Cost-efficient Services Research Articles

Study of factors affecting customer patronage: engagement model of health insurance

PurposeImproving health outcomes requires a robust health-care service model that delivers cost-efficient services and increase customer patronage. The purpose of this study is to examine how service quality and convenience influence perceived value, satisfaction and customer patronage of health insurance policyholders. Based on contemporary research, this study further investigates the moderating role of trust, inertia, insurer type and word-of-mouth (WOM) on relationship between satisfaction and customer patronage.Design/methodology/approachThis study conceptualized the dimensions of SERVQUAL and SERVCON as drivers of perceived value leading to satisfaction and finally customer patronage in presence of four moderators. To test the hypotheses, data from 500 consumers who had a running health insurance policy was collected and analyzed using partial least square path modeling.FindingsThe results of this study showed service quality and convenience dimensions significantly affected perceived value. Perceived value strongly influenced satisfaction and customer patronage intentions. Satisfaction had a significant positive effect on patronage. WOM and trust moderated the satisfaction–patronage relationship for recommendation intention but not repurchase intention. The moderators had an indirect bearing on customer patronage.Social implicationsSuch an engagement ecosystem can be considered to be a revolution, as it will change the way businesses are conducted and how stakeholders interact with one another.Originality/valueThis study adapts and integrates the SERVQUAL and SERVCON models to health insurance domain. Second, this study conceptualizes a modified view of post-benefit convenience relevant for health insurance as policy renewal intention rather than returns/exchanges. This addresses a gap in the SERVCON scale's applicability to insurance services. This study also makes a novel attempt of examining implication of WOM and trust in health insurance domain.

ICT, Manufacturing and Industrial Automation of Biological Processes

The presence of wireless and mobile technologies in developing countries, the availability of low-cost, miniaturized wireless sensors, as well as the cost-efficient services provided by new hardware infrastructures have enabled new healthcare services, or new levels of quality and cost-efficiency in established ones. One crucial aspect of Healthcare 4.0 is the concept of P4 in Medicine: predictive, preventive, personalized and participatory. This approach, based on a comprehensive understanding of each patient biology, opposite to clustering patients into treatment groups, is being applied to reduce global health budgets by minimizing unnecessary use of drugs and procedures. These innovations come from the broad set of ICTs, and among them, the pillars of HC4.0 for their importance: IoT, Cloud Computing, and Big Data. Health 4.0 is used to improve the efficiency of physicians by enabling them to optimize resources and data availability. The future of health management will become timesaving and personalized as new technologies will empower individuals to conduct their health monitoring by using cyber–physical systems. Thanks to IoT, Cloud and Fog, as well as Big Data, researchers are allowed to design novel solutions, which are able to efficiently and effectively renew consolidated healthcare practices.

Performance analysis and cost investigations for state-dependent single unreliable server finite queue under F-policy using GA and QNM

State-dependent queues are essential as they accurately capture the dynamic behavior of systems, enabling more realistic modeling and analysis of various real-world scenarios. The present investigation deals with a state-dependent single unreliable server finite queueing model under admission control F-policy. The admission control F-policy is a valuable tool to control the congestion problem of customers where the queue forms. In the proposed model, the server is prone to breakdown and can fail at any time. There is a two-phase repair facility available to address server failures. Chapman–Kolmogorov (C–K) steady-state equations based on the birth–death process are formed and solved using the matrix analytic method to formulate a mathematical model. By setting suitable values for state-dependent parameters, some particular models, such as the machine repair problem (MRP) and queueing model with customers’ balking and feedback, can also be deduced. Furthermore, various queueing indices are established to predict the validity of state-dependent queues. A total cost function for the system is constructed to enhance customer service while minimizing costs. This cost function is then optimized using a genetic algorithm (GA) and quasi-Newton method (QNM). The cost optimization in the state-dependent queueing model enables businesses to optimize resource allocation and achieve cost-efficient service delivery, leading to improved profitability and customer satisfaction.

Measuring and managing service productivity: a meta-analysis

Despite service productivity’s scholarly prominence and practical relevance, past research in marketing has primarily adopted isolated perspectives from which disjointed empirical findings reign supreme. As the acquisition of knowledge about service productivity accelerates, the collective evidence becomes more interdisciplinary but also more fragmented. This study uses a meta-analysis to integrate the substantial empirical record on service productivity. We formulate hypotheses on the moderators of service productivity-determinant relationships and meta-analyze 77 articles, relying on 81 independent samples with a cumulative sample size of 30,238 participants to test our predictions. Our meta-analysis provides empirical evidence that service quality and internal efficiency must be considered jointly, not in isolation, to maximize profitability. Thus, relying on one aspect in isolation is less appropriate for measurement purposes and might not lead to positive outcomes. This important finding should concern service scholars and managers because falling profit margins require service firms to move beyond the traditional manufacturing productivity that separates service quality from internal efficiency and consider service productivity as a profitability concept. In sum, our findings provide a viable model to explain the main service productivity determinants and moderating variables, offering valuable insights for practitioners that aim to deliver cost-efficient service quality and promising future research directions.

An Overview: Quality of Life of Diabetes Mellitus Type 2 Patients who Participate in The Prolanis Program in Sukoharjo Regency

Prolanis is a health service system and a proactive approach that is implemented in an integrated manner implemented by the Social Health Facility Implementing Agency (BPJS Kesehatan). The program focuses on BPJS Health participants who suffer from chronic diseases including type 2 diabetes mellitus. Participants who take part in Prolanis are expected to achieve optimal quality of life with cost effective and efficient health services. This study aims to describe the quality of life of people with type 2 diabetes mellitus who joined the prolanis program in Sukoharjo Regency. This research is a quantitative study using descriptive methods and survey approaches. The measuring instrument used was the DQOL questionnaire sheet. The sampling technique used in this study was convenience sampling with 95 samples. The results show that as many as 82 people (86.3%) sufferers of type 2 DM who participated in the prolanis program in Sukoharjo regency have a good quality of life with the characteristics of most women, around 61-70 years old, graduated high school, housewife, and suffer from type 2 DM for less than 5 years.

Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows

The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an ɛ-constraint technique. A case study of the situation of Wuhan, China, during the initial COVID-19 outbreak is employed to examine the performance and applicability of the proposed model. Our numerical tests indicate that the B&P algorithm outperforms CPLEX in the computational times by more than 83% in small-sized problem instances and reduces the gaps by at least 70% in large-scale ones. Through a comparison with the current and deterministic systems, our proposed stochastic system can timely adjust itself to fulfill nearly four times the demand of other systems in an extreme pandemic scenario, while maintaining a cost-efficient operation with no outbreak.

The False Economy of Seeking to Eliminate Delayed Transfers of Care: Some Lessons from Queueing Theory.

It is a stated ambition of many healthcare systems to eliminate delayed transfers of care (DTOCs) between acute and step-down community services. This study aims to demonstrate how, counter to intuition, pursual of such a policy is likely to be uneconomical, as it would require large amounts of community capacity to accommodate even the rarest of demand peaks, leaving much capacity unused for much of the time. Some standard results from queueing theory-a mathematical discipline for considering the dynamics of queues and queueing systems-are used to provide a model of patient flow from the acute to community setting. While queueing models have a track record of application in healthcare, they have not before been used to address this question. Results show that 'eliminating' DTOCs is a false economy: the additional community costs required are greater than the possible acute cost saving. While a substantial proportion of DTOCs can be attributed to inefficient use of resources, the remainder can be considered economically essential to ensuring cost-efficient service operation. For England's National Health Service (NHS), our modelling estimates annual cost savings of £117m if DTOCs are reduced to the 12% of current levels that can be regarded as economically essential. This study discourages the use of 'zero DTOC' targets and instead supports an assessment based on the specific characteristics of the healthcare system considered.

How Do Mobile Wallets Improve Sustainability in Payment Services? A Comprehensive Literature Review

Easy access to the Internet, smartphones, and mobile-based banking change customer shopping intentions. As a crucial component of financial technology (Fintech), mobile wallets enable customers to shop via smartphones. Mobile wallets present a cashless transactional method, cost-efficient services, and traceable options that improve sustainability in payment services. Over the last decade, mobile wallet services have evolved and attracted considerable attention from customers and companies. Due to the need for a comprehensive mobile wallet literature survey, this article aims at filling this research gap by covering articles published between 2012 and 2022 over the Scopus, Web of Science, and Science Direct databases. A clear filtering policy was conducted to observe the related article topics. Thus, 128 articles that met the inclusion and exclusion criteria were analyzed. Moreover, the articles were initially classified into three main groups, which was performed via scanning and categorizing all studies in the last ten years from different databases. In addition, the literature was systematically reviewed, providing a better understanding of mobile wallets and contributing to the literature by researching how this service can be improved for payment services with a focus on sustainability. The conducted literature review revealed that mobile wallets could be promoted in terms of environmental traceability, customer lifetime value, and security.

Dynamic Joint Functional Split and Resource Allocation Optimization in Elastic Optical Fronthaul

Dynamic reconfigurability in optical and mobile networks can facilitate heterogeneous service provisioning while utilizing minimal resources. This allows cost-efficient service delivery resulting in high revenues for network operators. Deployment of elastic mobile and optical networks is a key driver for enabling reconfigurability in modern networks. Elastic optical networks can be exploited as the fronthaul portion of new generation of mobile networks. Such elastic optical fronthaul networks facilitate joint reconfiguration of flexible radio and optical elements and provide considerable performance improvements. In this paper, we focus on the joint dynamic selection of functional splits and configuration of optical transponders and illustrate that designing a converged network with optical and radio elements improves network power efficiency. A time-averaged stochastic optimization problem is formulated and its solution is derived using a modified version of the Lyapunov drift technique. Simulation results demonstrate that the proposed scheme can reduce the average power consumption by up to 70% compared to a cloud radio access network with a traditional optical fronthaul. Further, the results show that the modified Lyapunov technique can afford stringent fronthaul delays below 250 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> . We also discuss how future technology upgrades such as increasing the number of radio antenna ports and decreasing the granularity of fiber spectrum grid may influence the results.

Reinforcement learning-based cost-efficient service function chaining with CoMP zero-forcing beamforming in edge networks

As two promising paradigms in emerging 6G wireless systems, service function chaining (SFC) and mobile edge computing (MEC) have attracted insensitive attentions from both industry and academia, and would bring more close-proximity services to 6G users with communication, computing and caching (3C) resources, yet also faced with challenges arising in time-varying channel conditions and resource dynamics. In this work, boosted by recent advents in artificial intelligence and reinforcement learning, we investigate the on-line SFC deployment in the edge of 6G wireless systems via the actor–critic learning framework. First, one long-run cost-efficient SFC deployment problem is investigated, and the coordinated multiple points (CoMP)-based zero-forcing beamforming is utilized to cancel the interference across SFCs. Then, by exploiting the Markov decision processes (MDP) property of long-run SFC deployment, one natural gradient-based actor–critic framework is proposed to characterize edge network dynamics, and meanwhile facilitates the training of neural networks to the global optimum. Next, to lower the size of action space, we follow the principle that a subproblem is embedded into each state–action pair’s critic to solve the reward function, and then utilize both the ℓp(0<p<1) norm-based successive convex approximation (SCA) and proximal center-based dual decomposition to approach the global optimum and accelerate the convergence. Finally, numerical results are used to validate proposed actor–critic approach, showing that the communication resource management deserves special attentions in the SFC deployment in the edge of 6G wireless systems.

The changing concept of radiographer's role in UAE: An analysis of radiologists' opinions and acceptance

IntroductionThe role of radiographers in the United Arab Emirates (UAE) is currently focused on image acquisition. However, many advances have been made in different countries in recent years whereby radiographers who receive appropriate education and training, can provide image interpretation/reports. When implemented, this role development has enabled a more cost effective and efficient service delivery whilst relieving the burden off radiologists, allowing them to concentrate on more complex imaging examinations. This role development is commonplace in many countries but not in the UAE. AimThis study aims to investigate the radiologists' opinions, perceptions, and willingness to accept the advanced practice role of reporting radiographers in the UAE and determine their level of support for implementing these roles. MethodsData was collected utilizing a mixed-methods study design that included a survey and focus group discussions (FGD). Study participants included radiologists who currently work in UAE public and private health organizations. The survey link was emailed directly to the radiologists, together with a covering letter and participants' information sheet outlining the study's aim. Participants indicated on the survey if they wanted to participate in FGD. Two online FGD were conducted using Zoom software (Zoom Video Communications Inc., San Jose, California, United States) and aimed to explore possible reasons for participant's opinions. Ethical approval was obtained from the Ministry of Health, and all methods were performed as per study protocol. ResultsA total of 69 radiologists participated in the survey, 48 males and 21 females aged between 41 and 60 years and with between 11 and 16 years of experience. Most participants (n = 54, 78.2%) believe that radiographers should only perform advanced tasks in image interpretation after obtaining adequate training and under the supervision of a radiologist. According to 55% of radiologists, the development of the radiographer role could draw more UAE nationals to the field. Six participants were recruited to FGD and declared mixed opinions that emphasized the need to improve the radiographers knowledge and experience to enable role development. ConclusionRadiologists' worries about radiographer engagement in image interpretation may be alleviated if they participate in education and training for new responsibilities. In addition, this could boost the confidence of radiologists and improve trust in radiographer competency and training. Implications for practiceGuidelines and work standards must be developed jointly by radiologists and radiographers to ensure the governance and acceptability of new radiographer reporting roles. Some radiologists perceive that radiographer reporting is possible in UAE when radiographers are trained to set guidelines and with supervision from radiologists. Change is taking place, and many radiologists express optimism for the future, though the rate of change will be determined by a willingness to change attitudes and perceptions.

A Review on Cloud Computing Technology, Cloud Deployment and Service Delivery Models

Abstract: With the advent of Cloud Computing technology, organizations are rapidly shifting their IT systems from traditional client/server to cloud computing model where everything is offered as a service whether it’s a software, hardware or storage capacity. Cloud computing uses the concept of pay as per use basis where users do not need to pay for infrastructure, installation and its maintenance. Anyone can access the desired service from the cloud anytime, anywhere in the world on demand basis. The utilization of this innovative technology makes collaboration easier among companies or organizations and has the potential to create financial and operational benefits. This study helps organizations and individuals understand how cloud computing can provide them with reliable, personalized and cost-efficient services in a wide variety of applications. Keywords: Cloud Computing; Public Cloud; Private Cloud; Hy

Value-Based Payments: Intellectual and Developmental Disabilities Quality Indicators Associated With Billing Expenditures.

Although managed care is expanding into the intellectual and developmental disabilities (IDD) service system, there is little agreement about measurable and meaningful outcomes for people with IDD, including for use in value-based payments (VBP). In this study, we examined potential VBP metrics for people with IDD-relationships between quality and costs. We analyzed Basic Assurances data and long-term services and supports billing data from 68 human service organizations that supported 6,608 people with IDD. Our final hierarchical regression model predicted 66.40% of the variance of annual long-term services and supports (LTSS) billing per person. Our findings suggest quality assurance indicators can account for a significant portion of cost variance-quality metrics represent a potential for cost savings and efficient service delivery.

\u2018What matters to you?\u2019 Normative integration of an intervention to promote participation of older patients with multi-morbidity \u2013 a qualitative case study

BackgroundInterventions in which individual older patients with multi-morbidity participate in formulating goals for their own care are being implemented in several countries. Successful service delivery requires normative integration by which values and goals for the intervention are shared between actors at macro-, meso- and micro-levels of health services. However, health services are influenced by multiple and different institutional logics, which are belief systems guiding actors’ cognitions and practices. This paper examines how distinct institutional logics materialize in justifications for patient participation within an intervention for patients with multi-morbidity, focusing on how variations in the institutional logics that prevail at different levels of health services affect vertical normative integration.MethodsThis qualitative case study of normative integration spans three levels of Norwegian health services. The macro-level includes a white paper and a guideline which initiated the intervention. The meso-level includes strategy plans and intervention tools developed locally in four municipalities. Finally, the micro-level includes four focus group discussions among 24 health professionals and direct observations of ten care-planning meetings between health professionals and patients. The content analysis draws on seven institutional logics: professional, market, family, community, religious, state and corporate.ResultsThe particular institutional logics that justified patient participation varied between healthcare levels. Within the macro-level documents, seven logics justified patients’ freedom of choice and individualization of service delivery. At meso-level, the operationalization of the intervention into tools for clinical practice was dominated by a state logic valuing equal services for all patients and a medical professional logic in which patient participation meant deciding how to maintain patients’ physical abilities. At micro-level, these two logics were mixed with a corporate logic prioritizing cost-efficient service delivery.ConclusionNormative integration is challenging to achieve. The number of institutional logics in play was reduced downwards through the three levels, and the goals behind the intervention shifted from individualization to standardization. The study broadens our understanding of the dynamic between institutional logics and of how multiple sets of norms co-exist and guide action. Knowledge of mechanisms by which normative justifications are put into practice is important to achieve normative integration of patient participation interventions.

How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword decoding error probability. In this paper, we propose to orchestrate network resources for a slicing system to guarantee more reliable bursty URLLC transmission. We re-cut physical resource blocks and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword decoding error probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem, which is challenging to be mitigated due to requirements of future channel information and of tackling a two timescale issue. To address the challenges, we develop a resource optimization algorithm based on a sample average approximate technique and a distributed optimization method with provable performance guarantees.

Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things.

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.

Education for Children With ASD in South Korea: A Case Study.

In South Korea, there is currently a massive gap between the demand and the supply of quality applied behavior analysis (ABA) services for children with autism spectrum disorder (ASD) and their families. However, the literature on the implementation and effectiveness of ABA intervention mainly comes from Western countries, and the voices of Asian countries are scarcely heard. The present article reports data collected from the KAVBA Center in Seoul, South Korea, as a direct replication of the CABAS educational model. Eleven 3- to 4-year-old children with ASD were the participants in the study and attended the center for 1 year. Our pre- and postintervention data show that the CABAS model provided an effective and cost-efficient service for children with ASD in South Korea.

A Cost Efficient Service Broker Policy for Data Center Allocation in IaaS Cloud Model

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

A STUDY ON SMART CITY

This concept is being used all over the world with different meanings. A smart city is a city that is well planned, and it provides the cost efficient services, environmental efficiency, and technological sound services for the welfare of the citizens. Smart solutions can be helpful in controlling the ever increasing population in the cities smart city is an emerging concept.

Performance Model for Video Service in 5G Networks

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.