Our analysis of the model reveals three main insights. First, when service capacity is relatively limited, the magnitude of the first-period discount becomes smaller. As a result, limited service capacity makes the market less competitive, and firms’ profit improves. Second, perhaps most surprisingly, when the service capacity is limited but close to cover one half of the total market demand, competition intensifies, forcing firms to use a mixed strategy with randomized pricing in the first period. Third, having higher service capacity in an asymmetric capacity case can provide a competitive advantage for firms. Our numerical experiments further demonstrate that the randomized pricing insight carries over to the asymmetric capacity case, as long as one of the firms has a limited service capacity close to cover one half of the total market demand.

Cache Replacement Strategy With Limited Service Capacity in Heterogeneous Networks

Recently, cellular networks face a huge challenge to satisfy large increment of user demands for higher-speed and lower-latency communication service. One promising solution is to apply the cache technology in edge cache networks to reduce redundant data transmission. The cache technology is effective, but the cache capacity is limited. Researchers have proposed various cache strategies to efficiently utilize the limited cache capacity. However, most existing solutions have not taken into account the service capacity limitation of mobile devices. In this paper, we propose a cache replacement strategy for heterogeneous networks considering the limitation of service capacity and user mobility. In the cache replacement strategy, we utilize the user characteristics, such as user mobility and file popularity, to estimate the user demands, and then define the system cost. We formulate the cache strategy design as a mixed integer linear programming problem to minimize the system cost, and use Lagrangian relaxation and hierarchical primal-dual decomposition method to solve this problem. Numerical results show that the proposed cache strategy can significantly reduce the system cost and increase the cache hit ratio compared to the cache strategy that does not consider the limitation of user service capacity.

Comprehensive QoS Analysis of Urban Public Charging Network in Topology and Capacity With Its Application in Optimization

Booming electric vehicles (EVs) lead to degraded quality-of-service (QoS) of urban public charging network (UPCN) consisting of public charging stations (PCSs) and traffic network, such as prolonged waiting time at popular PCSs. UPCN QoS analysis creates broad prospects in QoS optimization by providing reliable tools to improve UPCN performance. Conventional methods focusing on the impact of network topology on QoS underestimates effects of network capacity and its distribution among PCSs. In this paper, we proposed a heterogeneous UPCN model with limited PCS service capacity, and constructed a QoS analysis method according to PCS and EV characteristics from classic method for traffic assignment model, in which we analyzed waiting time at PCSs by queueing theory and calculated feasible routes for EVs recharging half-way by screening from deep-first-search results. Finally, we demonstrated an application of the proposed model and method in QoS optimization. We carried out experiments on popular Sioux Falls network with randomly-added PCSs. Results indicate that proportion of traditionally neglected waiting time at PCSs may reach 22% in EVs' total travel time if fast charging piles serve 2 vehicles/h, and the gap between our method and conventional method in average waiting time and spatial throughput distribution is growing as EV amount increases. These results support our method's advantages in simulating EVs' charging choice trade-off between farther routes and popular PCSs as well as higher performance in UPCN QoS optimization.

System occupancy in a multiclass batch-service queueing system with limited variable service capacity

In multi-class telecommunications or manufacturing systems, customers belonging to the same class can often be processed together. This results in a service capacity that depends on the classes of the customers in the queue. In this paper, we analyse a discrete-time batch-service queue with two customer classes. The single batch server can group all same-class customers at the head of the queue up to a constant class-dependent maximum service capacity. We focus on the analysis of the system occupancy at service initiation opportunities, and also compute both a light- and heavy traffic approximation in order to reduce the numerical complexity introduced by the maximum service capacities. Additionally, we propose a method for interpolating between these approximations in order to study the behaviour in the intermediate region. We also deduce the system occupancy and its approximations at random slot boundaries. In the numerical experiments, we examine the conditions under which these proposed approximations are accurate.