Service Resources Research Articles

The linkage misalignment of productive services and firms' domestic value-added ratio——Evidence from Chinese micro-firm data

Promoting the deep integration of advanced manufacturing and modern service industries is essential for enhancing the competitiveness of manufacturing enterprises, cultivating a modern industrial system, and achieving high-quality, sustainable development; however, the low allocation efficiency of productive service resources has led to an extremely limited, long-term process in enhancing China's manufacturing industries' competitiveness. Based on scientific quantification of productive service linkage, this study uses micro level data from China to empirically test its impact and mechanism on the domestic value-added ratio of enterprises' exports. The results reveal a relatively high degree of mismatch of productive service input in China, which primarily reduces enterprises' domestic value-added ratio through import induced and markup reducing effects. The conclusions remain highly consistent across benchmark regression and further robustness tests, including instrumental variable estimation and a quasi-natural experiment. Further analysis reveals that enterprises in regions with a higher degree of marketization, firms facing tighter financing constraints, those with smaller gaps from the technology frontier, and enterprises in industries with comparative advantages are more easily influenced by productive services linkage misalignment. Finally, this study proposes targeted policy recommendations based on the research findings.

University teaching skills in ICT and disability. The case of the Autonomous Community of Madrid

AbstractTeachers must be prepared to serve all learners, regardless of their abilities or needs in this information and communication society. This implies developing the necessary competences to provide inclusive education to all students. The purpose of this study was to investigate the opinion of university teachers in the Autonomous Community of Madrid on their training in the use of technologies to support students with disabilities. To carry out this research, a descriptive approach combining both quantitative and qualitative methods was used. The final sample consisted of 106 university teachers (information collected through questionnaires) and 8 professionals from ICT Production Resource Centres and Services for People with Disabilities in the respective universities (information collected through interviews). Two data collection instruments were used: an ad-hoc questionnaire as a data collection instrument, and a semi-structured interview. The results of the study showed that university teachers have a low level of competence in the use of information and communication technologies (ICT) to support students with disabilities. The lack of teacher training negatively affects the inclusion process in university education institutions. Higher education institutions need to promote the training of teachers in the use of technologies to cater for diversity.

Impact of specialist ataxia centres on health service resource utilisation and costs across Europe: cross-sectional survey

BackgroundLittle is known about the costs of treating ataxia and whether treatment at a specialist ataxia centre affects the cost of care. The aim of this study was to investigate whether patients who attended specialist ataxia centres in three European countries reported differences in their health care use and costs compared with patients who did not attend a specialist ataxia centre. We compared mean resource use and health service costs per patient affected by ataxia in the United Kingdom, Italy and Germany over a 12-month period. Data were obtained from a survey distributed to people with ataxia in the three countries. We compared mean resource use for each contact type and costs, stratifying patients by whether they were currently attending a specialist ataxia centre or had never attended one.ResultsResponses were received from 181 patients from the United Kingdom, 96 from Italy and 43 from Germany. Differences in the numbers of contacts for most types of health service use between the specialist ataxia centre and non-specialist ataxia centre groups were non-significant. In the United Kingdom the mean total cost per patient was €2209 for non-specialist ataxia centre patients and €1813 for specialist ataxia centre patients (P = 0.59). In Italy these figures were €2126 and €1971, respectively (P = 0.84). In Germany they were €2431 and €4087, respectively (P = 0.19). Inpatient stays made the largest contribution to total costs.ConclusionsWithin each country, resource use and costs were broadly similar for specialist ataxia centre and non-specialist ataxia centre groups. There were differences between countries in terms of health care contacts and costs.

Multi-Agent Deep Reinforcement Learning for Cooperative Computing Offloading and Route Optimization in Multi Cloud-Edge Networks

Edge computing is a new paradigm to provide computing capability at the edge servers close to end devices. A significant research challenge in edge computing is finding efficient task offloading to edge and cloud servers considering various task characteristics and limited network and server resources. Several reinforcement learning (RL)-based task-offloading methods have been developed, because RL can immediately output efficient offloading by pre-learning. However, these methods do not take into account clouds or focus only on a single cloud. They also do not take into account the bandwidth and topology of the backbone network. Such shortcomings strongly limit the range of applicable networks and degrade task-offloading performance. Therefore, we formulate a task-offloading problem for multi-cloud and multi-edge networks considering network topology and bandwidth constraints. We also propose a task-offloading method that is based on cooperative multi-agent deep RL (Coop-MADRL). This method introduces a cooperative multi-agent technique through centralized training and decentralized execution, improving task-offloading efficiency. Simulations revealed that the proposed method can minimize network utilization and task latency while minimizing constraint violations in less than one millisecond in various network topologies. It also shows that cooperative learning improves the efficiency of task offloading. We demonstrated that the proposed method has generalization performance for various task types by pre-training with many resource-consuming tasks.

Multicast Service Function Chain Orchestration in SDN/NFV-Enabled Networks: Embedding, Readjustment, and Expanding

Multicast is an effective transmission mode to support ever-growing multimedia applications. The introduction of software defined networking (SDN) and network function virtualization (NFV) makes the multicast service operation more flexible and efficient. Nevertheless, one main challenge of SDN/NFV-enabled multicast is optimally orchestrating the service function chain (SFC) to match service and network resources. Compared with unicast, multicast SFC orchestration (MSO) is more challenging due to the features of multicast service like multicast routing and user fluidity (i.e., frequent user arrival and departure). There are still some gaps in the joint optimization of MSO and multicast routing, and very little attention is paid to user fluidity. In this paper, we study the MSO in SDN/NFV-enabled networks encompassing multicast SFC embedding (MSE), multicast SFC readjustment (MSR), and multicast SFC expanding (MSEP), three types of MSO. For each kind of MSO, we simultaneously consider several key optimization factors when jointly optimizing MSO and multicast routing. Besides, aside from MSE, we investigate two new types of MSO: MSR and MSEP, for efficient orchestration under the fluidity of users. Specifically, we define and formulate MSE, MSR, and MSEP problems and develop three novel algorithms to respectively solve them. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Toward Intelligent Cross-Domain Resource Coordinate Scheduling for Satellite Networks

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

Federated Deep Reinforcement Learning-Based Intelligent Dynamic Services in UAV-Assisted MEC

Unmanned Aerial Vehicles (UAVs)-assisted Multi-access Edge Computing (MEC) has emerged as a promising solution in B5G/6G networks. The high flexibility and seamless connectivity of UAVs make them well-suited for providing enhanced communications coverage and efficient computing support. Particularly in situations where ground facilities may be compromised or communication is unreliable. In this paper, we study joint dynamic service switching and resource allocation for multiple UAVs in MEC network. We consider the heterogeneity of tasks and UAVs and model the dynamic service process of UAVs as a sequential decision problem based on the Markovian decision process. To enable dynamic and intelligent UAV service, we first propose a centralized dynamic service algorithm DDBC based on deep reinforcement learning. However, given the training difficulties of the centralized algorithm, we propose a more promising distributed learning algorithm FLBF, which combines federated learning. We conduct extensive simulations to evaluate the effectiveness and advantages of the proposed algorithms. Our results show that both DDBC and FLBF can improve the model convergence speed by 50%, and reduce the system cost by 12.30% to 35.72% compared to comparative algorithms. Furthermore, simulations indicate that FLBF is well-suited for training models in UAV-assisted MEC networks.

Insights of 6G and artificial intelligence-based internet-of vehicle towards communication

<p>The significance of internet-of-vehicle (IoV) is spontaneously increasing with exponentially rising demands towards transportation system and road safety. At present, there are various number of scientific approaches which is meant for leveraging the communication performance in IoV, but yet the problem still exists over multiple attributes e.g. resource management, privacy, security, and service offloading. Such problems are anticipated to be solved by 6G services that offers better communication capabilities compared to its prior version of 5G. At the same time, the quality of communication system can be enhanced by artificial intelligence (AI), which is capable of solving complex real-world problems. Therefore, this manuscript offers an insight towards strength and weakness of existing 6G based study model as well as AI-based solution in order to contribute towards highlighting an essential research gap that could directly offer better insight towards future planning towards improving communication in IoV.</p>

Dynamic Joint VNF Forwarding Graph Composition and Embedding: A Deep Reinforcement Learning Framework

Network Function Virtualization (NFV) is a network service deployment technology that reduces capital and operational costs while yielding flexibility and scalability for service operators. As such, an ordered chain of Virtual Network Functions (VNFs), known as a VNF Forwarding Graph (VNF-FG), should be composed and embedded into the underlying substrate network. In the literature, the composition and embedding stages of VNF-FGs are usually targeted separately, which may result in undesired solutions. In this paper, we propose our joint VNF-FG composition and embedding solution, which considers the variations of service demands while also accounting for dynamic network conditions. Specifically, our proposed solution relies on deep reinforcement learning empowered by two components for estimating dynamic parameters: network resource utilization and service demand analyzers. Moreover, to efficiently explore the problem’s large discrete action space, we utilize a specialized branching Q-network and enhance it with an action filtering mechanism. We evaluated our proposed method against joint and disjoint composition and embedding heuristics as well as versus other deep learning-based methods. Our results show that the proposed method can achieve up to a 95% improvement of embedding cost compared to our benchmarks.

Development and description of a theory-driven, evidence-based, complex intervention to improve adherence to treatment for tuberculosis in the UK: the IMPACT study

ABSTRACT Background Tuberculosis (TB) has a significant treatment burden for patients, requiring at least six months of anti-TB treatment (ATT) with multiple medicines. Ensuring good adherence to ATT is central to global TB strategies, including those in high-income, low-TB incidence (HILI) settings. For adherence interventions to be successful and deliverable, they need to address the personal and environmental factors influencing patient and provider behaviour. Purpose This paper describes the application of theory and research evidence to inform the design process of the IMPACT manualised intervention to support ATT adherence for adults with TB disease in the United Kingdom (UK). It also provides a full description of the resulting intervention. Methods We synthesised findings from our formative research (qualitative and quantitative scoping reviews and patient and carer interviews) and supplemented these with clinic observations, a literature review, and healthcare provider interviews. Findings were mapped to the guiding theoretical framework (Perceptions and Practicalities Approach) which was operationalised to design the intervention components and content. An Intervention Development Group (IDG) of relevant stakeholders were consulted to adapt the intervention to local clinical settings. Results The pragmatic, deliverable components and content for the IMPACT intervention included: (1) an enhanced, structured, risk assessment to systematically identify risk factors for non-adherence plus locally-adapted guidance to mitigate these; and (2) patient educational materials (an animated video and interactive patient booklet) about TB and its treatment, to communicate the need for treatment and address common concerns. Conclusions Using a theory– and evidence– based approach incorporating stakeholder input, we have developed a multi-component, pragmatic, manualised intervention, which addresses patients’ personal barriers to adherence within local service resources to improve adherence to ATT within UK TB services.

Association of Geographic Distance and Hospital Characteristics With Use of Interhospital Transfer by Air: A Multicenter Retrospective Study

ObjectiveInterhospital transfer by air (IHTA) represents the majority of helicopter air ambulance transports in the United States, but the evaluation of what factors are associated with utilization has been limited. We aimed to assess the association of geographic distance and hospital characteristics (including patient volume) with the use of IHTA. MethodsThis was a multicenter, retrospective study of helicopter flight request data from 2018 provided by a convenience sample of 4 critical care transport medicine programs in 3 US census regions. Nonfederal referring hospitals located in the home state of the associated critical care transport medicine program and within 100 miles of the primary receiving facility in the region were included if complete data were available. We fit a Poisson principal component regression model incorporating geographic distance, the number of emergency department visits, the number of hospital discharges, case mix index, the number of intensive care unit beds, and the number of general beds and tested the association of the variables with helicopter emergency medical services utilization. ResultsA total of 106 referring hospitals were analyzed, 21 of which were hospitals identified as having a consistent request pattern. Using the hospitals with a consistent referral pattern, geographic distance had a significant positive association with flight request volume. Other variables, including emergency department visit volume, were not associated. Overall, the included variables offered poor explanatory power for the observed variation between referring facilities in the use of IHTA (r2 = 0.09). Predicted flights based on the principal component regression model for all referring hospitals suggested the majority of referring hospitals used multiple flight programs. ConclusionGeographic distance is associated with the use of IHTA. Unexpectedly, most basic hospital characteristics are not associated with the use of IHTA, and the degree of variation between referring facilities that is explained by patient volume is limited. The evaluation of nonhospital factors, such as the density and availability of critical care or advanced life support ground emergency medical services resources, is needed.

Anti-CGRP mAbs for the Preventive Treatment of Migraine: An Overview Review and a Cost Saving Analysis in the Global Scenario.

Objectives: Migraine is a neurological disease with a high frequency of incidence. The new monoclonal antibodies selective for the calcitonin gene-related peptide and its ligand (anti-CGRP mAbs) have been marketed both in the USA and EU based on the positive efficacy results in the prevention of migraine. This search has been carried out with the aim of collecting real-world evidence on the effectiveness of anti-CGRP mAbs, performing a cost-savings analysis, and comparing performances among anti-CGRP mAbs medicines marketed in the American and European market. Methods: The literature review has been performed in PubMed database on 31 December 2022; the cost of the unitary dose of anti-CGRP mAbs has been extracted consulting an American national database. Results: The results confirm efficacy and good tolerability of anti-CGRP mAbs, determining a difference in the purchase price. In fact, all extracted studies showed a protective risk factor exposure in monthly migraine days reduction for all the anti-CGRP mAbs, whereas the cost analysis showed that using eptinezumab, in a quarter there is a cost saving of at least $425 per patient, compared with the other anti-CGRP mAbs. Conclusions: With equal efficacy and equal safety, anti-CGRP mAbs should be prescribed also regard to the cost established at the negotiation, making sure to guarantee the best treatment to the patients, but at the same time impacting as little as possible to the healthcare services resources.

The interplay of product modularity, service types, and servitization depth on firm performance: a moderated mediation model

The servitization literature has explored the role that product modularity plays in supporting service design and delivery. Importantly, product modularity has the potential to aid manufacturers in providing customised solutions on a larger scale, thereby strengthening firm performance. However, despite the prospective benefits of product modularity, manufacturers also need considerable servitization depth, which comprises service orientation, resources, and delivery systems, to provide services in a cost-effective manner. Taking this into account, the study both theoretically articulates and empirically tests relationships among product modularity, servitization depth, service types, and firm performance, employing a moderated mediation model. Using survey data collected from 204 manufacturers in the UK and German, the findings indicate that product modularity exerts a positive influence on firm performance, with servitization depth acting as a mediating factor. The mediation effect of servitization depth on the correlation between product modularity and firm performance was found to fluctuate based on the service types offered by the manufacturer. This study adds to the existing literature on servitization and the role of product modularity and servitization depth in achieving superior firm performance.

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

IntroductionForest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA.MethodsWe used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period.ResultsWe found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface.DiscussionOur results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes.

Pathodynamics.

Osteoporosis and osteoporosis-related fractures in the aging population are becoming a health care problem and a burden on health service resources available. Osteoporosis is a systemic skeletal disorder that results from an imbalance in bone remodeling, leading to a reduction in bone strength with microarchitectural disruption and skeletal fragility, increasing fracture susceptibility. Osteoporosis is considered a well-known metabolic bone disorder. Although its prevalence is more commonly seen in women than men, it is eventually seen in both genders. In the elderly population, there is an increase in disability and mortality due to osteoporotic fractures.

Sero-prevalence of contagious bovine pleuropneumonia in dryland of Borana, southern Oromia, Ethiopia

Ethiopia is one of the largest African countries where livestock farming represent a relevant resource for the economy and the livelihood of the population. Contagious bovine pleuropneumonia (CBPP) is among the transboundaries animal disease that is hindering cattle farming in Ethiopia. Due to the limited resources of veterinary services, disease control and surveillance is discontinuous and occasional field investigations of target areas contribute to depict disease spreading in the country. The study was conducted to determine the seroprevalence, at herd and animal level, and identify the risk factors involved in CBPP diffusion and persistence in the Borana pastoral zone. A total of 498 serum samples were collected from 120 cattle herds and tested using competitive Enzyme-Linked Immunosorbent Assay (c-ELISA). Of 120 herds sampled, 37 (30.83%; (95% CI = 22.73–39.91%) were tested positive to CBPP antibody. Out of 498 sera samples tested 46 (9.24%; 95% CI = 6.84–12.13%) were positive. The highest prevalence was observed in Teltele (12/95; 12.90%; 95% CI = 6.7–21%) followed by Yabello (12/104; 11.54%; 95% CI = 6.1–19.3%) and Arero (10/91; 10.99%; 95% CI = 95% CI = 5.4–19.3%), whereas the lowest prevalence was observed in Gomole (5/101; 6.42%; 95% CI = 1.6–11.2%) and Dubluk (7/109; 4.95%; 95% CI = 2.6–12.8%) districts and statistically not significant (p > 0.05). Results of multivariate logistic regression analysis revealed that, age, herd movement and herd size of the animals had statistically significant effect on sero-positivity to CBPP (p < 0.05). Sex, season and body condition were not significantly (p > 0.05) associated with the occurrence of CBPP. The study confirms that CBPP is persistent in the territory and remain as a major problem that affects health and productivity of cattle. Therefore, awareness creation to the pastoralists in the study area about the effect of CBPP and designing appropriate control methods has a paramount importance to improve the health and productivity of cattle production in the area.

Research on design methodology for railway freight service combination plans to meet diverse demands

PurposeFacing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.Design/methodology/approachBased on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.FindingsUsing the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.Originality/valueWith the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices

Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the scenario where public and non-public network slices coexist. There are two kinds of resources to be allocated: one is the resource blocks (RBs) allocated to the users in the radio access network, and the other is the server resources in the core network. We first formulate the above resource allocation problem as a nonlinear integer programming problem by maximizing the operator profit as the objective function. Then, a combination of deep reinforcement learning (DRL) and machine learning (ML) algorithms are used to solve this problem. DRL, more specifically, independent proximal policy optimization (IPPO), is employed to provide the RB allocation scheme that makes the objective function as large as possible. ML, more specifically, random forest (RF), assists DRL agents in receiving fast reward feedback by determining whether the allocation scheme is feasible. The simulation results show that the IPPO-RF algorithm has good performance, i.e., not only are all the constraints satisfied, but the requirements of the non-public network slices are ensured.

Unveiling consumers’ nonlinear evaluation of service performances in online food delivery: a quantitative Kano analysis

PurposeThe study proposes an evaluation model that allows quantitative characterization of the effects of service quality on consumer’s satisfaction for online food delivery (OFD) services in a nonlinear manner. As such, the authors endeavor to bridge the research-to-practice gaps whereby the effect magnitudes and nonlinear patterns of service quality have been overlooked in the current literature.Design/methodology/approach The quantitative Kano method is adopted. A Kano questionnaire was first developed by synthesizing and operationalizing existing evidence on OFD service qualities. The questionnaire solicited consumers’ evaluations of 21 OFD service attributes, and it was distributed to an online panel in Singapore. With 580 valid responses, the functions that quantitatively depict effects of each attribute on consumer’s satisfaction were subsequently derived.Findings The results reveal that among Singaporean consumers, food quality, reliability of delivery, responsiveness of customer support, ease-of-use of digital interfaces and promotions are pivotal attributes contributing to above-average satisfaction improvement across all performance levels. Meanwhile, delivery riders’ attitudes and real-time tracking functions emerge as substantial contributors to satisfaction at high-performance levels.Practical implications The findings provide crucial insights for OFD practitioners in Singapore in resource prioritization and service optimization. This study demonstrated the importance of streamlining customer support services and focusing on the utilitarian aspects of OFD services. Moreover, these results can be employed in advanced service improvement procedures, providing a roadmap for future OFD service enhancements.Originality/value This study pioneers the development of a quantitative quality evaluation model in the OFD context. With the established quantitative Kano model, the study addresses the omission of effect magnitudes and nonlinear patterns of service quality. It highlights the transition from a binary “does it affect satisfaction” to a more nuanced “how much does it affect satisfaction” approach, offering a robust understanding of consumer’s satisfaction dynamics.

Literatur Review : Implementasi Manajemen Mutu Dan Kualitas Layanan Di Unit Laboratorium

This comprehensive review synthesizes findings from various journals on the management of quality and service in laboratory units within the healthcare context. The synthesis reveals the importance of service quality and its impact on patient satisfaction and service effectiveness. Key findings include the positive influence of service quality dimensions such as reliability, physical evidence, and friendliness on patient satisfaction. Additionally, the interconnection between service quality and adherence to health standards and regulations is emphasized, underlining the significance of compliance for service improvement. External factors, such as government regulations and health policies, are identified as potential influencers of service quality, adding complexity to quality management. Practical implications for healthcare policies and practices are highlighted, suggesting improvements in human resource training, service processes, and infrastructure. Recommendations include strengthening human resource training through regular and ongoing programs, optimizing service processes by thorough workflow evaluations, and ensuring strict compliance with health standards. Furthermore, the integration of technological innovations is proposed as a progressive step toward enhancing service efficiency and result accuracy. The implementation of laboratory information systems, modern equipment, and related applications is suggested to advance service quality. The establishment of a regular monitoring and evaluation system, including internal audits, routine performance reviews, and patient feedback collection, is crucial for continuous improvement. Collaboration with external entities, such as health oversight agencies and laboratory professional associations, is advocated for robust guidance and support. Encouraging research and innovation in quality management and laboratory service is recommended to enhance understanding of new technology applications, quality maintenance strategies, and policy impact evaluation. By implementing these suggestions, laboratory units are expected to continually enhance service quality and ensure optimal patient satisfaction.