Data Visualization Platform Research Articles

RESEARCH ON THE STANDARDIZATION AND VISUALIZATION PLATFORM FOR MONITORING AND TESTING DATA OF METRO CIVIL FACILITIES BASED ON BIG DATA

Introduction: With consideration for extensive standardized detection and monitoring data, multi-source data fusion analysis and comparison were implemented at cross-project and inter-regional levels to provide robust data support for the full life-cycle management of metro civil facilities. The purpose of the study was to integrate detection and monitoring data on metro bridges and tunnel structures and address the significant data isolation issue in existing systems and various units. Based on big data and Internet of Things technology, this paper investigates the standardized format of uploading, storage, processing, sharing, and other types of managing detection and monitoring data on metro bridges and tunnel structures. Additionally, a third-party data standardization and visualization platform for bridges and tunnel structures was developed, ensuring integration of detection and monitoring data fusion. In the course of the study, the following methods were used: theoretical analysis and software system development. As a result, the practicability and feasibility of the platform were verified through practical applications.

An open-source data storage and visualization platform for collaborative qubit control.

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization (QubiCSV), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

SYSTEMATIC REVIEW OF BUSINESS INTELLIGENCE AND ANALYTICS CAPABILITIES IN HEALTHCARE USING PRISMA

This systematic review explores the role of Business Intelligence and Analytics (BI&A) in healthcare, focusing on its applications, benefits, and challenges. Using the PRISMA framework, a total of 52 peer-reviewed studies published between 2010 and 2023 were analyzed to identify key BI&A tools, such as Clinical Decision Support Systems (CDSS), predictive analytics, and data visualization platforms, and their impact on healthcare outcomes. The findings show that BI&A significantly enhances clinical decision-making, improves patient outcomes, and optimizes operational efficiency, with 19 studies highlighting the effectiveness of CDSS and 16 studies demonstrating the value of predictive analytics in reducing patient readmissions and improving early disease detection. However, challenges such as data integration issues, privacy concerns, and resistance to technology adoption were evident across 25 studies, limiting the broader adoption of BI&A. The review also identifies gaps in the literature, particularly the need for more longitudinal studies and research on emerging technologies like artificial intelligence (AI) and machine learning (ML). Future research should focus on addressing these challenges to fully unlock the potential of BI&A in healthcare.

ImageGP 2 for enhanced data visualization and reproducible analysis in biomedical research.

ImageGP is an extensively utilized, open-access platform for online data visualization and analysis. Over the past 7 years, it has catered to more than 700,000 usages globally, garnering substantial user feedback. The updated version, ImageGP 2 (available at https://www.bic.ac.cn/BIC), introduces a redesigned interface leveraging cutting-edge web technologies to enhance functionality and user interaction. Key enhancements include the following: (i) Addition of modules for data format transformation, facilitating operations such as matrix merging, subsetting, and transformation between long and wide formats. (ii) Streamlined workflows with features like preparameter selection data validation and grouping of parameters with similar attributes. (iii) Expanded repertoire of visualization functions and analysis tools, including Weighted Gene Co-Expression Network Analysis, differential gene expression analysis, and FASTA sequence processing. (iv) Personalized user space for uploading large data sets, tracking analysis history, and sharing reproducible analysis data, scripts, and results. (v) Enhanced user support through a simplified error debugging feature accessible with a single click. (vi) Introduction of an R package, ImageGP, enabling local data visualization and analysis. These updates position ImageGP 2 as a versatile tool serving both wet-lab and dry-lab researchers with expanded capabilities.

Enhancing greenhouse strawberry irrigation: integrating IoT technologies and low-cost moisture sensors in substrate

The integration of Internet of Things (IoT) technologies, particularly sensor-based systems, is revolutionizing agriculture, including strawberry cultivation in greenhouses. Furthermore, the use of wireless communication technologies, alongside efficient data storage and visualization platforms, can enhance monitoring and decision-making capabilities. This study aimed to calibrate and verify the real-time operation of the low-cost SKU SEN0193 V2.0 sensor and the SHT20 in strawberry cultivation substrate, evaluating IoT technologies for data transmission, including LoRa, InfluxDB for storage, and Grafana for visualization and monitoring. The adaptability and accuracy of the SHT20 and SKU SEN0193 V2.0 sensors were tested in laboratory and greenhouse strawberry crops. Water retention curves in the substrate were determined using the Haines Funnel, while recording sensor measurements simultaneously for calibration. For real-time monitoring, sensors were placed in two slabs with strawberry plants in a greenhouse. InfluxDB stored the data, and Grafana visualized it. While the SHT20 sensor showed limited suitability for substrates due to its lack of response to moisture variations, SKU SEN0193 V2.0 produced excellent results, detecting changes in moisture content in both laboratory and greenhouse tests. Sensor-specific calibration for greenhouse slabs is crucial. The utilization of data storage and visualization tools like InfluxDB and Grafana facilitated real-time data manipulation, demonstrating their potential for enhancing irrigation management in greenhouse strawberry production.

XR-based interactive visualization platform for real-time exploring dynamic earth science data

The transition from 2D planar displays to immersive holographic 3D environments has brought advancements in visualization technology. However, there remains a lack of effective interactive visualization tools for complex multi-dimensional structured or unstructured datasets in immersive space. To address this gap, we have developed MetIVA, a state-of-the-art multiscale interactive data visualization platform that leverages the Extended Reality (XR) and cloud rendering technology for immersive data exploration. In this paper, we firstly outline the historical development of scientific visualization and the recent shift towards 3D and higher-dimensional visualization, and then basically introduce the conceptual framework and platform structure of MetIVA, and finally present the evaluation results from recruited potential users. The results confirm that MetIVA is a powerful tool to accelerate data exploration and decision-making processes. Its interactive and intuitive features, along with ongoing optimization efforts, make it a valuable tool for researchers and practitioners in the field of Earth science.

Developing a smart forest protection mini program with Spring Boot: Integrating big data and image recognition technologies

Forests are essential for ecological stability yet pose significant management challenges due to their expansive and remote nature. Despite advancements in data acquisition, there is a lack of tools that effectively combine image recognition with big data analytics for real-time monitoring. This study introduces a "Smart Forest Protection" mini program, developed using Spring Boot, which integrates these technologies to facilitate immediate and efficient forest management. The program features intelligent plant recognition, user-driven incident reporting, waste recycling point displays, and a platform for disseminating forest conservation knowledge. Additionally, a big data visualization platform illustrates the spatiotemporal distribution of forests in Zhuhai, aiding governmental and environmental agencies in making informed decisions that enhance forest conservation and promote sustainable practices.

Live Data Monitoring in Industry

Abstract: In contemporary industrial environments, real-time monitoring of critical parameters is vital for ensuring safety, efficiency, and compliance with regulations. This thesis details the development and implementation of an advanced live data monitoring system designed to track temperature, humidity, fire presence, and oxygen levels. Key components of the system include a DHT sensor for temperature and humidity measurement, a fire sensor for fire detection, an oxygen sensor for monitoring atmospheric oxygen, and a buzzer for immediate auditory fire alerts. Central to the system is the ESP8266 WiFi module, which facilitates the wireless transmission of sensor data to ThingSpeak, a cloud-based data collection and visualization platform. This setup allows for continuous, remote monitoring of environmental conditions, providing critical insights and alerts to industrial operators. The project delves into the hardware configuration, sensor calibration, data acquisition processes, and wireless communication protocols essential for effective real-time monitoring. It also addresses the challenges of integrating multiple sensors and ensuring reliable data transmission in an industrial setting. The results demonstrate the system's capability to provide accurate, timely data, enhancing safety protocols and operational efficiency. By utilizing IoT technology, the proposed system offers a scalable solution for industrial environments aiming to implement advanced monitoring and alert systems. This work contributes to the field of industrial automation by illustrating a practical application of IoT in improving monitoring and safety measures, highlighting both the technical achievements and practical implications of the implemented system.

Enhancing Performance Metrics: A Google Looker Studio Approach to Key Performance Indicator (KPI) Management System for Homecorp Offshore Drafting Team

The Homecorp Offshore Drafting Key Performance Indicator (KPI) Management System is a comprehensive process that aims to improve the management and monitoring of key performance indicators within the drafting department. This system utilizes Google Looker Studio, which provides a user-friendly real-time data analysis and visualization platform. It enables managers to make well-informed decisions and enhance departmental performance. The process begins with collecting daily task data, logged manually or through automated Google Apps Scripts into a Google Sheets database. This data includes task details such as type, status, and completion time. A KPI-pointing system, developed through focus group discussions, also assigns point values to tasks based on their complexity and time requirements. Integrating Google Looker Studio with Google Sheets allows real-time synchronization and web-based monitoring of key performance indicators (KPIs). The dashboard offers detailed insights such as task counts, average KPIs, and monthly performance graphs, providing a comprehensive performance evaluation. The implementation of this system has been well-received by the team, with survey results indicating improvements in tracking performance metrics, ease of use, and enhanced collaboration. While these outcomes are positive, there is still a recognized need for further refinement, particularly in incorporating qualitative data and improving IT support. The KPI Management System has significantly enhanced performance monitoring and management for the drafting team. To keep the system up-to-date and effective, ongoing feedback and iterative improvements must be maintained. Potential future enhancements could include extending the system's use company-wide to foster a culture of continuous improvement and accountability.

AI-Enhanced Data Visualization: Transforming Complex Data into Actionable Insights

Purpose: The purpose of this study is to explore how artificial intelligence (AI) becomes a part of data visualization. Thus, data from complex datasets are transformed into dynamic, interactive, and personalized visual experiences that will help in deeper insights and actionable knowledge. The research is supposed to design a holistic system and rules for using AI to make data visualization more effective and super interactive for the users. Methodology: The methodology involves the in-depth examination of artificial intelligence-based data visualization tools and platforms by using case studies. The study analyses the impact of AI technologies such as machine learning, natural language processing, and augmented and virtual reality on the scalability, interactivity, and personalization of data visualizations. The sentence also talks about the analysis of the moral factors that are part of the process of introducing AI in data visualization. Findings: The findings indicate that AI greatly improves the process and the quality of data visualization, thus, it makes possible the management of big, complicated, multi-dimensional datasets in a more efficient and precise way. The AI-driven tools give the users the opportunity to see the actions that are happening in real-time, predict the results, and personalize the tools according to their individual needs, thereby increasing the decision-making processes. Furthermore, ethical issues like data privacy, bias, and transparency must be well managed. This research has the distinctive feature of providing a theoretical framework that emphasizes the importance of AI in the development of data visualization technologies. Unique contribution to theory, policy and practice: In practice, it gives the rules for the implementation of AI tools to achieve more effective and user-focused visualizations. The policy focuses on the necessity of ethical standards in AI deployments, which means the data visualization practices should be transparent, accountable, and bias-free, thus creating trust and reliability in the AI applications.

Novel data visualization and risk analytic algorithm in a low-middle income country.

To discuss the use of T3™, a data aggregation, visualization, and risk analytic platform in a single centre and its framework for implementation of such a tool in clinical care. We share experience of a tool implemented in a tertiary care Intensive Care Unit (ICU) with limited resources. Superusers were identified and trained. Implementation involved monitoring, evaluation, and user engagement data for continuous emphasis on the use of this tool. Persistent display of T3 data enhanced nursing operational efficiency. Its use was expanded to use in nurses rounds and handover, mortality and morbidity meetings, clinical team teaching through selected teaching cases and analysis of stored data with different research questions. However, lack of infrastructure and technological comprehension, paucity of multidisciplinary teams makes it a challenge in its implementation. Clear framework of implantation and pre-designed studies to determine the clinical usage and effectiveness are important for wide-spread use of such tools.

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Bridging the clinical-research gap: Harnessing an electronic data capture, integration, and visualization platform to systematically assess prospective patient-reported outcomes in mitochondrial medicine

PurposeOptimizing individualized clinical care in heterogeneous rare disorders, such as primary mitochondrial disease (PMD), will require gaining more comprehensive and objective understanding of the patient experience by longitudinally tracking quantifiable patient-specific outcomes and integrating subjective data with clinical data to monitor disease progression and targeted therapeutic effects. MethodsElectronic surveys of patient (and caregiver) reported outcome (PRO) measures were administered in REDCap within clinical domains commonly impaired in patients with PMD in the context of their ongoing routine care, including quality of life, fatigue, and functional performance. Descriptive statistics, group comparisons, and inter-measure correlations were used to evaluate system feasibility, utility of PRO results, and consistency across outcome measure domains. Real-time tracking and visualization of longitudinal individual-level and cohort-level data were facilitated by a customized data integration and visualization system, MMFP-Tableau. ResultsAn efficient PRO electronic capture and analysis system was successfully implemented within a clinically and genetically heterogeneous rare disease clinical population spanning all ages. Preliminary data analyses demonstrated the flexibility of this approach for a range of PROs, as well as the value of selected PRO scales to objectively capture qualitative functional impairment in four key clinical domains. High inter-measure reliability and correlation were observed. Between-group analyses revealed that adults with PMD reported significantly worse quality of life and greater fatigue than did affected children, while PMD patients with nuclear gene disorders reported lower functioning relative to those with an mtDNA gene disorder in several clinical domains. ConclusionIncorporation of routine electronic data collection, integration, visualization, and analysis of relevant PROs for rare disease patients seen in the clinical setting was demonstrated to be feasible, providing prospective and quantitative data on key clinical domains relevant to the patient experience. Further work is needed to validate specific PROs in diverse PMD patients and cohorts, and to formally evaluate the clinical impact and utility of harnessing integrated data systems to objectively track and integrate quantifiable PROs in the context of rare disease patient clinical care.

Assessing a Digital Scorecard on Global Immunization Progress: Stakeholder Views and Implications for Enhancing Performance and Accountability.

Global health agencies and regional and national stakeholders collaborated to develop the Immunization Agenda 2030 Scorecard, a digital data visualization platform displaying global, regional, and country-level immunization progress. The scorecard serves to focus attention and enable strategic actions around the measures visualized. To assess the scorecard's usability, appropriateness, and context for use, we interviewed 15 immunization officers working across five global regions. To further understand the implementation context, we also reviewed the characteristics of 15 public platforms visualizing population health data. We integrated thematic findings across both methods. Many platforms highlight service gaps and enable comparisons between geographies to foster political pressure for service improvements. We observed heterogeneity regarding the platforms' focus areas and participants' leading concerns, which were management capacity and resourcing. Furthermore, one-third of platforms were out of date. Results yielded recommendations for the scorecard, which participants felt was well suited to focus the attention of decision makers on key immunization data. A simpler design coupled with implementation strategies that more actively engage policymakers would better align the scorecard with other public platforms engaging intended users. For population health platforms to serve as effective accountability mechanisms, studying implementation determinants, including usability testing, is vital to meet stakeholder needs.

Exploring the Perspectives of Patients Living With Lupus: Retrospective Social Listening Study.

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease affecting various organs with a wide range of clinical manifestations. Cutaneous lupus erythematosus (CLE) can manifest as a feature of SLE or an independent skin ailment. Health-related quality of life (HRQoL) is frequently compromised in individuals living with lupus. Understanding patients' perspectives when living with a disease is crucial for effectively meeting their unmet needs. Social listening is a promising new method that can provide insights into the experiences of patients living with their disease (lupus) and leverage these insights to inform drug development strategies for addressing their unmet needs. The objective of this study is to explore the experience of patients living with SLE and CLE, including their disease and treatment experiences, HRQoL, and unmet needs, as discussed in web-based social media platforms such as blogs and forums. A retrospective exploratory social listening study was conducted across 13 publicly available English-language social media platforms from October 2019 to January 2022. Data were processed using natural language processing and knowledge graph tagging technology to clean, format, anonymize, and annotate them algorithmically before feeding them to Pharos, a Semalytix proprietary data visualization and analysis platform, for further analysis. Pharos was used to generate descriptive data statistics, providing insights into the magnitude of individual patient experience variables, their differences in the magnitude of variables, and the associations between algorithmically tagged variables. A total of 45,554 posts from 3834 individuals who were algorithmically identified as patients with lupus were included in this study. Among them, 1925 (authoring 5636 posts) and 106 (authoring 243 posts) patients were identified as having SLE and CLE, respectively. Patients frequently mentioned various symptoms in relation to SLE and CLE including pain, fatigue, and rashes; pain and fatigue were identified as the main drivers of HRQoL impairment. The most affected aspects of HRQoL included "mobility," "cognitive capabilities," "recreation and leisure," and "sleep and rest." Existing pharmacological interventions poorly managed the most burdensome symptoms of lupus. Conversely, nonpharmacological treatments, such as exercise and meditation, were frequently associated with HRQoL improvement. Patients with lupus reported a complex interplay of symptoms and HRQoL aspects that negatively influenced one another. This study demonstrates that social listening is an effective method to gather insights into patients' experiences, preferences, and unmet needs, which can be considered during the drug development process to develop effective therapies and improve disease management.

The Development of a Clinical Registry Digital Database on Invasive Fungal Infections in India: Advancing Epidemiological Understanding and Patient Care.

A well-structured digital database is essential for any national priority project as it can provide real-time data analysis and facilitate quick decision making. In recent times, particularly after the COVID-19 pandemic, invasive fungal infections (IFIs) have emerged as a significant public health challenge in India, affecting vulnerable population, including immunocompromised individuals. The lack of comprehensive and well-structured data on IFIs has hindered efforts to understand their true burden and optimize patient care. To address this critical knowledge gap, the ICMR has undertaken a Pan-India pioneer initiative to develop a network of Advanced Mycology Diagnostic research centres in different geographical zones of the country (ICMR-MycoNet). Under the aegis of this project, a clinical registry on IFIs in the ICUs is initiated. This process paper presents a detailed account of the steps involved in the establishment of a web-based data entering and monitoring platform to capture data electronically, ensuring robust and secure data collection and management. This system not only allows participating ICMR-MycoNet centres to enter patient information directly into the database using standardized Case Report Form (CRF) but also includes data validation checks to ensure the accuracy and completeness of entered data. It is complemented by a real-time, web-based, and adaptable data visualization platform. This registry aims to provide crucial epidemiological insights, promote evidence-based hospital infection control programs, and ultimately improve patient outcomes in the face of this formidable healthcare challenge.

Triboelectric gait sensing analysis system for self‐powered IoT‐based human motion monitoring

AbstractQuantitative analysis of gait parameters, such as stride frequency and step speed, is essential for optimizing physical exercise for the human body. However, the current electronic sensors used in human motion monitoring remain constrained by factors such as battery life and accuracy. This study developed a self‐powered gait analysis system (SGAS) based on a triboelectric nanogenerator (TENG) fabricated electrospun composite nanofibers for motion monitoring and gait analysis for regulating exercise programs. The SGAS consists of a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. Within the sensing module, two specialized sensing units, TENG‐S1 and TENG‐S2, are positioned at the forefoot and heel to generate synchronized signals in tandem with the user's footsteps. These signals are instrumental for real‐time step count and step speed monitoring. The output of the two TENG units is significantly improved by systematically investigating and optimizing the electrospun composite nanofibers' composition, strength, and wear resistance. Additionally, a charge amplifier circuit is implemented to process the raw voltage signal, consequently bolstering the reliability of the sensing signal. This refined data is then ready for further reading and calculation by the micro‐controller unit (MCU) during the signal transmission process. Finally, the well‐conditioned signals are wirelessly transmitted to the IoT platform for data analysis, storage, and visualization, enhancing human motion monitoring.image

Rationale and Design of a Wearable Cardiopulmonary Monitoring System for Improving the Efficiency of Critical Care Monitoring

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent’s Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time.

Utilization of Pharmacologic and Non-Pharmacologic Therapies for Pain in Sickle Cell Disease: A Cross-Sectional Study

Background: The sickling deformity of red blood cells in sickle cell disease (SCD) results in multisystem sequelae, including severe, acute pain from vaso-occlusive crises (VOCs) and chronic pain from central sensitization. Pain is the main cause of poor quality of life and hospital admissions among patients with SCD. While opioid therapy is an important component of treatment for acute SCD pain, it can be complicated by side effects and toxicities. In contrast, chronic SCD pain is best managed with non-pharmacologic therapies. A multimodal, multidisciplinary approach to pain management that promotes non-pharmacologic therapies can improve the quality of care for patients with SCD by addressing biopsychosocial components of the pain experience and is recommended by the American Society of Hematology (ASH). This study examines current usage of pharmacologic and non-pharmacologic therapies among Adolescents and Young Adults (AYAs) with SCD receiving care at our institution. Methods: The electronic medical record and Sickle Cell Knowledgebase, a customized data visualization platform unique to our institution, were utilized to conduct a retrospective chart review of AYAs, ages 13-21 years of age, with SCD from 2016-2021. Data collected included demographic information, diagnosis of chronic pain, emergency department (ED) visits and inpatient admissions for VOCs, as well as pharmacologic and non-pharmacologic therapies utilized in the ED, inpatient setting, and, for patients with chronic pain, in the outpatient setting. Results: The cohort included 90 AYAs with SCD (73% were 13-17 years old). The most common SCD genotypes were HbSS (n=47, 52%) and HbSC (n=32, 36%). The majority were female (n=53, 49%), Black (n=85, 94%), and non-Hispanic (n=86, 96%). There were 95 ED visits for VOC, where the patient was subsequently discharged home, and 246 admissions for VOC during the study period. There was a high frequency of opioid interventions for VOCs during the 95 extracted ED visits (81.8% in 2016, 91.7% in 2021). During VOC admissions, there was consistent use of opioids in 100% of admissions from 2016-2021 with increasing utilization of patient-controlled analgesia (7.7% in 2016, 45.1% in 2021) and subanesthetic ketamine (0% in 2016, 18.3% in 2021) each year during the study period. Compared to pharmacologic measures, non-pharmacologic therapies (i.e., healing touch, massage, art therapy, child life, music therapy, occupational therapy, physical therapy, psychology) were less utilized during VOC admissions. While healing touch and child life therapy were ordered during approximately 85% of VOC admissions, they occurred <50% of the time. All other integrative therapies were ordered between 5-55% of admissions each year and occurred <50% of the time. There was no correlation between length of hospital stay and use of integrative therapies. Seven patients with a diagnosis of chronic pain were identified. All 7 received non-pharmacologic therapies in the outpatient setting, including cognitive behavioral therapy, guided imagery, and psychology consultation. Six received either a gabapentinoid, serotonin and norepinephrine reuptake inhibitor (SNRI), or tricyclic antidepressant (TCA). Conclusion: Per the ASH SCD guidelines, non-pharmacologic therapies are recommended for SCD-related pain. In our cohort, we found that non-pharmacologic therapies occurred in <50% of VOC admissions and were underutilized for SCD-related pain in AYAs when compared to pharmacologic therapies at our institution. Though we speculate that healing touch and child life were frequently ordered as a default within our admission order set, utilization rates were still low. To better understand barriers to non-pharmacologic therapies in SCD, we are conducting qualitative interviews of patients, caregivers, and healthcare providers using the Consolidated Framework for Implementation Research (CFIR) as a theoretical model. Understanding barriers to the use of non-pharmacologic therapies will be essential to maximizing their implementation and hence maximizing pain control, improving quality of life, and increasing self-efficacy.