Service Resources Research Articles

Women Empowerment Practices in Nepal

This study examines women empowerment practices in Nepal. Financial independency is the dependent variable. The selected independent variables are self-efficacy, leadership skill, entrepreneurial skills, access to resource and insurance service. The primary source of data is used to assess the opinions of the respondents regarding the women empowerment practices in Nepal. The study is based on primary data of 126 respondents. To achieve the purpose of the study, a structured questionnaire is prepared. The correlation coefficients and regression models are estimated to test the significance and importance of women empowerment practices in Nepal. The study showed that leadership skills have a positive impact on financial independency of Nepalese women. It indicates that better the leadership skills, higher would be the financial independency of the women in Nepal. Similarly, self-efficacy is positively correlated to financial independency. It means that increase in self-efficacy leads to increase in financial independency of the women. Similarly, access to resources has a positive relationship with financial independency. It implies that access to resources to women leads to increase in financial independency. The result also revealed that entrepreneurial skill has a positive relationship with financial independency. It indicates that enhancing entrepreneurial skills of women leads to increase in financial independency of women. In addition, insurance services are positively correlated with financial independency. This reveals that better insurance services to the women leads to increase in financial independency.

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Traditional techniques for edge computing resource scheduling may result in large amounts of wasted server resources and energy consumption; thus, exploring new approaches to achieve higher resource and energy efficiency is a new challenge. Deep reinforcement learning (DRL) offers a promising solution by balancing resource utilization, latency, and energy optimization. However, current methods often focus solely on energy optimization for offloading and computing tasks, neglecting the impact of server numbers and resource operation status on energy efficiency and load balancing. On the other hand, prioritizing latency optimization may result in resource imbalance and increased energy waste. To address these challenges, we propose a novel energy optimization method coupled with a load balancing strategy. Our approach aims to minimize overall energy consumption and achieve server load balancing under latency constraints. This is achieved by controlling the number of active servers and individual server load states through a two stage DRL-based energy and resource optimization algorithm. Experimental results demonstrate that our scheme can save an average of 19.84% energy compared to mainstream reinforcement learning methods and 49.60% and 45.33% compared to Round Robin (RR) and random scheduling, respectively. Additionally, our method is optimized for reward value, load balancing, runtime, and anti-interference capability.

Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study

BACKGROUND Psychological problems affect economic development. However, there is a huge gap between mental health service resources and mental health service needs. Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people. Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible. AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors. METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects. The general condition Questionnaire, Eysenck Personality Questionnaire, Symptom Checklist-90, General Health Questionnaire, and Smart Medical Service Use Intention Questionnaire were used to collect data. Pearson’s correlation was used to analyze the correlation between the participants’ willingness to use smart medical services and their personality characteristics, psychological symptoms, and mental health. RESULTS The results revealed that the mental health of young and middle-aged people was poor, and some had psychological problems such as anxiety, depression, and physical discomfort. Familiarity, acceptance, and usage of smart healthcare in this population are at a medium level, and these levels correlate with psychological characteristics. Acceptance was positively correlated with E, and negatively correlated with P, anxiety, fear, anxiety/insomnia, and social dysfunction. The degree of use was negatively correlated with P, obsessive-compulsive symptoms, depression, anxiety, hostility, paranoia, and somatic symptoms. CONCLUSION The familiarity, acceptance, and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.

MS-GD-P: priority-based service deployment for cloud-edge-end scenarios

In cloud-edge-end scenarios, how to achieve rational resource allocation, implement effective service deployment, and ensure high service quality has become a hot research topic in academic domains. Service providers usually deploy services by considering the characteristics of different geographical regions, which helps to meet the diverse needs of users in different regions and optimize resource allocation and utilization. However, due to the widespread distribution of users and limited server resources, providing all types of services to users in every geographical region is not feasible. In addition, edge servers are prone to operational failures caused by software anomalies, hardware malfunctions, and malicious attacks, which will decrease service reliability. To address the problems above, this paper proposes a metric for service priorities based on user demands and regional characteristics for different geographical regions. Building upon this foundation, a Multi-Service Geographic region Deployment based on Priority (MS-GD-P) is proposed. This method takes user coverage and service reliability into consideration, which facilitates users’ needs for multiple services in different geographical regions. Experimental results on real datasets demonstrate that MS-GD-P outperforms baseline methods in user coverage and service reliability.

Research on the Status Quo and Internal Control of Enterprise Human Resource Service Outsourcing

Research on the Status Quo and Internal Control of Enterprise Human Resource Service Outsourcing

Differential Pricing Strategies for Airport Shuttles: A Study of Shanghai Based on Customized Bus Ticketing Data

Airport shuttle buses, as a specialized form of bus service, serve as an economical, efficient, and sustainable transportation option for air travelers. In contrast to conventional bus services, airport shuttle bus operations exhibit more pronounced market-oriented characteristics, striving to balance extensive public transport coverage with the optimization of corporate profitability. Although these services outperform regular bus transit in terms of efficiency, they incur higher operational costs. However, existing studies on enhancing profitability and optimizing resource allocation for airport shuttle buses are inadequate. This study proposes a differential pricing strategy based on historical ticketing data. Initially, we analyze the characteristics of orders, users, and reservations within the context of customized bus operations. Leveraging the differences among various groups, we employ clustering techniques to classify seat grades and segment users. Based on the clustering outcomes, we determine distinct price elasticity values for each segment. As the strategies are developed based on seat grades, booking time, and user travel patterns, the numerical experiments indicate that the proposed differentiated pricing strategy can increase the revenue of customized public transport services by at least 41%. This strategy not only enhances the efficiency of resource allocation and service accessibility but also makes the service more financially sustainable.

TINJAUAN KETEPATAN KODEFIKASI DIAGNOSIS PENYAKIT HYPERTENSI BERDASARKAN ICD-10-CM PASIEN RAWAT JALAN PERIODE TRIWULAN 1 TAHUN 2023 DI RSUD SITI FATIMAH AZ-ZAHRA PROVINSI SUMATERA SELATAN

Coding is very important in the prospective financing system which will determine the amount of fees paid to the FKRTL (Advanced Referral Health Facility). Appropriately coded clinical data will be needed for information for the purposes of patient care, research, improving service performance, resource planning and management, as well as to obtain appropriate reimbursement for health services provided. The accuracy of a code is intended for all types of diseases, one of which is hypertension. The purpose of this study was to determine the accuracy and inaccuracy percentage of hypertension diagnosis codes. This type of research uses a quantitative approach with descriptive methods. The sample of this study was the medical record files of outpatients diagnosed with hypertension in the January-March 2023 period, totaling 71 files. From a sample of 71 files in the diagnosis of hypertension, it was found that the inaccuracy of filling in the hypertension diagnosis code was 7 files (10%). While the accuracy of filling is 64 files (90%). From a sample of 71 files in the diagnosis of hypertension, it was found that the inaccuracy of filling in the hypertension diagnosis code was 7 files (10%). While the accuracy of filling is 64 files (90%). Factors that cause inaccuracies in the diagnosis code are doctors who write the wrong diagnosis due to incomplete medical supporting information, such as checking blood pressure. Completeness of medical supporting examination results can be used as information to determine the appropriate diagnosis code. Another factor was the doctor's writing which was difficult to read which made it difficult for officers to input diagnoses into the SIMRS.

Sustaining mental health amidst high-pressure job scenarios: a narrative review

Today’s fast-paced world presents with increased work-related demands and pressures. These pressures may overwhelm an individual’s coping ability. From recognizing the signs of stress to implementing proactive measures for self-care, we embark on a journey to promote resilience and a healthy equilibrium between professional aspirations and mental well-being. Maintaining mental health and well-being is essential to living a healthy life even in high-pressure professional contexts. Nursing is one of the most challenging and demanding professions and use their psychological and emotional resources in service of their patients as well as navigating through the ups and downs of their own lives. Workplace stress has the potential to adversely impact both the physical and mental well-being of individuals, leading to a decline in job productivity through heightened stress levels. This leaves the professionals depleted and drained. Through this article, the author tries to highlight major stressors related to nursing and some practical coping measures.

Role of Mangroves as Fishery Resource: A Systematic Review

Mangroves are valuable for their fishery resources, coastline stabilization abilities, water quality and clarity benefits, provision of habitat to economically profitable and ecologically important species, carbon sequestration services, and climate regulation capabilities. For centuries, mangroves have contributed significantly to the socioeconomic lives of coastal dwellers; especially in coastal fishery by providing nursery grounds for finfishes. Fishery resource services provided by mangrove ecosystem play an important role as the habitat, nursery ground, feeding ground, and spawning ground for various marine fish species. Despite their enormous socio-economic value and ecological significance, mangrove ecosystems are under severe threat such as large scale clearing, small scale harvesting and grazing, and industrial threats. The productivity of mangrove ecosystems has been linked to the well-being of valuable fisheries because of the role mangroves play as nursery habitat for a variety of marine and estuarine species. Many species of mangrove-related fish and crab have served the coastal communities as a natural, cheap and balanced nutritional source. Mangroves provide vital ecosystem services to coastal fishing communities through the enhancement of fisheries production. Due to their economic and ecologic significance, it is important to look to the future of these systems in the face of ‘fishery resource’ so that policy makers may act quickly to preserve and protect these ecosystems. Present review provides recent information on role of mangroves as fishery resource by studying the updated published literature and the threats faced by the mangroves. It is suggested that strategies should be prepared and rigorously implemented for the conservation and restoration of mangrove ecosystem.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Pengaruh Budaya Organisasi, Disiplin Kerja, dan Lingkungan Kerja terhadap Kepuasan Kerja Pegawai Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Kabupaten Penajam Paser Utara

This study aims to analyze the influence of organizational culture, work discipline, and work environment on job satisfaction among employees of the Civil Service and Human Resource Development Agency in Penajam Paser Utara Regency. A quantitative descriptive method was used, with total sampling of 44 employees consisting of civil servants and government contract employees. Data collection was conducted through literature review, interviews, and questionnaires based on a Likert scale. Data analysis was performed using SEM-PLS with Smart-PLS and SPSS applications. The outer model was tested for construct validity and reliability, while the inner model measured relationships between variables using R-square values and t-statistic hypothesis testing. Results indicated composite reliability and Cronbach's Alpha values above 0.70, demonstrating good reliability. The R-square value of 0.729 showed that job satisfaction was influenced by organizational culture, work discipline, and work environment by 70.9%. Hypothesis testing showed that organizational culture, work discipline, and work environment significantly affected job satisfaction with p-value < 0.05. Based on the findings, it is recommended to enhance organizational culture, work discipline, and work environment to improve employee job satisfaction.

Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm

Industrial operating systems (IOS) are essential for supporting smart manufacturing, particularly in managing and utilizing heterogeneous production resources through resource instantiation scheduling (RIS) technique. However, RIS faces the challenge of efficiently selecting optimal resource service compositions from numerous options with varying quality of service. To boost the solving of the RIS problem and improve the quality of the solution, this paper proposes a novel hybrid algorithm, named DWOA, based on the whale optimization algorithm (WOA) and deep reinforcement learning (DRL). It first incorporates the DRL algorithm to learn experience from the historical data regarding exploration and exploitation in the WOA search process and train an optimal behavior decision model. Subsequently, utilizing the trained model, the DWOA can effectively guide the search agent in achieving a better balance between global exploration and local exploitation, thereby enhancing its convergence speed and solution quality. The effectiveness and efficiency of the DWOA approach are evaluated by the CEC2017 benchmark functions and RIS problems with various scales, compared with 11 state-of-the-art methods. The experimental results indicate that our method converges faster and produces better solutions for RIS problems.

Workforce estimate to treat mental disorders in the Kingdom of Saudi Arabia

BackgroundMental, neurological, and substance abuse (MNS) disorders describe a range of conditions that affect the brain and cause distress or functional impairment. In the Middle East and North Africa (MENA), MNS disorders make up 10.88 percent of the burden of disease as measured in disability-adjusted life years. The Kingdom of Saudi Arabia (KSA) is one of the main providers of mental health services and one of the largest contributors to mental health research in the region. Within the past decade, mental health resources and services has increased.MethodsWe employ a needs-based workforce estimate as a planning exercise to arrive at the total number of psychiatrists, nurses, and psychosocial care providers needed to meet the epidemiological need of mental health conditions of the population of KSA. Estimates for a potential mental health workforce gap were calculated using five steps: Step 1—Quantify target population for priority mental health conditions. Step 2—Identify number of expected cases per year. Step 3—Set target service coverage for each condition. Step 4—Estimate cost-effective health care service resource utilization for each condition. Step 5—Estimate service resources needed for each condition.ResultsThe planning exercise indicates an epidemiologic need for a total of 17,100 full-time-equivalent (FTE) health care providers to treat priority MNS disorders. KSA appears to have a need-based shortage of 10,400 health workers to treat mental disorders. A total of 100 psychiatrists, 5700 nurses, and 4500 psychosocial care providers would be additionally needed (that is, above and beyond current levels) to address the priority mental health conditions. The shortfall is particularly severe for nurses and psychosocial workers who make up 98.9 percent of the shortfall. This shortage is substantial when compared to other high-income countries. Overall, the workforce needed to treat MNS conditions translates to 49.2 health workers per 100,000 population.ConclusionChallenges to addressing the shortfall are Saudi specific which includes awareness of cultural customs and norms in the medical setting. These challenges are compounded by the lack of Saudi nationals in the mental health workforce. Saudi nationals make up 29.5 percent of the physician workforce and 38.8 percent of the nursing workforce. Policymakers and planners supplement this shortfall with non-Saudi providers, who must be mindful of Saudi-specific cultural considerations. Potential solutions to reducing the shortfall of mental health care workers includes nurse task shifting and training of general practitioners to screen for, and treat, a subset of MNS disorders.

AI Service Deployment and Resource Allocation Optimization Based on Human-Like Networking Architecture

AI Service Deployment and Resource Allocation Optimization Based on Human-Like Networking Architecture

Housing-Resources Spatial Mismatch Investigation of Affordable Housing and Policy Implementation: A Case Study from Hangzhou, China

Intensive spatial transformation and restructuring in China these years has brought the separation of residence-job, so as to residence-public services, which adversely affects people’s access and enjoyment of job and public service resources, especially for the disadvantaged populations with limited choice of residence location and insufficient access to those resources. The residents of affordable housing are among the typical disadvantaged populations under the existing affordable housing policy in China. Residence-job separation of affordable housing residents has been paid much attention in literature, but no specific study has been conducted on the residence-public services separation and the behavioral responses of the residents. Spatial mismatch theory was originally put forward to reveal the adverse labor market outcome brought about by residence-job separation, and nowadays some scholars have tried to extend the residence-job spatial mismatch to residence-other resource spatial mismatch. This research responds to both the dilemma facing by the disadvantaged affordable housing residents and the extension trend of spatial mismatch theory. By applying the spatial mismatch theory and taking several affordable housing communities of Hangzhou as examples, the study explore the problems existing in affordable housing in commuting, employment, public service and residents' behavior response. The results found that the current affordable housing policy has led to an increase in commuting costs, a decrease in employment accessibility and welfare, a decrease in accessibility and satisfaction of public services, and disruptive behavior in the affordable housing market. These problems are mainly caused by remote layout and single supply, unreasonable allocation methods, difficulty in exchanging housing units and inadequate supervision of undesirable behaviors. To alleviate the spatial mismatch between housing and resources in Hangzhou, policy suggestions could be considered from four aspects: housing layout and supply, allocation methods, housing exchange and supervision.

Tourism-driven livelihood dynamics: A comprehensive empirical study of Mount Kilimanjaro National Park communities in Tanzania

Tourism is pivotal for rural economic development; however, despite hosting a substantial number of tourists, many developing countries with rural landscapes face persistent poverty among local communities. This study aims to delve into the impact of tourism (geoheritage and national parks) on local livelihoods, examining drivers for livelihood portfolio and community decisions to engage in tourism activities. The Multivariate Probit and instrumental variable models (Instrumental Variable Probit, and Two Stage Least Squares) were employed on data from 582 respondents collected through cross-sectional questionnaires. Results highlight the significant influence of socioeconomic factors—sex, age, household size, credit access, market access, social membership, and education—on livelihood portfolio. Results show that specific tourism activities, such as accommodation services, handcrafts, catering and hospitality, and tour guiding, exert varying effects on household food security, income, and access to essential social services. Challenges hindering participation include education levels, information and awareness, service quality, cultural barriers, financial costs, government policies, competition, resource monopolisation, corruption, and infrastructure deficiencies. Recommendations include investments in skill development, infrastructure enhancement, cultural preservation, financial inclusion, regulatory frameworks, and community awareness programs. These strategies aim to facilitate household participation in tourism activities, promoting geotourism and enhancing the well-being of Mount Kilimanjaro National Park communities. Policymakers are urged to implement these measures to uplift local livelihoods and foster sustainable tourism in the region.

What determines informal care need among community-dwelling older adults in China? Results from a longitudinal study

BackgroundWith an intensified aging population and an associated upsurge of informal care need in China, there is an ongoing discussion around what factors influence this need among older adults. Most existing studies are cross-sectional and do not focus on older people living in the community. Conversely, this study empirically explores the factors that affect informal care need of Chinese community-dwelling older individuals based on longitudinal data.MethodsThis study constructed panel data using the China Health and Retirement Longitudinal Research Study (CHARLS) from 2011 to 2018 for analysis. Generalized linear mixed models were used to analyze the factors affecting reception of informal care, and linear mixed models were used to analyze the factors affecting informal care sources and intensity.ResultsDuring the follow-up period, 7542, 6386, 5087, and 4052 older adults were included in 2011–2018, respectively. The proportion receiving informal care increased from 19.92 to 30.78%, and the proportion receiving high-intensity care increased from 6.42 to 8.42% during this period. Disability (estimate = 4.27, P < 0.001) and living arrangement (estimate = 0.42, P < 0.001) were the critical determinants of informal care need. The rural older adults reported a greater tendency to receive informal care (estimate = 0.14, P < 0.001). However, financial support from children did not affect informal care need (P > 0.05).ConclusionsAt present, there is a great demand for the manpower and intensity of informal care, and the cost of informal care is on the rise. There are differences in informal care needs of special older groups, such as the oldest-old, living alone and severely disabled. In the future, the region should promote the balance of urban and rural care service resources, rationally tilt economic support resources to rural areas, reduce the inequality of long-term care resources, improve the informal care support system, and provide a strong community guarantee for the local aging of the older adults.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

A method of identifying domain-specific academic user information needs based on academic Q&A communities

PurposeTo address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.Design/methodology/approachThis study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.FindingsExperimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.Originality/valueThis study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

Navigating Dravet syndrome in Spain: A cross-sectional study of diagnosis, management, and care coordination.

Dravet syndrome (DS) is a rare form of refractory epilepsy that begins in the first year of life. Approximately 85% of patients have a mutation in the SCN1A gene, which encodes a voltage-gated sodium channel. The main objective of the present work was to assess the degree of knowledge of DS among Spanish primary care (PC) professionals, the communication flow between them and the pediatric neurologists (PNs), and the services available and resources offered to patients in Spain when searching for a diagnosis and adequate treatment. Two anonymized online surveys on DS diagnosis and patient management in PC were conducted with Spanish PC pediatricians (PCPs) and caregivers of DS patients in Spain. Most PCPs are aware of genetic epilepsy but lack full knowledge of DS and patient advocacy groups (PAGs). Access to epilepsy treatments varies among regions, with many referrals to hospitals and pediatric neurologists. Diagnosis is often delayed, with misdiagnoses and frequent emergency room (ER) visits. Treatment involves multiple drugs, and sodium channel blockers are used, which are contraindicated in DS treatment. Improved training, resources, and communication are needed for early diagnosis. To improve the care and treatment of DS patients in Spain, early diagnosis is required and, possibly, specific efforts aimed at identifying patients in adulthood, generating socio-sanitary structures that integrate social and health services to provide comprehensive care, taking into account the different features and comorbidities of the disease. Dravet syndrome (DS) is a form of genetic epilepsy that starts within the first year of life. We present a study showing that, while family doctors are aware of genetic epilepsies, many don't have a complete understanding of DS. Unfortunately, getting the right diagnosis can take a long time, leading to unnecessary visits to the emergency room. Patients often need several medications, and sometimes they're given drugs that aren't recommended for DS. The takeaway is that training for doctors, more resources, and improved communication could help creating better healthcare systems and therefore give easier access to the right therapies.