Resource Managers Research Articles

A Paradigm Shift in Cross-Border E-Commerce Regulatory Compliance: Evidence From Brazil

Cross-border e-commerce data quality and taxation pose a challenge for customs administrations worldwide. The increased volume of transactions involving small packages addressed to individuals has led to more precise controls, imposing a new dynamic in the need to receive accurate data. Globalisation has reached individuals actively participating in international trade as direct importers and exporters, made scalable by the platform-type digital business model. This study uses a qualitative approach to analyse Brazil’s Programa Remessa Conforme (Compliant Shipment Program), implemented by companies that treat small parcels in Brazil. It illustrates the program’s contribution to improving data quality, controls, and better allocation of human and technological resources in risk management, stimulating good practices and contributing to tax collection. Our findings also hold practical applications that can be used by other emerging markets facing similar issues in managing the risk associated with small packages.

ON THE METHOD AND TOOL OF INTELLECTUALIZATION OF PUBLIC MANAGEMENT OF ENERGY SUPPLY SECTOR IN UKRAINE

By analyzing the strategic documents on the development of the energy sector of the national economy of Ukraine, the article states that one of the key problems of the development in the context of the energy transition is that these documents lack goals and objectives to change the mentality of the population of the country and the human resources of the industry, as well as of public authorities and the administration of their activities. The article outlines the practical lack of conscious consideration of behavioural factors of human resources in the energy sector of national market economy, which is defined as one of the most difficult components to change given the energy transition of global national economies. To theoretically substantiate the possibilities of improving the public management of the energy transition strategy of the national energy supply system through the intellectualization of its managerial and executive personnel, the article proposes the use of technology of change management in the intellectual orientation and mentality of human resources involved in energy generation, distribution and consumption by intellectualizing and changing their mentality. For this, the article uses an original method and algorithm to create personal and group intellectual portraits based on the graph-analytical model of a person’s economic temperament. Ideal, desired and real portraits of a person were formalized, and recommendations were made on their possible participation in the energy supply processes. The article demonstrates that new mechanisms for using the intelligence of each employee and the collective intelligence of the system can be formed in the country’s energy management system through selecting human resources for the public management and administration system according to the reasonable and recommended intellectual dominants of the individual. These mechanisms are directed towards accomplishing tasks and ensuring the achievement of the goals of energy transition and sustainable development of the country, as outlined in the government document “Energy Strategy of Ukraine until 2050.

Surplus value quantification of overdue medical devices based on Kohonen network algorithm.

With the continuous updating and progress of medical equipment, the overdue medical device has problems such as management difficulties, resource waste, and potential security risks. Therefore, this paper used the Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, the Kohonen network algorithm was used to build a quantitative model of the surplus value of the overdue medical device, and the self-organization characteristics and data-driven learning ability of the Kohonen network were used to predict the surplus value of the equipment more accurately. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical devices, and further optimize the model performance, to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper evaluated the correlation between the service life and maintenance cost of eight types of overdue medical devices and quantitatively predicted the surplus value of overdue medical devices with the random forest algorithm. According to the comparison of prediction bias, the maximum deviation between the expected surplus value and the actual surplus value is only 1, and the deviation value by the random forest algorithm is as low as 6, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing the decision tree algorithm, the average error rate of the Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of the random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical devices, thus improving the efficiency of medical equipment management, reducing costs, and ensuring patient safety.

Second Version on A Centralized Approach to Reducing Burnouts in the IT industry Using Work Pattern Monitoring Using Artificial Intelligence Using MongoDB Atlas and Python

Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large amounts of data. Industries face a gigantic problem i.e., employee burnout which can charge a firm loss in numerous hours and thousands of dollars every year. The more advanced companies use work pattern monitoring using Artificial Intelligence to make their employees work more professionally. In this research my attempts to understand the development and leadership, on the effects of work pattern monitoring using Artificial Intelligence technology on information technology organizations (Sasibhushan Rao Chanthati, 2022). In this updated second version, the data of the employees will be stored on a cloud server with governance & compliances. The study discussed the development of methods which are configured as two different system interfaces, which are of minimum valuable product (MVP) and the results obtained from the two approaches. The system will provide work pattern monitoring via the ‘Real-Time Database – MongoDB Atlas’ which will synchronize the employee burnout data to improve the employee experience. This research also illustrates the advantages and disadvantages of the proposed solutions. “Burnout Detection Mechanism” that will help Industry management and Human Resource Management to manage the emotional state of the employees, understanding their real state. The study conducted a self-survey, and the outputs of the surveys are explained in this paper. The sample data we are using is mainly focused on information technology employment perception.

Epidemiology and real-world management of childhood epilepsy in Japan: A descriptive study using a health claim database

IntroductionThe purpose of this epidemiological study was to assess the prevalence, comorbidities, and real-world management of childhood epilepsy to provide insights for enhancing epilepsy management and medical resource planning. Materials and methodsThe study encompassed insured individuals aged 0–17 years as of December 2018 who were registered at any point in 2018, for at least part of the year, in a Japanese health claims database spanning January—December 2018. Epilepsy was defined as a diagnosis of epilepsy based on the International Classification of Diseases, 10th Revision codes, and a claimed management fee for epilepsy or an anti-seizure medication (ASM) prescription for longer than 4 weeks. The prevalence of epilepsy, patient characteristics, including comorbidities, and management status, such as prescription of ASMs, were evaluated. ResultsAmong 1528,905 registered children, 9279 were identified as having epilepsy. The prevalence of epilepsy was the lowest at 1.97 per 1000 population (95 % confidence interval [CI] 1.80–2.15) in the 0–2-year age group and increased with age to 9.34 per 1000 population (95 % CI 8.98–9.72) in the 15–17-year age group, with a significantly higher prevalence in boys than in girls in the ≥12-year age group. ASMs were prescribed to 88.3 %–91.9 % of the patients. Moreover, 27 (0.29 %) patients underwent epilepsy surgery. The frequency of claiming intravenous ASMs and long-term electroencephalogram fees increased with a decrease in age. ConclusionsOur findings indicate that young children receive more medical resources than adolescents and that epilepsy surgery is underutilized. Further investigations will help improve the management of and develop measures against epilepsy.

A research data management (RDM) community for ELIXIR.

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR's vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

Обеспечение качественного сервиса и поддержание конкурентоспособности предприятий гостеприимства: переосмысление подходов к стимулированию персонала отрасли

Every company, including those in the hospitality industry, faces an important task — finding and using effective approaches to distributing tasks in order to reveal the capabilities of each employee of the company and improve overall productivity. The leading role in increasing productivity is played by activating the interest of employees. Analysis of this area allows us to understand what serves as a stimulus for labor activity and what elements influence decision-making when performing tasks. An effectively designed incentive system is an important resource for management, contributing to increased employee loyalty and growth of the organization's profits. Such a system must be well-thought-out, balanced and really attractive to employees. In the hotel business, such strategies become key due to the specifics of the starting conditions, where employee salaries are often lower than in other industries. This leads to an outflow of qualified specialists and a drop in overall efficiency. However, a well-designed and implemented motivational system can radically change the situation.

Climate change impacts on the hydrological behaviour of watershed in northeastern Tunisia (Oued El Abid watershed)

Climate change significantly impacts watershed hydrology, altering water supplies, natural disasters, and eco-hydrologic processes. These effects vary geographically, with some regions facing severe droughts while others experience increased precipitation and flooding. Climate change influences atmospheric evaporation demand, precipitation patterns, vegetation composition, streamflow characteristics, and groundwater dynamics. The temperature increases and rainfall changes in some watersheds can lead to increased potential evapotranspiration and decreased streamflow and soil water. Flood frequency analyses indicate an overall increasing trend in the latter half of the 21st century. The Oued El Abid catchment area, as like all catchments in northern Tunisia, is likely to be subject to major climatic and hydrological variations as a result of climate change. Advanced hydrological modelling using the HBV-Light model was set up to simulate current conditions and future scenarios based on Afric-Cordex climate models. The model demonstrates robust performance, achieving Nash-Sutcliffe Efficiency (NSE) values of 0.71 in calibration (1972-1992), and 0.66 in validation from 1993-2001. This climate change has a direct effect on the hydrological projections, which reveal significant changes, with annual flow reductions ranging from 35% to 43%, with more severe impacts under the RCP8.5 scenario for 2069-2099. Spring and summer seasons are particularly vulnerable, showing flow decreases of up to 68% and over 40%, respectively, extreme monthly variability is projected, reaching 88% reduction in July under RCP8.5. This research provides important results for water resource managers, highlighting the necessity of long-term planning and sustainable water management practices in the face of climate change.

Investigating the performance of PBL parametrizations in WRF model for front enhanced SES simulation in istanbul megacity

Sea-effect snow (SES) is a meteorological phenomenon resulting from cold air moving over warmer waters. Accurate prediction of SES is vital for emergency management, transportation, and water resource planning. A thundersnow event in Istanbul from 17–19 February 2015 caused significant disruptions, with traffic and flights affected, highways temporarily closed, and trees falling due to heavy snowfall. This study investigates the influence of different parameterization schemes in the Weather Research and Forecasting (WRF) model on SES simulations. Six distinct PBL parameterization schemes were used in a series of WRF simulations. In addition, the following factors pivotal to SES event have also been investigated: 1000–500 hPa thickness, total and latent heat fluxes, radar and satellite analyses, temperature gradients, wind shear, inversion levels, and atmospheric stability indices. Additionally, the formation of SES during the cold front transition further contributed to these elements in the Black Sea region. The simulations displayed notably high total heat flux and latent heat flux values, particularly following the passage of the cold front. Furthermore, the northeast-southwest oriented SES cloud, distinguished by its banded structure, was successfully validated using radar and satellite imagery. However, it's worth noting that the model positioned it farther west than its actual location. This study highlights the challenges in precise prediction and analysis of such convective activities. In this thundersnow event, the local closure schemes, particularly MYNN in first place and second MYJ, demonstrated superior performance compared to non-local schemes within the parameterization options.

Cyanobacterial blooms management: A simulation-based optimization method

Controlling cyanobacterial blooms is not only an engineering and technical issue but also an optimization problem in environment management. Under budget constraints, a novel simulation-based optimization model for cyanobacterial control is constructed in this study. The simulation model is used for simulating cyanobacteria growth and diffusion processes. The optimization model is utilized to determine the optimal search and treatment path. Through the interactive coupling of simulation modeling and resource allocation optimization, this research provides decision-makers with new operational guidelines for cyanobacterial control. Our test results demonstrate that the initial invasion frequency has a greater economic impact than invasion abundance. The nearby cells to the initial invasion are affected first, and then the influence radiates outward in a diffusion pattern. Using a slow search speed and a treatment frequency of every 10 days can achieve the lowest possible economic losses in most test scenarios. Moreover, we also find that the optimal search and treatment paths revolve around the initial invasion location. This study is a typical interdisciplinary research, which can assist water resource managers in making more accurate decisions regarding cyanobacteria removal paths, removal frequencies, and treatment speeds.

Human-connected wild lands: How network analysis can inform invasive species management

Wild landscapes are critical strongholds for biodiversity, yet even in the remotest parts of the globe, increasing human use and development are leading to an influx of biodiversity threats including invasive species. Natural resource management agencies, and those that rely on public lands, need a better understanding of the long-distance dispersal pathways in which invasive species are introduced to remote locations. Pathway information is essential for targeting prevention and early detection across vast landscapes, but it is often challenged by information gaps and high surveillance costs. Data-driven approaches centered around a participating public can help resource managers and biosecurity professionals to better prioritize prevention and early detection activities to minimize incipient and secondary invasions. This study employed surveys with resource users to integrate and analyze multiple human-mediated dispersal networks for aquatic invasive species (AIS) across Alaska's part of the North American Boreal Forest. Specifically, it applied network analysis to further inform management priorities that so far were only based on a single pathway and different metrics. Results underline the vulnerability of remote wild freshwater systems to the introduction of AIS and provide a waterbody-specific tool for prioritizing monitoring and inform pathway-specific interventions that were unavailable through past research. The study compares the prioritization of waterbodies under newly derived network topology metrics accounting for multiple generic pathways with the existing single- species and single-pathway prediction model. Advantages of a more comprehensive multi-pathway network topology are discussed in the context of various invasion stages, multiple invasive taxa, and resource constraint conservation management systems.

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

Sustainable Natural Resources for Aphid Management: β-Ionone and Its Derivatives as Promising Ecofriendly Botanical-Based Products.

β-Ionone, sustainably derived from Petunia hybrida as a natural bioresource, was identified as a lead compound for integrated aphid management. A series of β-ionone derivatives containing ester groups were designed and synthesized for the purpose of discovering renewable botanical-based products. The odorant-binding protein (OBP) binding test indicated that β-ionone and its derivatives displayed binding affinities with Acyrthosiphon pisum OBP9 (ApisOBP9) and Harmonia axyridis OBP15 (HaxyOBP15). Bioactivity assays revealed that most β-ionone derivatives exhibited a higher repellent activity than that of β-ionone. β-Ionone and derivatives 4g and 4l displayed attractiveness to H. axyridis. Specifically, 4g was a highly promising derivative, possessing good repellent activity against A. pisum and attractiveness to H. axyridis. Molecular dynamics simulations revealed that integrating the hydrophobic ester group into the β-ionone framework strengthened the van der Waals interactions of 4g with ApisOBP9/HaxyOBP15, improving the binding affinity with OBPs and producing higher push-pull activity than β-ionone; 4g also had low toxicity toward nontarget organisms. Thus, 4g is a potential ecofriendly, botanical-based option for aphid management.

Spatiotemporal analysis of land use and land cover changes, LST and NDVI in Thatta district, Sindh, Pakistan

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources.

Peran Environmental Knowledge Memoderasi Pengaruh Green Supply Chain Management dan Enterprise Resources Planning Terhadap Sustainability Performance

The Green Supply Chain Management (GSCM) process is widely associated with its contribution to the company's sustainability performance. The GSCM process will also be associated with an integrated Enterprise Resource Planning (ERP) system that will have an impact on achieving sustainability performance. The importance of environmental awareness from manufacturers to direct their business processes that are beneficial to the environment in the long term. This study aims to analyze the effect of Green Supply Chain Management and Enterprise Resource Planning on Sustainability Performance, then this study also analyzes the role of Environmental Knowledge as a moderation of the influence of Green Supply Chain Management and Enterprise Resource Planning on Sustainability PerformanceThe analysis method used is path analysis with the pattern of relationships between independent variables in this study being correlative and causal. Researchers use primary data collection techniques because this study will use a questionnaire. This questionnaire technique is also a data collection technique by providing a set of written questions or statements to respondents. Based on the results of this study, it shows that Green Supply Chain Management has a significant effect on Sustainable Performance, Enterprise Resource Planning does not have a significant effect on Sustainable Performance, Environmental Knowledge strengthens the influence of Green Supply Chain Management on Sustainable Performance, Environmental Knowledge does not strengthen the influence of Enterprise Resource Planning on Sustainable Performance.

Drivers of Intrapreneurial Innovation: A Study of Individual Innovative Behaviour in Organisations

Organisations can gain a competitive edge through Intrapreneurial Behaviour (IB) by swiftly innovating and adapting to market changes. Research indicates that intrapreneurship plays a crucial role in the banking sector in achieving a competitive advantage. Organisations can enhance their intrapreneurial capability by leveraging Individual Innovative Behaviour (IIB). However, innovation in the banking sector is often constrained by strict regulations, high safety requirements, and a fundamental need for trust and stability. Therefore, it is important to embrace and stimulate factors that encourage and enable innovation. To our knowledge, research on how intrapreneurship can be stimulated and measured in the Dutch banking sector is currently lacking. Thus, developing indicators and metrics for measuring intrapreneurship in Dutch banks is essential. This study investigates the factors affecting IIB using a quantitative survey. Building on the connection between IB and IIB, IIB is treated as a dimension of IB. The perceived work context, comprising an organisation's entrepreneurial orientation and innovative climate, can empower employees' IIB in the digital era. This context encourages them to generate new ideas and engage in collaborative innovation without fear of failure, thus fostering IIB. Our study explores factors driving IIB, empowering employees to act as intrapreneurs through organisations perceived Entrepreneurial Orientation (EO) and Innovative Climate (IC), with Digital Maturity (DM) as a moderating variable. Data were collected in 2021 from the three largest Dutch banks, which hold over 43% of the market share. A snowball sampling method was used to reach Dutch bank managers knowledgeable about the study's key constructs. This method involved initial contact with human resources departments and managers within these banks. The survey was administered to 167 managers, yielding 125 usable responses (75% response rate). Results indicate that employees in the banking industry with high DM are more likely to engage in IIB when firms support EO strategies and IC, and when employees adopt self-leadership skills.

Post-acquisition transformation challenges: how did the change in ownership affect Soni Manipal Hospital, Jaipur, in the early years?

Subject area of the teaching case MBA. Student level and proposed courses the teaching case can be used on Master’s level in Change Management, Organizational Leadership and Human Resource Management. A brief overview of the teaching case Mr Sharma, the dynamic and entrepreneurial Chief Executive Officer (CEO) of the newly formed Soni Manipal Hospital (SMH), Jaipur, and Unit Head, Manipal Hospitals [Manipal Health Enterprises Pvt Ltd. (MHEPL)], in a meeting with SMH’s Head of Human Resources and the Head of the Nursing Management, Mr Yaduvanshi realised the exponential growth of employee resistance, their lack of skills and technological advancements for documentation hindering the hospital's transformation goal. The case study highlighted the challenges the protagonist faced when taking charge as the CEO after nine months of acquisition and the factors contributing to them. Expected learning outcomes Students reading this case are expected to understand leadership theories, strategic and quality management approaches, and theories of social behaviour, such as Herzberg’s two-factor theory and social exchange theory (SET) and the application of these concepts in acquired organisations to develop healthy leadership–employee relations and change management theories. Supplementary materials Teaching notes are available for educators only. Subject code CSS 6: Human resource management.

HumanEnerg Hotspot: Conceptual Design of an Agile Toolkit for Human Energy Reinforcement in Industry 5.0

This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) methodologies, enabling a comprehensive human-centered problem identification and solution-seeking approach. The toolkit includes a variety of strategies, techniques, frameworks, and resource recommendations for industry use and has been designed to be easily adaptable for use in diverse industry settings. The toolkit is intended to support the European Union’s goal for industry to influence society through a human-centric approach to Industry 5.0 by prioritizing human energy reinforcement and creating a more resilient and productive workforce. The toolkit provides a valuable resource for employees and managers alike and offers a promising solution for addressing the human energy crisis in the era of Industry 5.0.

The state of Louisiana’s freshwater commercial fishery: a study of fishermen’s attitudes towards fishing and their future

As an important contributor to Louisiana’s economy, the commercial freshwater fisheries have been the subject of growing attention in recent years by resource managers as the industry appears to be on the brink of collapse. Fewer participants are entering the field, fish buyers and processors are closing, and market prices remain stagnant. To better understand this industry, we conducted a study to characterize the commercial freshwater fisheries in Louisiana including the fishermen, the fishing operations, the effort across the state, local knowledge on management issue and barriers and opportunities for the future of the industry. We completed structured interviews at fish houses across Louisiana. The average full-time fisherman was 56 years old with about 34 years of commercial fishing experience, and catfish (Ictaluridae spp.) and buffalo (Ictobius spp.) appear to be the most sought-after finfish; however, this slightly differs by region. Overall, this study provides in-depth insight into the trends, problems, and successes of Louisiana’s freshwater commercial fisheries. This information will allow managers and other groups like cooperative extension to identify and design opportunities for the industry and utilize their information in management across the state.

Frailty among stroke patients and its effects on hospital outcomes

BackgroundAlthough assessment of frailty is increasingly being included in routine practice, its effects on hospital outcomes is not well studied. In this study, we used a national database to estimate the effects of frailty on hospital outcomes among stroke patients. MethodsThis study was a retrospective analysis of data from Nationwide Inpatient Sample (NIS) database collected during the years 2016 to 2019. Adult patients 45 years and older with a primary diagnosis of stroke were included for the analysis. Primary outcome was frequency of frailty among stroke patients. Secondary outcomes were in-hospital mortality, prolonged length of stay, mechanical ventilation, and acute renal failure. Frailty levels were assessed by using the Hospital Frailty Risk Score (HFRS). ResultsAmong 2,031,085 stroke hospitalizations, 362,140 (17.8 %) were non-frail, 1,333,000 (65.6 %) were pre-frail, and 335,945 (16.6 %) were frail. Regression analysis showed that the odds of mortality were significantly higher among frail (aOR, 2.82, 95 % CI: 2.63–3.04) and pre-frail (aOR, 1.62, 95 % CI: 1.53–1.73) patients, compared to non-frail patients. Similarly, the odds of mechanical ventilation were significantly higher among frail (aOR, 9.72, 95 % CI: 8.84–10.69) and pre-frail (aOR, 3.41, 95 % CI: 3.12–3.73) patients. The odds of acute renal failure were significantly higher among frail (aOR, 6.96, 95 % CI: 6.62–7.33) and pre-frail (aOR, 2.94, 95 % CI: 2.80–3.08) patients. ConclusionCollaborative efforts by neurologists, neurosurgeons, and physiatrists towards identifying frailty and incorporating it in risk estimation measures could help improve management strategies, resource utilization, and optimization of patient outcomes among frail stroke patients.