Resource Allocation Research Articles

Guidelines for diagnosis, treatment management pathways, and quality control of ovarian cancer in county-level regions (2025 edition)

To establish diagnosis, treatment management pathways, and quality control guidelines tailored to county-level hospitals, standardize ovarian cancer care, improve treatment quality, and promote the equitable distribution of high-quality medical resources, the Expert Committee on Ovarian Cancer under the National Cancer Quality Control Center formulated the "Guidelines for diagnosis, treatment management pathways, and quality control of ovarian cancer in county-level regions (2025 edition)". This guideline integrates existing national standards for ovarian cancer diagnosis and treatment with evidence-based medicine and clinical expertise. Structured as a pathway framework, this guideline addresses hierarchical diagnosis and treatment, diagnostic workflows, therapeutic approaches, and follow-up protocols for ovarian cancer in county-level settings, offering 13 specific recommendations. The guideline clarifies the roles of county-level hospitals in ovarian cancer management, including primary diagnosis and treatment, administration of antitumor therapies, maintenance therapy, adverse event management, recurrence monitoring, rehabilitation guidance, two-way referrals, and follow-up. For cases beyond local capacity, timely referral to higher-level hospitals or qualified county-level facilities is advised. County-level hospitals may perform surgery for early-stage ovarian cancer, but suspected advanced-stage cases should be promptly referred. The guideline emphasizes the importance of multidisciplinary collaboration in county-level hospitals and recommend that initial chemotherapy for newly diagnosed patients should be conducted at higher-level or adequately equipped county-level institutions. Follow-up evaluations may be performed locally, but suspected recurrence or metastasis requires referral to higher-level hospitals for assessment. Detailed pathways are provided for diagnosis, treatment (including surgery, chemotherapy, maintenance therapy, and recurrence management), and follow-up, with recommendations for incorporating online follow-up methods to enhance patient monitoring. By defining the roles of county-level hospitals, standardizing referral processes, and strengthening multidisciplinary coordination, the guideline aims to promote standardized ovarian cancer care. Its implementation is expected to improve county-level hospitals' diagnostic and therapeutic capabilities, foster collaboration with higher-level institutions, and ultimately enhance patient outcomes and quality of life. These efforts will optimize medical resource allocation, elevate national ovarian cancer care standards, and advance balanced regional healthcare development.

Post-trial access in the intersection between research ethics and resource allocation

In 2024, new legislation introduced significant changes to the rules, procedures and institutions governing research ethics in Brazil. One of its objectives was to limit sponsors’ post-trial access (PTA) obligations....

Prevalence and associated factors of caesarean section delivery: analysis from the Nepal Demographic and Health Survey 2022

ObjectiveEnsuring equitable access to emergency obstetric care is essential for reducing maternal mortality. This study examines the prevalence and associated factors of caesarean section (CS) delivery in Nepal during 2022.DesignThis...

Distributional equality in Ethereum? On-chain analysis of Ether supply distribution and supply dynamics

Blockchain-based emerging technologies such as decentralized finance (DeFi), cryptocurrencies, tokens, and smart contracts have introduced innovative frameworks for resource allocation and economic interactions. Ethereum, as the major technical network foundation of DeFi and tokenized assets, is becoming increasingly pivotal in facilitating an extension and alternative to traditional finance for many stakeholders, including those who are “unbanked”. Moreover, the recent transition of Ethereum from a proof-of-work (PoW) mechanism to a proof-of-stake (PoS) consensus mechanism and the Shanghai upgrade may significantly impact Ether (ETH) distribution. However, the status quo and dynamics of wealth distribution, especially after these changes in governance structure, remain unclear. By utilizing a rich dataset spanning the entire Ethereum history from July 2015 to December 2024, we analyze the balances across address groups of different sizes and the role of key economic activities and infrastructure components within Ethereum, such as exchanges, DeFi platforms, and staking. To provide detailed insights into ETH’s distributional equality, our approach combines descriptive, longitudinal, and causal inference analyses; a complete enumeration of more than 98 million unique wallet addresses; and novel on-chain analysis. Our findings show a substantial concentration of ETH within a small fraction of addresses, with approximately 0.3% of wallets holding nearly 95% of the total supply, despite the majority of wallets holding less than 0.1% ETH. However, the ETH distribution broadly resembles wealth distributions in traditional economies, with a log-normal body and Pareto-like tails. We assert that previous studies have overstated the concentration of ETH. Additionally, our dynamic analysis reveals a nuanced trend toward less concentration over time, driven by market cycles, increasing staking participation, and reinvestment in DeFi. These results challenge the notion of pervasive centralization. This study contributes to a deeper understanding of the current ETH distribution and its evolution over time. Therefore, this work provides an objective, data-driven basis for the ongoing discussion on wealth (in)equality in blockchain-based ecosystems, particularly in DeFi.

Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem

A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design a social planner that promotes sustainable contributions from human participants. We first trained neural networks to behave like human players, creating a stimulated economy that allows us to study the dynamics of receipt and reciprocation. We use RL to train a mechanism to maximise aggregate return to players. The RL mechanism discovers a redistributive policy that leads to a large but also more equal surplus. The mechanism outperforms baseline mechanisms by conditioning its generosity on available resources and temporarily sanctioning defectors. Examining the RL policy allows us to develop a similar but explainable mechanism that is more popular among players.

Sport tourism and destination attractiveness: bridging gaps in research for emerging economies

ABSTRACT While sport tourism holds immense potential for emerging economies, limited research explores factors influencing destination attractiveness in these contexts. This study addresses this gap by identifying and evaluating key drivers of sport tourism destination attractiveness (STDA) in emerging economies. We broke down the data analysis process into three steps. The first step was to conduct a literature review and use industry and academic experts’ assistance to identify the critical factors- fuzzy Delphi method (FDM). We then utilized the Analytic Hierarchy Process (AHP) method to evaluate the criteria and ascertain their relative significance. In the last step, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to deal with the importance and causal relationships among the criteria and to recognize the influential criteria of STDA. We identified 21 key factors related to the STDA in emerging economies. Having a grasp of these key factors can assist managers and decision-makers in optimizing resource allocation, devising targeted marketing strategies for different sport tourism segments, and improving the overall tourist experience in these destinations. This knowledge enables them to make well-informed decisions regarding developing and promoting their destinations, and ultimately stimulates economic growth.

DEA-based centralized resource allocation with a balance between efficiency and equity: evidence from healthcare services across 31 provinces in China.

In the context of increasing investment in healthcare, the key issue of China's healthcare system reform is how to maximize output and ensure the equity of resource allocation. The generalized DEA-based resource allocation model (Model 1) pursues the maximization of DMU efficiency in resource allocation without considering equity, and it could yield a multi-solution problem by considering only the outputs instead of the inputs in the objective function. Thus, a DEA-based centralized resource allocation model with a balance between efficiency and equity (Model 2) is proposed, in which efficiency and equity are measured by output and input indicators in the objective function simultaneously, this could be more consistent with the essence of the DEA method. Model 2 effectively prevents the multi-solution problem by introducing both outputs and inputs into the objective function, and its Pareto-efficiency is proven. The main advantage of the proposed Model 2 is that efficiency and equity can be optimized in resource allocation; in particular, it can ensure equity for all DMUs in both absolute and relative terms. Furthermore, we illustrate and examine the application of Model 2 with centralized healthcare service resource allocation across 31 provinces in mainland China. We investigate the properties and effectiveness of Model 2 by comparison with Model 1 in terms of both efficiency and equity. Efficiency and equity are measured from three perspectives: efficiency values and slacks, input and output indicators, and allocation deviation. The results prove that Model 2 is superior to Model 1 in terms of both efficiency and equity.

Innovative Technologies for Smarter and Efficient Operating Room Scheduling.

An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., which developed a predictive model of surgical case durations. The model appears to adopt a pragmatic approach by analyzing tangible variables and undergoing validation across various types of surgical procedures, which suggests potential avenues for enhancing efficiency and sustainability in healthcare practices. However, we have some observations, particularly regarding the feasibility and practical implementation of the proposed model. A key limitation of the model is the precise definition of surgical duration, which requires further specification. To effectively translate the model into a practical scheduling approach, it is essential to consider total Operating Room (OR) occupancy time as a critical determinant of surgical planning and resource allocation. This includes not only the actual procedural time but also preoperative preparation, anesthesia induction and recovery, cleaning, and material restocking, all of which significantly impact overall scheduling efficiency. Another critical aspect concerns the quality and reliability of the input data, which is fundamental for ensuring the accuracy and effectiveness of the model. Furthermore, the adoption of new technologies should be regarded not merely as an innovation but as a means to develop high-performance, efficient tools that enhance current clinical practice. In this context, machine learning models should not only serve as analytical instruments but also as actionable tools, enabling the transition from predictive insights to strategic planning and optimized scheduling, ultimately improving decision-making and resource allocation. While making accurate predictions is a good starting point, maintaining an active AI model requires investment in resources, such as an increase in the number of surgical cases compared to the current organizational system. It may be beneficial to consider the creation of a multidisciplinary group that could promote the integration of AI with other emerging technologies.

Body Mass Index, Comorbidities, and Ambulatory Care Visits: The REGARDS Study.

Individuals with obesity have more ambulatory care usage than individuals with normal weight and overweight. There is limited information on whether this is consistent across provider specialties and whether comorbidities explain the associations. Among REGARDS (Reasons for Geographic and Racial Differences in Stroke) cohort study participants with Medicare fee-for-service coverage (n=9648), we identified ambulatory visits over 5 years. We used marginalized zero-inflated Poisson models to calculate ratios of means by body mass index (BMI), adjusted for demographics and health behaviors, and inverse odds weighting to evaluate mediation by diabetes, hypertension, dyslipidemia, stroke, coronary heart disease, atrial fibrillation, heart failure, chronic kidney disease, depressive symptoms, cancer, arthritis, and sleep apnea. The mean age of participants was 71.7±7.3 years, 35.1% were Black individuals and 64.9% White individuals, and 51.1% were women. Participants had a mean of 37.8 total, 16.0 primary care, 3.4 cardiology, 1.9 orthopedics, 0.9 pulmonology, and 0.4 endocrinology visits. Compared with individuals with BMI 18.5 to <25 kg/m2 (n=2613), participants with BMI ≥35 kg/m2 (n=1259) had 23% (95% CI, 21%-24%) more ambulatory visits. Participants with BMI ≥35 kg/m2 had 26% more primary care, 20% more cardiology, 74% more orthopedics, 62% more pulmonology, and 85% more endocrinology visits. Comorbidities partly explained associations with overall, primary care, and orthopedics visits (39%, 38%, and 15%, respectively) and largely explained associations with cardiology, pulmonology, and endocrinology visits. Understanding which specialty visits are associated with higher BMI can help with workforce planning and allocation of resources.

A standard network selection and resource allocation mechanism in 5G heterogeneous networks using hybrid heuristic algorithm with multi-objective constraints

The integration of various Random Access Technologies (RATs) within 5G Heterogeneous Networks (HetNets) to satisfy the diverse communication needs of the Internet of Things (IoT) causes significant challenges in network topology selection and resource allocation. Traditional approaches do not handle network congestion and poor user experience which necessitates the development of more efficient and intelligent network management strategies. The main novelty of the research work is to combine the two intelligent optimization algorithms to address the complexities of resource management for enhancing system performance. The integrated optimization algorithm also aims to reduce the latency and communication costs while enhancing resource utilization within the network. The novel Hybrid Snow Leopard and Dark Forest Algorithm (HSL-DFA) combines the strengths of the Snow Leopard Optimization Algorithm (SLOA) and the Dark Forest Algorithm (DFA) to optimize network performance based on the multiple objectives including resource utilization, makespan, Quality of Service (QoS), energy consumption, communication cost, congestion control, and latency. The HSL-DFA algorithm integrates the SLOA and DFA to leverage their respective advantages in solving complex optimization problems. It worked based on a position-updating process using current and mean fitness values. Here, the SLOA is used for the position-updating process if the current fitness exceeds the mean fitness and DFA is used for the opposite scenario. This approach ensures higher convergence rates and optimal solutions for complex network optimization problems. By addressing multi-objective constraints, the algorithm significantly improves network performance and provides a promising solution for the management of a 5G network. Various metrics are utilized to confirm the effectiveness of the proposed model. The results showed that the throughput of the proposed model was 93 at the 200th node.

Integrated specialty care for amyloidosis: a scoping review using the Consolidated Framework for Implementation Research

BackgroundAmyloidosis is a complex and rare disease requiring specialized, multidisciplinary care to effectively manage its diverse manifestations. Existing evidence underscores the benefits of such care, linked to improved patient outcomes and clinician satisfaction. With the rising incidence of amyloidosis diagnoses and rapid advancements in treatment, the need for coordinated, expert-led care is increasing. However, implementing these centers is challenging due to resource allocation and inter-specialty collaboration. While resource allocation is a known hurdle, there has not been a comprehensive review of all the barriers and facilitators to establishing these clinics. This scoping review aims to identify the barriers and facilitators related to the implementation of coordinated, multidisciplinary specialty care clinics in amyloidosis management.MethodsAn electronic search was conducted in Medline, Embase, and CINAHL for studies published in English from 2013 to 2023, supplemented by a grey literature search. The inclusion criteria focused on studies discussing multidisciplinary clinical environments for amyloidosis care, particularly light-chain (AL) and transthyretin amyloidosis (TTR). Exclusion criteria included books, opinion pieces, dissertations, and conference abstracts. Data were analyzed and synthesized using a narrative synthesis approach, guided by the Consolidated Framework for Implementation Research (CFIR), and reported according to PRISMA-ScR guidelines.ResultsThe search resulted in 1547 findings. After screening with Covidence, 7 papers were included in the final review. Independent reviewers screened and extracted the papers. Key facilitators identified include access to experts, adequate staffing, secure funding, partnerships with patient advocacy groups, and robust processes for multidisciplinary communication. Barriers primarily relate to the complexity of care, a lack of standardized protocols, difficulties in communication and coordination between providers, and challenges in training and maintaining knowledgeable care providers. The review also revealed significant gaps in existing research.ConclusionsThis review enhances understanding of the barriers and facilitators in establishing amyloidosis specialty clinics. Addressing these barriers and leveraging facilitators are crucial for shaping the future of amyloidosis care. These insights support a model for implementing integrated care for this growing patient population and highlight the need for further research to support policy development and effective implementation of these specialized clinics.

A decision-making framework for evaluating medical equipment suppliers under uncertainty

The procurement of medical equipment is a critical concern for healthcare organizations striving to deliver comprehensive patient care. Thus, the procurement process, including performance evaluation and selection of medical equipment suppliers, poses a significant challenge for healthcare organizations. The decision-making process also involves multiple decision-makers making subjective judgments about various quantitative and qualitative criteria for several alternative suppliers. This paper presents a framework for medical equipment supplier evaluation under uncertain assessment information by integrating rank order centroid (ROC) and fuzzy analytic hierarchy process (fuzzy AHP) techniques. The first stage involves identifying the key criteria influencing the performance evaluation of medical equipment suppliers for healthcare organizations. The ROC technique is used to assign weights to the important criteria, reducing uncertainty of weight assignment and subjective judgment information of the decision-makers. The fuzzy AHP method is then applied to evaluate and rank potential suppliers based on their overall performance. The approach is validated through a case study in a hospital setting, demonstrating the practical applicability of the proposed approach in a real-world scenario. Results indicate that the proposed hybrid method effectively supports group decision-making under uncertainty, providing healthcare organizations with a systematic and logical approach for selecting the most suitable medical equipment supplier. This framework enhances procurement efficiency and supports better resource allocation in healthcare.

Geographical information system mapping of paediatric referrals to the community paediatric dental services in Birmingham, UK.

Aims To map the origin of paediatric referrals to the Community Dental Services (CDS) using geographical information systems (GIS). This is to assess the spatial relationship between areas of high referral density in relation to measures of deprivation according to the English Index of Multiple Deprivation 2019 (IMD) and subsequently recommend the allocation of resources to areas of need.Materials and methods Information from 4,146 paediatric referrals to the CDS over an 18-month period between April 2017 and October 2018 was collated and mapped using the GIS software (ArcGIS), allowing for analysis of data and identification of patterns. Maps of both IMD and decayed, missing or filled teeth scores were generated using the Indices of Deprivation Interactive Dashboard and Public Health Profiles mapping resources. Both paediatric referral data and deprivation score maps were then superimposed to create a combined map, permitting identification of spatial relationships.Results Most patients were externally referred to the CDS with three main triage outcomes: Level 2, Level 3a and Level 3b care. The maps produced showed high densities of referrals for patients residing in areas with the highest deprivation scores.Conclusion Such information may be of use to managed clinical networks when planning locations of healthcare facilities.

Effectiveness of Patients' Education and Telenursing Follow-Ups on Self-Care Practices of Patients With Diabetes Mellitus: Cross-Sectional and Quasi-Experimental Study.

Information and communications technology can be used in telenursing to facilitate remote service delivery, thereby helping mitigate the general global nursing shortage as well as particular applications (eg, in geographically remote communities). Telenursing can thus bring services closer to end users, offering patient convenience and reduced hospitalization and health system costs, enabling more effective resource allocation. This study aims to examine the impact of patients' education and telenursing follow-ups on self-care indicators among patients with type I and type II diabetes mellitus (DM). In phase I, a cross-sectional descriptive analysis was conducted to evaluate the self-care practices of 400 patients with DM at Kafr El Sheikh University Hospital in Egypt. In phase II, a pretest-posttest experiment was applied with a selected group of 100 patients purposively recruited from phase I due to their low self-care practice knowledge to ascertain the impacts of a 4-week intervention delivered via telenursing. They were reminded via telephone follow-up communication of the importance of adhering to recommendations on physical activity, nutritional intake, and the management of blood sugar (ie, insulin). Data collection was undertaken using a structured quantitative questionnaire, encompassing sociodemographic characteristics, medical symptoms and history, and knowledge of DM. Paired t test analysis was applied to study pre- and postintervention self-care behaviors. Participants had a mean age of 49.7 (SD 11.5) years. More than one-third received their DM diagnosis over a decade previously (135/400, 33.8%) and were obese (147/400, 36.8%). Almost half (176/400, 44%) received insulin, and the majority had cardiac disease (231/400, 57.7%) and the DM symptom of elevated blood sugar levels while fasting (365/400, 91.3%). A relatively high score of DM knowledge was reported (255/400, 63.7%). Males exhibited significantly lower knowledge levels (102/200, 51%) compared to females (153/200, 76.5%; P<.001). The intervention was effective in improving knowledge of DM (t99=30.7, two-tailed; P<.001), self-care practices (t99=53.7, two-tailed; P<.001), and self-care skills (t99= 47, two-tailed; P<.001) among patients with DM. The emergent evidence suggests that patients' education and telenursing follow-ups have the potential to improve self-care behavior in patients with DM. The delivery of frequent nursing reinforcement via telenursing enables improved self-management while contemporaneously reducing the need for patients to visit clinical settings (ie, improving patient condition and reducing net health system costs). The outcomes of this research underscore the need to integrate telenursing within conventional care for DM, and more research is needed to longitudinally assay its efficacy and sustainability over the long term and in different clinical and geographical contexts.

Transforming Patient Care: The Role of Predictive Analytics in Medical Diagnosis and Resource Allocation

Transforming Patient Care: The Role of Predictive Analytics in Medical Diagnosis and Resource Allocation

Healthcare and productivity cost of osteoporosis: a Danish register-based quasi-experimental study.

As the prevalence of osteoporosis rises, driven by an ageing population, quantifying its financial impact and guiding resource allocation becomes crucial. The aim of this paper is to establish the healthcare (medical and social care) costs and productivity costs attributable to osteoporosis and osteoporosis-related fractures in Denmark. The osteoporosis and osteoporosis fracture groups were identified from Danish healthcare registers using ICD-10 codes. The intervention group included individuals born in 1930-1950 with an osteoporosis diagnosis or an osteoporotic fracture with incidence between 2000 and 2021. A control group without osteoporosis and osteoporosis fractures was matched 1: 1 on a number of clinical and demographic variables from the general Danish population. Difference-in-difference approach was applied through generalised estimating equations with individual-level fixed effects to establish attributable costs. Osteoporosis and osteoporosis-related fractures can be attributed with more than €3097 annually in healthcare costs for individuals aged 50 to 91, with expenses increasing sharply with age. Cumulative attributable healthcare (medical and social care) cost of osteoporosis between the ages of 50 and 91 was estimated at reach €127,000 per person. For the identified population of over 667,000 people with osteoporosis, the total annual healthcare burden attributable to the disease would amount to over €2 billion. The osteoporosis group also incurred an annual productivity loss of €3883, until the age of 66. Osteoporosis carries a pronounced economic burden for the health system and the individual. Resource allocative decisions should consider whether implementing strategies improving prevention, earlier detection, and better management of osteoporosis could be efficient given the high identified costs.

Understanding healthcare efficiency—an AI-supported narrative review of diverse terminologies used

BackgroundPhysicians have become more responsible for pursuing healthcare efficiency. However, contemporary literature uses multiple terminologies to describe healthcare efficiency. To identify which term is best suitable for medical education to equip physicians to contribute to healthcare efficiency delivery in clinical practice, we performed a narrative review to elucidate these terms' meanings, commonalities, and differences.MethodsThe PubMed-database was searched for articles published in 2019–2024 describing healthcare efficiency terminology. Eligible articles conceptually described and applied relevant terminologies for physicians, while empirical studies and practice-specific articles were excluded. The screening was supported by an open-source artificial intelligence tool (ASReview), which prioritizes articles through machine learning. Two reviewers independently screened the resulting articles, resolving disagreements by consensus. Final eligibility was determined through predefined inclusion criteria.ResultsOut of 3,655 articles identified, 26 met the inclusion criteria. Key terminologies: cost-effectiveness, high-value care, low-value care, and value-based healthcare, were identified, and explored into more depth. ‘Value’ is central in all terms, but our findings reveal that the perspectives herein differ on what constitutes value. Within cost-effectiveness, resource allocation to the population’s needs drives decision-making—maximizing value at population-level. Within value-based healthcare, patient-centricity guides decision-making—maximizing value at individual patient-level. High-value and low-value care are somewhat ambiguous, depending solely on cost-effectiveness results or patient preferences to determine whether care is considered as low or high value.ConclusionsCost-effectiveness may be too rigid for patient-physician interactions, while value-based healthcare might not ensure sustainable care. As physicians are both stewards of finite societal resources and advocates of individual patients, integrating cost-effectiveness (resource allocation for population needs) and value-based healthcare (individualized care plans) seems necessary. Both terms emphasize delivering high-value care and avoiding low-value care. We suggest that medical education: (1) train (future) physicians to apply healthcare efficiency principles through case-based discussion, (2) use the cost-effectiveness plane to evaluate treatments, (3) deepen knowledge of diagnostic and treatment procedures’ costs within evidence-based guidelines, and (4) enhance communication skills supporting a healthcare efficiency-driven open shared decision-making with patients.

Uncovering Fraud in Medicaid: A Data- Driven Approach to Detecting Waste, Abuse, and Inefficiencies in Public Health Programs

Healthcare fraud continues to be a challenge, funds critical to patient care are diverted from being used where it is needed most, trust in government programs is eroded, and the existence of Medicaid is threatened. This paper considers how AI can thwart fraudulent activities, especially GNNs and GPT driven auditing. The real time fraud detection is improved with the aid of AI driven solutions, evaluation of claims is automated and policy is enforced. AI handles advanced data analysis and machine learning to improve oversight and this results in an efficient allocation or use of resources. The implications for AI in public healthcare funds protection, the reduction of financial losses and transparency in turn contribute to an accountable and equitable public healthcare system.

Ethical dilemmas and decision-making in emergency and critical care nursing in Western Amhara region, Northwest Ethiopia: a multi-method qualitative study

BackgroundEthical dilemmas in emergency and critical care nursing often involve complex decision-making that impacts patient outcomes, emotional well-being of healthcare providers, and team dynamics. Understanding these dilemmas and the decision-making processes involved is crucial for improving nursing practices and patient outcomes in this context.ObjectiveThis study aims to explore the ethical dilemmas and decision-making processes of emergency and critical care nurses, using the Four Box. Method ethical decision-making framework.MethodsA multi-method qualitative approach was employed, incorporating in-depth individual interviews (IDI), focus group discussions (FGD), and case studies to capture diverse perspectives. Data were collected from 28 nurses working in emergency and critical care settings at Tibebe Ghion Specialized Hospital and Felege Hiwot Comprehensive Specialized Hospital through purposive sampling. The interviews and focus groups were audio recorded, transcribed verbatim in Amharic, translated into English, and entered into OpenCode software for analysis. Inductive thematic analysis method was applied to analyze the data.ResultsThe study revealed several key ethical dilemmas faced by nurses, including balancing patient autonomy with beneficence, and the allocation of limited resources during crises, opioid administration for pain management vs. respiratory depression, and conflicts regarding informed consent. Nurses reported emotional and professional impacts from these dilemmas, contributing to moral distress, burnout, and ethical fatigue. Nurses emphasized the importance of interdisciplinary collaboration and structured decision-making frameworks to navigate these challenges. However, the lack of consistent access to ethics consultations and peer support during critical moments was noted as a significant barrier.Conclusion and recommendationNurses encounter complex ethical dilemmas that impact their emotional well-being and job satisfaction. Effective decision-making requires applying ethical principles, adhering to guidelines, and consulting peers. The study reveals the need for improved support systems, including more frequent and comprehensive ethics training, better access to ethics consultations, and structured frameworks to guide decision-making in high-pressure situations.Clinical trial registrationThe study is not a clinical trial, a clinical trial number is not applicable.

Primary Prevention of Cardiovascular Disease in Asia: Challenges: A Narrative Review.

Primary Prevention of Cardiovascular Disease in Asia: Challenges: A Narrative Review.