Enhanced Resource Allocation Research Articles

Comparison of the market share of public and private hospitals under different Medical Alliances: an interrupted time-series analysis in rural China

BackgroundChina initiated the Medical Alliances (MAs) reform to enhance resource allocation efficiency and ensure equitable healthcare. In response to challenges posed by the predominance of public hospitals, the reform explores public-private partnerships within the MAs. Notably, private hospitals can now participate as either leading or member institutions. This study aims to evaluate the dynamic shifts in market share between public and private hospitals across diverse MAs models.MethodsData spanning April 2017 to March 2019 for Dangyang County’s MA and January 2018 to December 2019 for Qianjiang County’s MA were analyzed. Interrupted periods occurred in April 2018 and January 2019. Using independent sample t-tests, chi-square tests, and interrupted time series analysis (ITSA), we compared the proportion of hospital revenue, the proportion of visits for treatment, and the average hospitalization days of discharged patients between leading public hospitals and leading private hospitals, as well as between member public hospitals and member private hospitals before and after the reform.ResultsAfter the MAs reform, the revenue proportion decreased for leading public and private hospitals, while member hospitals saw an increase. However, ITSA revealed a notable rise trend in revenue proportion for leading private hospitals (p < 0.001), with a slope of 0.279% per month. Member public and private hospitals experienced decreasing revenue proportions, with outpatient visits proportions declining in member public hospitals by 0.089% per month (p < 0.05) and inpatient admissions proportions dropping in member private hospitals by 0.752% per month (p < 0.001). The average length of stay in member private hospitals increased by 0.321 days per month after the reform (p < 0.01).ConclusionsThis study underscores the imperative to reinforce oversight and constraints on leading hospitals, especially private leading hospitals, to curb the trend of diverting patients from member hospitals. At the same time, for private hospitals that are at a disadvantage in competition and may lead to unreasonable prolongation of hospital stay, this kind of behavior can be avoided by strengthening supervision or granting leadership.

Healthcare Resource Allocation Optimization

Assuring high-quality patient care and making the best use of the resources at hand requires the efficient allocation of healthcare resources. This study uses a thorough examination of patient admission data, treatment plans, and staff availability to tackle the complex problem of optimizing healthcare resource allocation. We want to reduce wait times, optimize resource use, and eventually improve patient outcomes in healthcare facilities by integrating these essential elements. Our strategy makes use of cutting-edge data analytics tools, such as optimization algorithms and predictive modelling, to find underlying patterns and trends in patient demand, treatment regimens, and staffing needs. Our goal is to create prediction models that anticipate patient admissions, treatment requirements, and staffing levels by analysing historical data and real-time inputs. This will allow for proactive resource allocation techniques. The study's conclusions have important ramifications for healthcare administration since they provide practical advice on how to improve patient care delivery, cut wait times, and make the most use of the resources at hand. Healthcare institutions can enhance resource allocation procedures to meet the changing demands of patient care while guaranteeing the optimal use of resources by integrating technology, data-driven decision-making, and evidence-based practices. In summary, this study lays the groundwork for future investigations and innovations in hospital administration and operations while also supporting current initiatives to enhance healthcare resource allocation procedures. Healthcare organizations can improve patient outcomes and achieve operational excellence while providing high-quality, patient-centred care by adopting data-driven approaches and utilizing technology.

Early prediction of long hospital stay for Intensive Care units readmission patients using medication information

ObjectivePredicting Intensive Care Unit (ICU) Length of Stay (LOS) accurately can improve patient wellness, hospital operations, and the health system's financial status. This study focuses on predicting the prolonged ICU LOS (≥3 days) of the 2nd admission, utilizing short historical data (1st admission only) for early-stage prediction, as well as incorporating medication information. Materials and methodsWe selected 18,572 ICU patients’ records from the MIMIC-IV database for this study. We applied five machine learning classifiers: Logistic regression (LR), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (AB) and XGBoost (XGB). We computed both the sum dose and the average dose for the medication and included them in our model. ResultsThe performance of the RF model demonstrates the highest level of accuracy compared to other models, as indicated by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.716 and an Expected Calibration Error (ECE) of 0.023. DiscussionThe calibration improved all five classifiers (LR, RF, SVC, AB, XGB) in terms of ECE. The most important two features for RF are the length of 1st admission and the patient's age when they visited the hospital. The most important medication features are Phytonadione and Metoprolol Succinate XL. Also, both the sum and the average dose for the medication features contributed to the prediction task. ConclusionOur model showed the capability to predict the prolonged ICU LOS of the 2nd admission by utilizing the demographic, diagnosis, and medication information from the 1st admission. This method can potentially support the prevention of patient complications and enhance resource allocation in hospitals.

Trends in HIV-Related Services Offered by Substance Abuse Treatment Facilities.

Introduction In the United States, persons who inject drugs (PWID) represent an increasingly vulnerable population, with a high risk of HIV transmission related to needle sharing. This paper aims to investigate the availability of HIV-related services within substance abuse treatment facilities while emphasizing the need for implementing comprehensive harm-reduction strategies in such facilities. Methods This study explores the prevalence and trends regarding HIV-related services within substance abuse treatment facilities in the United States including testing, counseling, early intervention, and medication provision. Data from the National Survey of Substance Abuse Treatment Services (N-SSATS) were analyzed in order to assess trends in HIV-related services from 2013 to 2020. Results Facility response rates revealed an increase in the availability of HIV testing and specialized programs for individuals with HIV. However, there was a contrasting trend with the decline in early intervention and counseling services, only with a slight increase in 2020. Additionally, government-owned facilities demonstrated superior performance in delivering HIV services compared to private facilities. Conclusion This study highlights the dire need for implementing routine opt-out HIV testing within substance abuse treatment facilities in order to identify new cases. Additionally emphasized is the importance of early intervention for this at-risk population. To effectively address these challenges, we suggest considering the adoption of the "Seek, Test, Treat, Retain" model as a potential solution. Increasing access to HIV-related services within substance abuse facilities requires enhanced resource allocation as well as integrated programs. Identifying deficiencies in HIV service integration is crucial to enhancing care and reducing HIV transmission among PWID.

Integrating artificial intelligence into engineering processes for improved efficiency and safety in oil and gas operations

This paper delves into the significance, challenges, and potential of AI applications within the oil and gas sector. In the dynamic landscape of oil and gas operations, efficiency and safety stand as paramount concerns. Traditional engineering processes, while robust, often face limitations in adapting to the evolving complexities of the industry. However, the advent of AI technologies offers a paradigm shift, presenting unprecedented opportunities for optimization and risk mitigation. This paper explores the multifaceted role of AI in engineering processes throughout the oil and gas value chain. It examines how AI, encompassing machine learning, deep learning, and predictive analytics, empowers decision-makers with real-time insights, optimizing exploration, production, transportation, and refining processes. Efficiency gains are witnessed through predictive maintenance strategies, enabling proactive asset management and minimizing downtime. Additionally, AI-driven process optimization techniques enhance resource allocation, streamlining operations and maximizing output while reducing costs. Moreover, AI's integration fosters a culture of safety by augmenting risk assessment and hazard identification capabilities. Through advanced algorithms, AI systems analyze vast datasets to detect anomalies and predict potential safety hazards, enabling proactive intervention and accident prevention. However, the journey towards AI integration is not without challenges. Technical complexities, regulatory frameworks, and cyber security concerns pose significant hurdles that require careful navigation. Moreover, ethical considerations surrounding data privacy and algorithmic bias necessitate robust governance frameworks to ensure responsible AI deployment. Looking ahead, the paper delineates future trends and opportunities in AI adoption within the oil and gas sector. It underscores the potential for continued innovation and disruption, reshaping workforce dynamics and skill requirements. Embracing AI not only drives operational excellence but also propels the industry towards a sustainable and resilient future

Open government data and resource allocation efficiency: evidence from China

ABSTRACT Data are among the most valuable resources. This paper, utilizing the China Tax Survey Database from 2009 to 2016, investigates the impact of Open Government Data (hereafter, OGD) on the efficiency of resource allocation among firms within the Chinese context. The findings indicate that OGD significantly reduces the deviation in firms’ factor inputs, thereby enhancing the efficiency of resource allocation. This is primarily achieved through the optimization of government-business relationships, the reduction of rent-seeking costs, and the decrease in information asymmetry. Given the inclusive nature of OGD, it has a more pronounced positive impact on disadvantaged firms and cities. Furthermore, while OGD directly improves representative firm’s TFP, its greater economic impact lies in elevating aggregate TFP by enhancing resource allocation efficiency. In the current global economy, highly dependent on data-driven processes, the conclusions of this paper provide a novel perspective on the role of OGD in enhancing productivity. This has significant implications for accelerating the implementation of open data policies and enhancing economic efficiency.

A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication

In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE).

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Optimizing Enterprise AI Adoption with Converged Infrastructure: The Role of NVIDIA AI Enterprise and VMware in Streamlining IT Stack and Enhancing Resource Allocation

This research paper, titled "Streamlining IT Stack and Enhancing Resource Allocation; The Role of NVIDIA AI Enterprise and VMware" explores the benefits of combining NVIDIA AI Enterprise and VMware technologies to optimize IT infrastructure in businesses. By integrating these technologies organizations can effectively. Scale their AI initiatives focusing on innovation and extracting insights, from data. The paper highlights the importance of a integrated infrastructure, for hosting large scale language models and discusses how converged infrastructure eliminates the complexities associated with hardware and software. It emphasizes how this collaboration ensures performance, scalability, security and cost effectiveness enabling enterprises to leverage the potential of AI. To demonstrate the effectiveness of this approach the research includes testing with Cisco and NetApp converged infrastructure to deploy and manage AI models successfully. Ultimately this study showcases how businesses can gain an edge in todays evolving AI landscape.

Exploring the Impact of Artificial Intelligence on Patient Care: A Comprehensive Review of Healthcare Advancements

Artificial Intelligence (AI) is revolutionizing healthcare by transforming disease identification, treatment, and management. Healthcare organizations are rapidly adopting AI technologies to improve patient outcomes, streamline operations, and optimize costs. Utilizing a broad toolkit comprising Robotics, Computer Vision, Natural Language Processing, and Machine Learning, AI has made significant advancements across various healthcare domains. AI-driven diagnostic systems are showcased for their precision in analyzing medical images, enabling early detection of diseases such as cancer. Personalized treatment plans and preventive treatments are made possible by predictive analytics, which uses large amounts of patient data to predict the course of a disease and identify those who are at risk. This leads to an improvement in patient care. Beyond clinical applications, AI is reshaping healthcare delivery through solutions like telemedicine, virtual consultations, and remote monitoring. Virtual Health Assistants, empowered by AI, deliver personalized health information, medication reminders, and lifestyle guidance, enhancing patient engagement and treatment adherence. Telemedicine systems employ Artificial Intelligence (AI) algorithms to enhance resource allocation, expedite appointment scheduling, and supply superior medical services to remote and isolated populations. Hence, AI’s potential to improve productivity, encourage creativity, and solve difficult problems through sophisticated data analysis and automation is what has made it so important in revolutionizing many sectors.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response. To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources. Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.

A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.

The economic burden of Ebola virus disease: a review and recommendations for analysis

Background Ebola virus disease (EVD) continues to be a major public health threat globally, particularly in the low-and-middle-income countries (LMICs) of Africa. The social and economic burdens of EVD are substantial and have triggered extensive research into prevention and control. We aim to highlight the impact and economic implications, identify research gaps, and offer recommendations for future economic studies pertaining to EVD. Method We conducted a comprehensive librarian-led search in PubMed/Medline, Embase, Google Scholar, EconLit and Scopus for economic evaluations of EVD. After study selection and data extraction, findings on the impact and economics of EVD were synthesized using a narrative approach, while identifying gaps, and recommending critical areas for future EVD economic studies. Results The economic evaluations focused on the burden of illness, vaccine cost-effectiveness, willingness-to-pay for a vaccine, EVD funding, and preparedness costs. The estimated economic impact of the 2014 EVD outbreak in Guinea, Liberia, and Sierra Leone across studies ranged from $30 billion to $50 billion. Facility construction and modification emerged as significant cost drivers for preparedness. The EVD vaccine demonstrated cost-effectiveness in a dynamic transmission model; resulting in an incremental cost-effectiveness ratio of about $96 per additional disability adjusted life year averted. Individuals exhibited greater willingness to be vaccinated if it incurred no personal cost, with a minority willing to pay about $1 for the vaccine. Conclusions The severe impact of EVD puts pressure on governments and the international community for better resource utilization and re-allocation. Several technical and methodological issues related to economic evaluation of EVD remain to be addressed, especially for LMICs. We recommend conducting cost-of-sequelae and cost-of-distribution analyses in addition to adapting existing economic analytical methods to EVD. Characteristics of the affected regions should be considered to provide evidence-based economic plans and economic-evaluation of mitigations that enhance resource allocation for prevention and treatment.

SliceOptiAI: Smart Resource Allocation for Seamless Network Slicing

An AI-driven model for resource allocation in network slicing is examined in this research paper. The model’s algorithm comprises three stages, each with its specific algorithm. Resource allocation begins with reservation, where the controller reserves minimum resources for each slice. The second stage is autonomous radio resource management, mainly focusing on AI model training and decision engines. The last stage is physical resource allocation, where resources are distributed to the slices and users. Simulations on MATLAB software indicated this model to be effective in enhancing resource allocation.

Digital Twin for Enhanced Resource Allocation in 6G Non-Terrestrial Networks

Digital Twin for Enhanced Resource Allocation in 6G Non-Terrestrial Networks

Innovation at the Crossroads: How Public-Private Partnerships Drive Technological Development in Europe

This article investigates the pivotal role of public-private partnerships (PPPs) in driving technological development across Europe, highlighting how the collaboration between public institutions and private enterprises serves as a catalyst for innovation and economic growth. By analyzing a range of successful case studies and conducting an extensive review of existing literature, this study identifies the key factors that contribute to the effectiveness of PPPs and their substantial impact on technological advancement. The research reveals that PPPs significantly enhance resource allocation, allowing for more efficient use of financial and human capital. They facilitate risk sharing, which encourages private sector participation in high-risk technological projects that might otherwise lack sufficient funding or support. Moreover, PPPs promote knowledge transfer between the public and private sectors, fostering a culture of innovation and enabling the rapid deployment of new technologies. Different models of PPPs are examined to understand their implementation in various contexts, shedding light on the diverse approaches taken by European countries. The study also addresses the challenges faced in establishing and maintaining PPPs, such as regulatory hurdles, financial constraints, and coordination issues. Strategies for overcoming these barriers are discussed, providing practical insights for enhancing the success of PPPs. The findings underscore the critical importance of a supportive policy environment in nurturing PPPs. Effective regulatory frameworks, financial incentives, and clear governance structures are essential for maximizing the potential of PPPs. The article concludes with strategic recommendations for policymakers, aimed at strengthening PPP frameworks, fostering a conducive environment for sustainable technological innovation, and ensuring that the benefits of technological advancements are equitably distributed across society. By offering a comprehensive understanding of how PPPs drive technological development, this article contributes to the broader discourse on innovation policy and economic development in Europe. It underscores the necessity for continued collaboration between the public and private sectors to address the complex challenges of technological progress and to harness its potential for societal benefit.

Enhancing Resource Allocation and Optimization in IoT Networks Using AI-Driven Firefly Optimized Hybrid CNN-BILSTM Model

Enhancing Resource Allocation and Optimization in IoT Networks Using AI-Driven Firefly Optimized Hybrid CNN-BILSTM Model

Economic Efficacy: Exploring Decision-Making In Islamic School Budgeting For Enhanced Resource Allocation

Making the right decisions in determining Islamic School budgets is a key element to secure the efficiency and sustainability of the education system. The purpose of this study is Economic Efficacy: Exploring Decision-Making in Islamic School Budgeting for Enhanced Resource Allocation. This research method is based on a qualitative approach that allows researchers to understand in depth the context of decision making in Islamic School budgeting. The results of this study show that decision making in Islamic School budgeting plays a key role in the economic effectiveness of education resource allocation. Several key findings highlight the complexity and variety of factors influencing the decision-making process, including education priorities, government policies, and pressure from stakeholders. The results of this study show that Data analysis and the use of accurate information have proven essential in ensuring effective decision making. Islamic Schools that use solid data to evaluate student performance and the effectiveness of educational programs tend to make better decisions in budget allocation