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Health technologies are advancing rapidly and becoming more expensive, posing a challenge for financing healthcare systems. Health technology assessment (HTA) improves the efficiency of resource allocation by facilitating evidence-informed decisions on the value of health technologies. Our study aims to create a customized HTA roadmap for Oman based on a gap analysis between the current and future status of HTA implementation.

We surveyed participants of an advanced HTA training program to assess the current state of HTA implementation in Oman and explore long-term goals. A list of draft recommendations was developed in areas with room for improvement. The list was then validated for its feasibility in a round table discussion with senior health policy experts to conclude on specific actions for HTA implementation.

Survey results aligned well with expert discussions. The round table discussion concluded with a phasic action plan for HTA implementation. In the short term (1–2 years), efforts will focus on building capacity through training programs. For medium-term actions (3–5 years), plans include expanding the HTA unit and introducing multiple cost-effectiveness thresholds while from 6–10 years, publishing of HTA recommendations, critical appraisal reports, and timelines is recommended.

Although the HTA system in Oman is still in its early stages, strong initiatives are being taken for its advancement. This structured approach ensures a comprehensive integration of HTA into the healthcare system, enhancing decision-making and promoting a sustainable, evidence-based system addressing the population’s needs.

The purpose of this paper is to describe how first-line managers (FLMs) in home care (HC) reason about the opportunities and obstacles to lead the work according to the individual’s needs and goals.

In this participatory appreciative action reflection project, eight managers within one Swedish municipality were interviewed. The data were analysed using a thematic analysis.

The results showed a polarization between two different systems that FLMs struggle to balance when attempting to lead HC that adapts to the needs and goals of individuals. One system was represented by the possibilities of a humane system, with human capital in the form of the individual, older persons and the co-workers in HC. The second system was represented by obstacles in the form of the economic needs of the organization in which the individual receiving HC often felt forgotten. In this system, the organization’s needs and goals governed, with FLMs needing to adapt to the cost-effectiveness principle and keep a balanced budget. The managers had to balance an ethical conflict of values between the human value and needs-solidarity principles, with that of the cost-effectiveness principle.

The FLMs lack the opportunity to lead HC according to the needs and goals of the individuals receiving HC. There is a need for consensus and a value-based leadership model based on ethical principles such as the principles of human value and needs-solidarity to lead the HC according to the individual’s needs and goals.

This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development.

The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact.

The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs.

While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness.

This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

The purpose of this study is to assess a novel method for creating tangible three-dimensional (3D) morphologies (scaled models) of neuronal reconstructions and to evaluate its cost-effectiveness, accessibility and applicability through a classroom survey. The study addresses the challenge of accurately representing intricate and diverse dendritic structures of neurons in scaled models for educational purposes.

The method involves converting neuronal reconstructions from the NeuromorphoVis repository into 3D-printable mold files. An operator prints these molds using a consumer-grade desktop 3D printer with water-soluble polyvinyl alcohol filament. The molds are then filled with casting materials like polyurethane or silicone rubber, before the mold is dissolved. We tested our method on various neuron morphologies, assessing the method’s effectiveness, labor, processing times and costs. Additionally, university biology students compared our 3D-printed neuron models with commercially produced counterparts through a survey, evaluating them based on their direct experience with both models.

An operator can produce a neuron morphology’s initial 3D replica in about an hour of labor, excluding a one- to three-day curing period, while subsequent copies require around 30 min each. Our method provides an affordable approach to crafting tangible 3D neuron representations, presenting a viable alternative to direct 3D printing with varied material options ensuring both flexibility and durability. The created models accurately replicate the fidelity and intricacy of original computer aided design (CAD) files, making them ideal for tactile use in neuroscience education.

The development of data processing and cost-effective casting method for this application is novel. Compared to a previous study, this method leverages lower-cost fused filament fabrication 3D printing to create accurate physical 3D representations of neurons. By using readily available materials and a consumer-grade 3D printer, the research addresses the high cost associated with alternative direct 3D printing techniques to produce such intricate and robust models. Furthermore, the paper demonstrates the practicality of these 3D neuron models for educational purposes, making a valuable contribution to the field of neuroscience education.

Efficiency standards, similar to industrial measures like overall equipment effectiveness (OEE), are being used in healthcare systems more and more. Performance indicator models applied to machines assume a constant completion time. However, for human resources, the completion time of a task may vary depending on the stress experienced. This study seeks to bridge this gap by integrating the human behavior of the physician into the performance evaluation.

The paper presents a new algorithm called PerfoBalance that is intended to distribute waiting-patient values among doctors. By maximizing each physician’s stress zones, this method helps to improve their performance as a whole. A thorough case study with medical professionals is carried out to confirm the effectiveness of the suggested methodology. The PerfoBalance algorithm is used in a variety of contexts to divide waiting-patient values among doctors and optimize stress zones.

Experimental results demonstrate a significant improvement in physician efficiency when implementing the PerfoBalance algorithm. The algorithm strategically selects stress zones that contribute to higher performance rates for physicians by optimizing waiting-patient values.

By addressing the undervaluation of human performance difficulties in current efficiency models used in the healthcare industry, this research constitutes a significant contribution to the field. With its launch, the PerfoBalance algorithm offers a fresh viewpoint on waiting-patient value allocation and stress zone management in healthcare settings, hence representing a powerful method for increasing physician productivity.

The process of performance measurement provides support to company management to achieve the objectives established in strategic planning. Through the definition of critical success factors and related key performance indicators, performance measurement verifies the gap between planned objectives and the results achieved, informing the responsible bodies to enable them to evaluate performance and, if necessary, implement improvement actions. Although many types of companies adopt performance measurement, this process is challenging when applied to national health services. This paper aims to identify the evolution of performance measurement and the critical success factors of national health services.

The authors conducted an explorative case study of a leading national health service to delineate the evolutionary path of performance measurement and the main critical success factors.

The results indicate a significant increase in the maturity of performance measurement of a national health service that has been motivated by international reforms and national regulations. This research highlights performance measurement features such as a balanced set of metrics, targets, and incentives linked to strategic objectives and regular and frequent performance reviews. Furthermore, it identifies the performance measurement model of a leading national health service.

The evolution of performance measurement and numerous critical success factors of national health services are described; the critical success factors cover a wide range of financial to operational aspects such as patient safety, organizational appropriateness, and clinical appropriateness.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.