Decision Support Capabilities Research Articles

Resilience Assessment of Deep Excavation Engineering Systems Based on Bayesian Network

As a key component of infrastructure, the construction safety of deep excavation is particularly important. In order to improve the safety and stability of Deep excavation construction, research on the risk resistance and rapid recovery capabilities of Deep excavation projects is carried out. By introducing the concept of resilience, we analyze the key factors that affect the safety and resilience of Deep excavation projects, use Bayesian networks to establish a resilience model that expresses the causal relationship between factors, and learn the parameters of the network model through data samples collected through surveys; by adjusting the network Analyze the relationship between each factor and system resilience at each node, and clarify the measures that should be taken and key control objects, thereby improving the system resilience and ensuring that normal construction can be carried out safely and orderly. Research shows that this model can be used to quantify the safety toughness value of deep excavation and reason and analyze the impact of various factors on the toughness value, thereby improving risk management and decision support capabilities.

RDAG U-Net: An Advanced AI Model for Efficient and Accurate CT Scan Analysis of SARS-CoV-2 Pneumonia Lesions.

Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools.

Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.

Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA-related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes.

Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems

This research article investigates the synergies between artificial intelligence (AI), digital transformation (DT), and financial reporting systems within the business context. The central theme explores how organizations enhance their decision-making processes by integrating AI technologies into digital transformation initiatives, particularly in financial reporting. The focal point is comprehending how the synergy of these integrated systems can unlock substantial business value, instigate strategic innovation, and elevate overall financial analytics through the adoption of intelligent, data-driven decision-making methodologies. By harnessing advanced analytics, automation, and adaptive decision support capabilities, organizations navigate the complexities of a rapidly evolving business environment, in which neural networks emerge as a valuable tool for calibrating outcomes in the financial accounting environment, demonstrating effectiveness in processing complex financial data, identifying patterns, and making predictions, ushering in a new era of transformative possibilities. The introduction of a game theory payoff matrix in this AI decision-making tool adds a strategic framework for analyzing interactions among decision-makers, considering strategic choices and outcomes in a dynamic and competitive context.

Implementing Pega Collections Command Center for Real-Time Monitoring and Decision Support

The Pega Collections Command Center (PCCC) represents a significant advancement in financial institutions' capabilities for real-time monitoring and decision support. By integrating dynamic dashboards, alerting mechanisms, and predictive analytics, PCCC offers a comprehensive solution for managing collections operations. This technical paper explores the implementation of PCCC, detailing its features and the benefits it provides to financial institutions. The study discusses the rationale behind this technological adoption, the methodologies employed, the results observed, and the broader implications of this implementation. The findings contribute to the existing knowledge base by demonstrating the effectiveness of PCCC in enhancing operational efficiency and decision-making processes.

Generative AI enhanced with NCCN clinical practice guidelines for clinical decision support: A case study on bone cancer.

e13623 Background: Bone cancer is a complex and challenging disease to diagnose and treat in clinical practice. Recently, generative AI, especially large language models (LLMs), has demonstrated potential as a decision support tool for cancer. However, most implementations have overlooked the integration of available cancer guidelines, such as the NCCN Bone Cancer Guidelines, in fine-tuning the outputs of generative AI models. Incorporating these guidelines into LLMs presents an opportunity to harness the extensive clinical knowledge they contain and improve the decision-support capabilities of the model. Methods: In this study, the aim is to enhance the LLM with cancer clinical guidelines to enable accurate medical decisions and personalized treatment recommendations. Therefore, we introduce a novel method for incorporating the NCCN Bone Cancer Guidelines into LLMs using a Binary Decision Tree (BDT) approach. The approach involves constructing a BDT based on NCCN Bone Cancer Guidelines, where internal nodes represent decision points from the Guidelines, and leaf node signify final treatment suggestions. Then the LLM makes decision at each internal node, considering a given patient's characteristics, and guides toward a treatment recommendation in the leaf node. To assess the efficacy of Guideline-enhanced LLMs, an oncologist from our team created 11 hypothetical osteosarcoma patients’ medical progress notes. Each note contains their demographics, medical history, current illness, physical exams, diagnostic tests. We tested three LLMs in the implementation (GPT-4, GPT-3.5, and PaLM 2) and compared the LLM-generated treatment recommendations with the gold standard treatment across four runs with different random seeds (random seeds is a setting to control the LLM outputs). The results are reported as the average of four runs. The original LLMs are used as baseline methods for comparison. Results: The table below provides a comparison between the performance of original LLMs and those augmented with cancer guidelines for osteosarcoma treatment recommendations. We can observe that the PaLM 2 model demonstrated superior performance compared to its counterparts, underscoring the effectiveness of integrating cancer guidelines into LLMs for decision support. Conclusions: The clinical decision support capabilities of the LLMs are promising when enhanced by NCCN Bone Cancer Guidelines using our approach. To fully exhibit the potential of our proposed method as a clinical decision support tool, further investigation into other subtypes of bone cancer should be conducted in the future study. [Table: see text]

Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems

PurposeThis study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.Design/methodology/approachA systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.FindingsThe findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.Originality/valueThis study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

Assessing and forecasting collective urban heat exposure with smart city digital twins

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.

An optimized diabetes mellitus detection model for improved prediction of accuracy and clinical decision-making

Diabetes Mellitus (DM) is an enduring metabolic illness that disturbs many individuals globally. This study addresses the global impact of Diabetes Mellitus (DM) and emphasizes the critical role of accurate DM detection in early diagnosis, effective treatment, and prevention of complications. The research introduces an optimized DM detection model, the GBM-DRU (Gradient Boosting Machine - Data Reduction Unit) network, which integrates feature engineering and ensemble learning techniques to enhance prediction accuracy and support clinical decision-making. The GBM-DRU network combines the powerful gradient boosting machine algorithm with a data reduction unit (DRU) for efficient feature selection, reducing dimensionality and improving computational efficiency. Feature engineering enhances discriminatory power, while ensemble learning methods, including bagging and boosting, improve overall model performance. Rigorous experiments on a comprehensive dataset of DM patients demonstrate that the proposed approach outperforms existing models in terms of accuracy, sensitivity, specificity, and AUC-ROC. The optimized model provides valuable insights into feature importance, aiding clinical decision-making and deepening the understanding of DM risk factors. Therefore, the GBM-DRU network, utilizing feature engineering and ensemble learning, presents a viable approach to precise diagnosis of diabetes mellitus, with favorable implications for patient outcomes, disease control, and public health campaigns. The improved prediction accuracy, feature interpretability, and clinical decision support capabilities of the model may have a beneficial effect on public health campaigns, disease management, and patient outcomes.

Assessment of ChatGPT-3.5's Knowledge in Oncology: Comparative Study with ASCO-SEP Benchmarks.

ChatGPT (Open AI) is a state-of-the-art large language model that uses artificial intelligence (AI) to address questions across diverse topics. The American Society of Clinical Oncology Self-Evaluation Program (ASCO-SEP) created a comprehensive educational program to help physicians keep up to date with the many rapid advances in the field. The question bank consists of multiple choice questions addressing the many facets of cancer care, including diagnosis, treatment, and supportive care. As ChatGPT applications rapidly expand, it becomes vital to ascertain if the knowledge of ChatGPT-3.5 matches the established standards that oncologists are recommended to follow. This study aims to evaluate whether ChatGPT-3.5's knowledge aligns with the established benchmarks that oncologists are expected to adhere to. This will furnish us with a deeper understanding of the potential applications of this tool as a support for clinical decision-making. We conducted a systematic assessment of the performance of ChatGPT-3.5 on the ASCO-SEP, the leading educational and assessment tool for medical oncologists in training and practice. Over 1000 multiple choice questions covering the spectrum of cancer care were extracted. Questions were categorized by cancer type or discipline, with subcategorization as treatment, diagnosis, or other. Answers were scored as correct if ChatGPT-3.5 selected the answer as defined by ASCO-SEP. Overall, ChatGPT-3.5 achieved a score of 56.1% (583/1040) for the correct answers provided. The program demonstrated varying levels of accuracy across cancer types or disciplines. The highest accuracy was observed in questions related to developmental therapeutics (8/10; 80% correct), while the lowest accuracy was observed in questions related to gastrointestinal cancer (102/209; 48.8% correct). There was no significant difference in the program's performance across the predefined subcategories of diagnosis, treatment, and other (P=.16, which is greater than .05). This study evaluated ChatGPT-3.5's oncology knowledge using the ASCO-SEP, aiming to address uncertainties regarding AI tools like ChatGPT in clinical decision-making. Our findings suggest that while ChatGPT-3.5 offers a hopeful outlook for AI in oncology, its present performance in ASCO-SEP tests necessitates further refinement to reach the requisite competency levels. Future assessments could explore ChatGPT's clinical decision support capabilities with real-world clinical scenarios, its ease of integration into medical workflows, and its potential to foster interdisciplinary collaboration and patient engagement in health care settings.

Data Warehousing Revolution: AI-driven Solutions

The contemporary landscape of data warehousing is undergoing a revolutionary transformation propelled by the integration of Artificial Intelligence (AI). This paper explores the intersection of AI and data warehousing, unraveling the dynamics that fuel this revolution and examining the profound implications for businesses and data management practices. The traditional paradigm of data warehousing has relied on structured data and predefined schemas, limiting its adaptability to the dynamic nature of modern datasets. The advent of AI injects a new dimension, enabling data warehouses to evolve into intelligent, adaptive entities capable of handling diverse data types, volumes, and velocities. This abstract encapsulates the essence of the research, delving into key themes that define the AI-driven revolution in data warehousing. The paper begins by surveying the historical trajectory of data warehousing, highlighting the challenges posed by the increasing complexity and heterogeneity of contemporary data sources. As businesses grapple with unstructured data, streaming data, and the need for real-time insights, the limitations of traditional data warehousing architectures become apparent. The introduction of AI-driven solutions revolutionizes data warehousing in several dimensions. Machine learning algorithms are harnessed for automating data integration, cleansing, and transformation processes, mitigating the manual labor associated with traditional ETL (Extract, Transform, Load) methods. Deep learning techniques, such as neural networks, unlock the potential to uncover complex patterns within massive datasets, enhancing predictive analytics and decision support capabilities. Moreover, the abstract explores the role of AI in enabling self-optimizing data warehouses. Adaptive query optimization, automated indexing, and real-time performance tuning emerge as pivotal components, ensuring that data warehouses evolve in response to changing workloads and user patterns. Ethical considerations and responsible AI practices within the context of data warehousing are also addressed. The abstract concludes by underlining the transformative impact of AI-driven solutions on the efficiency, agility, and strategic value of data warehousing, offering a glimpse into the future where intelligent data warehouses play a central role in shaping data-driven enterprises.

Exploring Theoretical Foundations of Activity-Based Costing

This paper aims to explore the theoretical foundations of Activity-Based Costing (ABC) by examining its underlying assumptions, key concepts, methodology, applications and future directions. ABC emerged as an innovative approach to address limitations in traditional costing methods by assigning overhead costs based on activities and cost drivers. The paper at first discusses ABC’s assumption that products consume activities, and activities consume resources. Key concepts of activities, cost drivers and cost pools are then defined. The methodology of how ABC traces costs from activities to cost objects is outlined. Applications across industries are reviewed and benefits of more accurate product costing is discussed. Challenges in implementation like extensive data requirements are acknowledged. Emerging trends integrating ABC with technologies like real-time costing, analytics and artificial intelligence are also examined. The paper argues that understanding ABC’s theoretical foundations is essential for effective organizational adoption and optimization of its strategic decision-support capabilities. Continuous refinement will strengthen ABC’s relevance in the evolving business landscape.

Analysis of Enterprise Accounting Data Management Evaluation Model Based on Maturity Modeling

Abstract This paper takes the capability of accounting data management in Company D as the research object and constructs a maturity model of accounting data management. The idea of the maturity model’s gradient is used to divide key process domains and practices and then create a set of accounting data management index systems. Meanwhile, the fuzzy element evaluation and analysis method is applied to the evaluation of accounting data management maturity of enterprises, and “maturity level, evaluation index, and quantitative value” are defined as the basic elements of fuzzy elements. Finally, based on the evaluation results, the evaluation indicators at all levels of the accounting data management maturity model of Company D are analyzed. The results show that the evaluation value of the cost management capability and decision support capability of Company D is 3.1678 and 2.9518, which can ensure the authenticity, timeliness and reliability of accounting data to a certain extent. This paper provides new ideas for the research of enterprise accounting management evaluation, which has certain theoretical significance and practical value.

Application of GIS in Land Policy and Planning Strategies in Rural Revitalization

In recent years, there has been a growing emphasis on reviving rural areas in China, which has become a crucial strategic direction for the nation's economic and social development. Currently, rural revitalization is a major endeavor in China, and the successful implementation of land policy and planning strategies is crucial to its achievement. This study seeks to examine the use of Geographic Information System (GIS) in crafting land policy and planning strategies for rural rejuvenation. By harnessing GIS technology, a thorough evaluation and analysis of rural land resources can be undertaken, furnishing a scientific basis for formulating land policy. Furthermore, Geographical Information Systems (GIS) provide opportunities for spatial analysis, simulation, and decision support capabilities to implement rural land policy and planning strategies. Nevertheless, the use of GIS technology presents challenges and limitations in terms of data acquisition and technological implementation. To promote the sustainable development of the rural economy, future research efforts should concentrate on further enhancing the utilization of GIS technology in rural revitalization.

Development of a watershed health assessment framework integrating ecological, social, cultural, economic and policy attributes

ABSTRACT Watershed health assessment is vital for effective resource management, necessitating an integrated framework that captures the multidimensional nature of watershed health and guides protection and restoration efforts. This study presents the development of the Integrated Watershed Health Assessment Framework (IWHF), incorporating ecological, social, cultural, economic and policy attributes to comprehensively assess watershed health as a socio-ecological system. By integrating primary and secondary data, including literature reviews and expert opinions, the IWHF captures the multidimensional nature of watershed health and provides a practical tool for sustainable watershed management. The results, evaluated through the Analytic Hierarchy Process (AHP) within the context of Multi-Criteria Decision Making (MCDM), prioritize ecological factors (48.80%), followed by economic considerations (21.90%), socio-cultural factors (16.91%) and policy aspects (12.39%). This highlights the critical importance of preserving ecological integrity, emphasizing sustainable economic practices, inclusive community engagement and well-defined policy frameworks. The IWHF's holistic and flexible approach, comprehensive integration, and enhanced decision support capabilities set it apart from other frameworks, contributing to the understanding, management, and preservation of watershed health while guiding policy direction and optimizing the utilization of watershed resources.

Decision-support system for safety and security assessment and management in smart cities

Counter-terrorism measures and preparedness play a critical role in securing mass gatherings, soft targets, and critical infrastructures within urban environments. This paper introduces a comprehensive Decision Support System developed as part of the S4AllCitites project, designed to seamlessly integrate with existing legacy systems in Smart Cities. The system encompasses urban pedestrian and vehicular evacuation, incorporating predictive models to anticipate the progression of incendiary and mass shooting attacks, alongside a probabilistic model for threat assessment in the case of improvised explosive devices. A notable achievement of this research is the successful implementation and deployment of the system in operational environments through pilot studies. It empowers public and private security operators with real time decision support capabilities during both prevention and intervention stages of potential attacks. The decision support information provided encompasses various aspects, including optimal evacuation strategies, estimated egress times, pedestrian movement profiles, probability assessments, and the potential impact of different terrorist threats in terms of casualties. Additionally, the system offers real-time insights into the status of the traffic network under normal and unusual conditions, enabling efficient emergency management throughout its progression. This includes the ability to identify optimal intervention routes and assess the impact of anomalous traffic resulting from evacuations.

METHOD FOR DETERMINING THE RATIONAL NUMBER OF UAV FLOTILLA TAKING INTO ACCOUNT THE RELIABILITY OF THE AIRCRAFT

The conducted studies showed that projects of analysis and assessment of emergencies at critical infrastructure facilities have been initiated in Ukraine and abroad. The purpose of such a system is the formation of data for the development of architecture, demonstration analytics and a prototype of decision support capabilities, taking into account the use of currently available data and analytical methodologies. Unmanned aerial vehicles (UAVs), which work with ground and air control points in emergencies, are planned as the basis for the construction of such a system. The subject of the study is methods of determining the rational number of UAV flotillas taking into account the assigned tasks and characteristics. Currently, there is no method for determining the rational number of a UAV flotilla, taking into account the reliability of the aircraft. An urgent scientific and technical task is the task of creating a rational number of the UAV flotilla, taking into account the reliability of the aircraft and the necessary quality of monitoring the situation in emergencies. The purpose of the article is to develop a method for determining the rational number of UAV flotillas taking into account the reliability of the aircraft and the necessary quality of monitoring the situation in emergencies. Research methods – provisions of risk theory, probability theory, combinatorics, mathematical apparatus of reliability theory, mathematical methods of optimization. Research results: an analysis of factors affecting the structure of the UAV grouping was carried out; the dependence of task performance on the probability of UAV failure under the influence of interfering factors was obtained; it is shown that in conditions with a low impact of interfering factors, the reliability of individual UAVs does not have a significant impact on the performance of assigned tasks. It has been established that with increasing influence of interfering factors, the probability of completing tasks depends on the reliability of the aircraft. Conclusions: The proposed method makes it possible to create a rational number of UAV flotillas taking into account the reliability of the aircraft and the necessary quality of monitoring the situation with a rational number of UAVs in emergencies.

Effect of Management Information System (MIS) on Decision-Making in the Academic Sector

The current era is the information era; people are running on society 5.0, where machines are integrated with humans for a healthy, comfortable, easy, and long life. In general, information is blood, and MIS is body; blood flows for life which can be compared with the flow of information on the organization as per the requirement for decision-making to grow the organization to meet its desired outcome. The overall objective of this research is to assess the effect of MIS on decision-making among academic institutions. This machine can function effectively and efficiently only with the help of data and information for humanitarian assistance for various purposes. Nowadays, every organization cannot run smoothly without the use of MIS. It signifies the dependability of decision-making on MIS. This research has attempted to identify necessary variables. They are used to evaluate the influence of management information systems on decision-support capabilities in the organization.
 This study also discusses the concept, attributes, characteristics, types of MIS, and the MIS model, and finally, highlights the effect of MIS in decision-making in academic institutions. At the same time, different models and figures are presented to enrich the discussion and to highlight the status of each MIS and DSS information system in an organization's decision-making process. Here fifteen related articles are incorporated to review the title to ensure the research gap and where conceptual review approach based on descriptive content analysis using long-term classroom discussion. This research will create an awareness to develop an integrated MIS among academic institutions to ensure success through rational scientific decision-making.

1743. Antimicrobial and Diagnostic Stewardship Targeting Urinary Tract Infections in the Emergency Department: A Pharmacist-driven Quality Improvement Project at a Tertiary Military Treatment Facility

Abstract Background With the increasing interest in ambulatory stewardship, we designed a quality improvement project to enhance guideline concordant antimicrobial and diagnostic stewardship practices targeting urinary tract infections (UTI) in the ED of a Military Treatment Facility. Between two data collection phases (P1, P2), we provided education on national guidelines, local protocols and antibiograms, modified order-sets, and implemented enhancements targeting urine collection. The primary outcome was a change in guideline-concordant practices between P1 and P2. Methods This prospective pharmacist-driven study had two 3-month data collection periods including ED encounters that resulted in a urinalysis (UA), urine culture (UCx) and antibiotics for UTI upon discharge. We collected data on participant demographics, clinical course and therapy details via chart review. Culture callback and corresponding interventions were made within 72 hours. Based on P1 results, we broadened our scope in P2 to include a diagnostic stewardship arm (Figure 1). This project was IRB approved under a non-research determination. Results There were no significant changes in guideline-concordant prescribing or diagnostic practices between P1 and P2 (Figure 2). Inappropriate antibiotic selection was primarily due to use of cephalosporins and FQs as first line agents (Table 1). The primary pharmacy intervention was discontinuation of therapy (P1 29.1%, P2 39.8%). One-third of patients receiving antibiotics were asymptomatic, a majority of which had a positive UA (P1 68.4%, P2 73.3%). There was a high rate of UCx contamination which improved in P3 but did not reach statistical significance (P1 25.6%, P2 17.2%, p=0.19). Across all P2 encounters, 51.4% of UCx were deemed inappropriate. Conclusion Significant guideline-discordant prescribing persisted despite interventions. An overreliance on UA findings results in treatment of asymptomatic bacteriuria. Education and order-set modification alone are insufficient for a lasting impact on prescribing practices especially where there is a high rate of provider turnover. An EMR with embedded clinical and alert-based decision support capabilities are likely to make a sustained impact in curbing inappropriate treatment of asymptomatic bacteriuria. Disclosures All Authors: No reported disclosures.

Examining the Evolution of Management Accounting: A Qualitative Review

This qualitative review investigates the evolution of management accounting, focusing on its historical development, theoretical frameworks, and contemporary practices. The research aims to provide insights into the adaptive nature of management accounting in response to changing organizational needs, technological advancements, and societal trends. The methodology involves a comprehensive literature review encompassing historical analysis, theoretical perspectives, and empirical studies in the field of management accounting. Key findings reveal the historical roots of management accounting tracing back to the Industrial Revolution, with significant milestones including the emergence of cost accounting techniques, budgeting systems, and advanced methodologies like activity-based costing and strategic management accounting. The review also identifies theoretical frameworks such as agency theory, contingency theory, and institutional theory, offering diverse perspectives on the role and functioning of management accounting within organizations. Contemporary practices in management accounting emphasize strategic analysis, risk management, performance measurement, and the integration of financial and non-financial metrics. Technological advancements, particularly in information technology, have revolutionized decision support capabilities, enabling real-time monitoring and analysis of organizational performance. The synthesis of literature underscores the dynamic and multifaceted nature of management accounting, highlighting its pivotal role in facilitating strategic decision-making, enhancing organizational performance, and driving sustainable growth. The findings contribute to a deeper understanding of management accounting evolution and offer implications for practice and future research.