Efficiency Of Data Collection Research Articles

Integration of statistical methods and neural networks for temperature regulation parameter optimization

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

Near-quantum-limited haloscope search for dark-photon dark matter enhanced by a high- Q superconducting cavity

We report new experimental results on the search for dark photons based on a near-quantum-limited haloscope equipped with a superconducting cavity. The loaded quality factor of the superconducting cavity is 6×105, so that the expected signal from dark-photon dark matter can be enhanced by more than one order compared to a copper cavity. A Josephson parametric amplifier with a near-quantum-limited noise temperature has been utilized to minimize the noise during the search. Furthermore, a digital acquisition card based on field programmable gate arrays has been utilized to maximize data collection efficiency with a duty cycle being 100%. This work has established the most stringent constraints on dark photons at around 26.965 μeV. In the future, our apparatus can be extended to search for other dark matter candidates, such as axions and axionlike particles, and scrutinize new physics beyond the Standard Model. Published by the American Physical Society 2024

Technology impact: A study on the performance appraisal process in the IT industry

This study investigates the impact of technology on the performance appraisal process within the IT industry. Rapid advancements in technology have transformed traditional performance appraisal methods, and understanding the implications of these changes is crucial for organizations striving to optimize their performance management strategies. The use of technology for performance appraisals in the IT industry can introduce several problems that can negatively impact the evaluation process and employee morale. Automated systems may rely on irrelevant or incomplete metrics that do not capture important qualitative aspects of performance. These systems may be prone to errors, biases, and inaccuracies in data collection and interpretation. The findings of this study reveal the significant impact of technology on the performance appraisal process in the IT industry. There are potential solutions for mitigating performance appraisal problems in the IT industry. To ensure that performance appraisal systems are relevant and accurate, organizations can involve employees in the development of metrics and provide regular feedback. The integration of digital tools has enhanced the efficiency and accuracy of data collection, tracking, and analysis, leading to streamlined processes, reduced administrative burdens, and increased data reliability. Real-time communication channels have facilitated more frequent and timely feedback, fostering employee engagement and development.

Leveraging ChatGPT for Enhanced Logical Analysis in the Theory of Constraints Thinking Process

Abstract Background/Purpose Artificial intelligence (AI) has traditionally been used for quantitative analysis using explicit data. However, much of the information critical to decision making remains undocumented and is not stored in a structured way. This study explores the integration of AI, specifically ChatGPT, into Theory of Constraints (TOC) Thinking Process (TP) tools. Method In this study, we applied ChatGPT to a real-world IT project management case using a variety of research methods, including international literature analysis, observation, and personal experience. The use of the TOC TP allowed us to understand the decision-making process of ChatGPT and to systematically explore its advantages and limitations in creating logical trees of TOC TP. Results ChatGPT significantly enhanced efficiency and depth in TOC TP data collection and analysis, effectively addressing logical leaps for more coherent structures. It also promoted deeper analytical thinking and aided root cause identification. The integration of ChatGPT into the TOC TP process led to faster decision-making, reduced bias, and clearer analysis. Challenges of ChatGPT including the need for human oversight, specific TOC TP training, and ethical considerations were noted. Conclusion This study provides an initial investigation into the use of ChatGPT in TOC TP tools. The results suggest that ChatGPT has the potential to be a valuable tool for organizations seeking to improve their decision making and performance. However, further research is needed to validate these findings and explore the full potential of AI in TOC TP.

High-Throughput Morphological Chirality Quantification of Twisted and Wrinkled Gold Nanorods.

Chirality in gold nanostructures offers an exciting opportunity to tune their differential optical response to left- and right-handed circularly polarized light, as well as their interactions with biomolecules and living matter. However, tuning and understanding such interactions demands quantification of the structural features that are responsible for the chiral behavior. Electron tomography (ET) enables structural characterization at the single-particle level and has been used to quantify the helicity of complex chiral nanorods. However, the technique is time-consuming and consequently lacks statistical value. To address this issue, we introduce herein a high-throughput methodology that combines images acquired by secondary electron-based electron beam-induced current (SEEBIC) with quantitative image analysis. As a result, the geometric chirality of hundreds of nanoparticles can be quantified in less than 1 h. When combining the drastic gain in data collection efficiency of SEEBIC with a limited number of ET data sets, a better understanding of how the chiral structure of individual chiral nanoparticles translates into the ensemble chiroptical response can be reached.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Machine learning for public wellness: optimizing hygiene practices and pollution monitoring in smart cities

Introduction. Public health in urban areas is of paramount importance, particularly in the context of smart cities where technology plays a vital role. The integration of sophisticated infrastructure and data-driven systems in smart cities has the potential to significantly enhance public health outcomes. This improvement hinges on optimizing various factors, especially in the realms of hygiene standards and pollution monitoring. The ability to adhere to stringent hygiene procedures and closely monitor pollutants is essential for mitigating health risks in densely populated urban environments. As metropolitan areas become increasingly complex, there is a pressing need to prioritize the optimization of these processes.
 Materials and Methods. To address the challenges associated with public health optimization in smart cities, this study introduces Optimized Public Wellness using Machine Learning (OPWML). OPWML employs advanced machine learning techniques to augment hygiene protocols and pollution surveillance in smart urban areas. The proposed approach incorporates real-time validation, enhanced data-collecting efficiency, intelligent intervention impact, and increased throughput. The methodology aims to streamline processes and overcome the limitations of current approaches, providing more precise and prompt outcomes.
 Results. Simulation findings demonstrate the superior performance of OPWML compared to other methods. The average estimate accuracy achieved by OPWML is 86.76%, showcasing its efficacy in delivering accurate results. Real-time validation latency is notably low at 12.99 ms, indicating the system’s responsiveness. With a data collection efficiency of 22.96 GB/hour, OPWML demonstrates its ability to efficiently gather relevant data. The smart intervention impact of 33.20% underscores the system’s effectiveness in implementing intelligent interventions. Additionally, the throughput of 314.67 kbps signifies the high processing capacity of OPWML.
 Limitations. While OPWML exhibits promising results, it is essential to acknowledge certain limitations in this study. The simulation-based nature of the findings may not fully capture real-world complexities. Additionally, the generalizability of the results to diverse urban contexts requires further investigation. Limitations such as data privacy concerns and potential technological barriers should also be considered when implementing OPWML in practical settings.
 Conclusion. In conclusion, Optimized Public Wellness using Machine Learning (OPWML) emerges as a powerful tool for transforming public health processes in smart cities. The study highlights OPWML’s capacity to significantly enhance hygiene protocols and pollution surveillance, ensuring a healthier and environmentally sustainable urban setting. While acknowledging certain study limitations, the overall outcomes emphasize the potential of OPWML in revolutionizing public health practices and contributing to the well-being of urban populations in the era of smart cities.

3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

Machine Learning in Financial Transaction Fraud Detection and Prevention

In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography.

In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices. The research methodology involves a survey administered through Computer-Aided Web Interviewing (CAWI) tools, enabling real-time engagement with a diverse cohort of medical radiology technicians in the health domain. A second CAWI tool is submitted to experts to assess their feedback on the methodology. The survey explores key themes, including perceived advantages and challenges associated with domiciliary imaging, its impact on patient care, and the technological intricacies specific to conducting radiologic procedures outside the conventional clinical environment. Findings from a sample of 26 medical radiology technicians (drawn from a larger pool of 186 respondents) highlight a spectrum of opinions and constructive feedback. Enthusiasm is evident for the potential of domiciliary imaging to enhance patient convenience and provide a more patient-centric approach to healthcare. Simultaneously, this study suggests areas of intervention to improve the diffusion of home-based radiology. The methodology based on CAWI tools proves instrumental in the efficiency and depth of data collection, as evaluated by 16 experts from diverse professional backgrounds. The dynamic and responsive nature of this approach allows for a more allocated exploration of technicians' opinions, contributing to a comprehensive understanding of the evolving landscape of medical imaging services. Emphasis is placed on the need for national and international initiatives in the field, supported by scientific societies, to further explore the evolving landscape of teleradiology and the integration of artificial intelligence in radiology. This study encourages expansion involving other key figures in this practice, including, naturally, medical radiologists, general practitioners, medical physicists, and other stakeholders.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Supplementing a Paper Questionnaire with Web and Two-way Short Message Service (SMS) Surveys

Abstract When deciding which modes to offer, researchers consider cost, known respondent contact information, and potential mode effects. For a short survey on employment, we evaluate the effect of adding one of two new electronic data collection modes to a mailed questionnaire. We sent a survey to principals who previously responded to the National Center for Education Statistics’ (NCES) National Teacher and Principal Survey (NTPS) asking about their current job status. This questionnaire, known as the Principal Follow-up Survey (PFS), has typically been administered as a short paper form that is mailed to NTPS respondents. In 2022, the PFS introduced two new modes of completion, and principals were randomly assigned to receive: (i) a paper form only; (ii) a paper form, as well as emails with a direct link to complete a web survey; or (iii) a paper form, as well as invitations by text message to complete an automated two-way short message service text survey by responding to texted “yes/no” questions. This article compares overall response rates and time-to-response by mode to determine respondent preferences for completing short surveys. Adding either electronic mode significantly increased response rates and decreased the number of days in which completed surveys were received, compared to offering only a paper questionnaire. Although email and text messages are both forms of electronic communication that may be accessible on a smartphone, the added text message survey resulted in higher response rates than the added web survey. This suggests that respondents interact differently with emails and text messages they receive and that offering an option to complete a survey by text message can increase the speed and efficiency of data collection for short surveys.

Image Captioning with Multi-Context Synthetic Data

Image captioning requires numerous annotated image-text pairs, resulting in substantial annotation costs. Recently, large models (e.g. diffusion models and large language models) have excelled in producing high-quality images and text. This potential can be harnessed to create synthetic image-text pairs for training captioning models. Synthetic data can improve cost and time efficiency in data collection, allow for customization to specific domains, bootstrap generalization capability for zero-shot performance, and circumvent privacy concerns associated with real-world data. However, existing methods struggle to attain satisfactory performance solely through synthetic data. We identify the issue as generated images from simple descriptions mostly capture a solitary perspective with limited context, failing to align with the intricate scenes prevalent in real-world imagery. To tackle this, we present an innovative pipeline that introduces multi-context data generation. Beginning with an initial text corpus, our approach employs a large language model to extract multiple sentences portraying the same scene from diverse viewpoints. These sentences are then condensed into a single sentence with multiple contexts. Subsequently, we generate intricate images using the condensed captions through diffusion models. Our model is exclusively trained on synthetic image-text pairs crafted through this process. The effectiveness of our pipeline is validated through experimental results in both the in-domain and cross-domain settings, where it achieves state-of-the-art performance on well-known datasets such as MSCOCO, Flickr30k, and NoCaps.

Research on improving accuracy and efficiency of animal data collection and classification using machine learning

With the continuous expansion of machine learning algorithms in various application domains, the application value of new algorithms, such as Support Vector Machines and Convolutional Neural Networks in data classification, has garnered increasing attention. This paper takes machine learning algorithms as the research entry point, explores the concept of machine learning, and delves into its application value in data classification. This paper, starting with an overview of machine learning algorithms, analyzes the supervised and unsupervised learning problems in machine learning, focusing on the applications of Convolutional Neural Networks, Support Vector Machine models, and logistic regression algorithms in data classification. This study emphasizes designing and implementing a machine learning-based image classification system. Through an in-depth exploration of the application of machine learning algorithms in data classification, a fully functional system is constructed, encompassing multiple modules, including machine vision and software development. This system accurately classifies and recognizes images, providing practical tools and technical support for image processing and analysis. In this study, the goal of achieving good image classification is realized through research and the application of machine learning algorithms. By designing and implementing a machine learning-based image classification system, the accuracy and efficiency of classification in handling massive data are improved. This system also demonstrates wide-ranging prospects in software development and machine vision, among other fields.

BitTrace

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

What impacts matriculation decisions? Identifying students’ university choice factors on a global scale with Artificial Intelligence

ABSTRACT This study provides an empirical approach to utilizing an Artificial Intelligence (AI)-based system for identifying students’ university choice factors that impact their matriculation decision. We created an AI-based chatbot that gathered both qualitative and quantitative data from nearly 1200 participants worldwide. The entire human-AI interaction process was managed autonomously by the AI without researcher intervention. We analysed all data collected by the AI and identified relevant matriculation decision factors and themes. The AI collaboration demonstrated remarkable efficacy in streamlining the research workflow by consistently adhering to predefined criteria, eliminating variations and human-induced biases, establishing rapport with participants, and amplifying not just the efficiency and scalability of data collection but also the reliability and generalizability of the research findings. Acquiring such insights into students’ university choice factors from such a diversified and large sample may potentially empower policymakers to make informed decisions that synch higher education policies with students’ preferences, expectations, and needs, ultimately aiding institutions to improve recruitment and retention strategies leading to better overall performance and outcomes for both students and the institution itself. From an economic perspective, gaining this insight can foster closer alignment between higher education and the job market. By understanding students’ aspirations and the influences driving their decisions, institutions can tailor programmes that not only enhance graduates’ job prospects but also proactively contribute to economic growth and the development of a highly skilled and committed workforce.

Data Collection on MSME Creative Industries in Medan City According to a Sharia Accounting Perspective

Increasing creative industries in small and medium businesses in Medan is the main focus in efforts to revitalize the local community's economy. The role of the Department of Industry, Trade, Energy and Mineral Resources (Dinas Perindustrian, Perdagangan, Energi, dan Sumber Daya Mineral/Disperindag ESDM) of North Sumatra Province must be analyzed from a sharia accounting perspective to ensure smoothness and fairness in collecting data on creative industries in Micro, Small and Medium Enterprises (MSMEs) in Medan City. This research aims to analyze the role of the Disperindag ESDM of North Sumatra Province in collecting data on creative industries among MSMEs in Medan using a sharia accounting framework. The method used is literature research to understand the concepts and principles of sharia accounting as well as interviews with other stakeholders in Medan City to understand the practices applied in developing the MSME creative industry. Currently there are several problems in collecting data on Small and Medium Industries (Industri Kecil Menengah/IKM) by the ESDM Department of Industry and Trade of North Sumatra Province. One of them is the difficulty of collecting MSME data in a timely and accurate manner, especially in places that are difficult to reach, so this can affect the quality of the information obtained. However, human resources are still limited to collect and analyze MSME data so that the effectiveness and efficiency of data collection is also disrupted.

Abstract WP91: Stroke Tracking: Improving Core Measure Compliance Tracking & Efficiency

Background: Stroke program development and expansion requires excellent patient care and continued quality improvement via ongoing data collection. Substantial time and effort are required to identify patient records and manually assess metrics within charts. While real-time assessment of this data is ideal, achieving this is challenging. Purpose: We sought to create a real-time, electronic medical record (EMR) derived patient tracking list to improve accuracy, efficiency, and timeliness of stroke patient data collection. Methods: Ischemic stroke patient identification was optimized utilizing current patient lists, which over capture, and applying filters based on EMR entries applicable to stroke patients. Patient capture was reviewed to ensure adequate sensitivity and specificity. Subsequently, key stroke quality data points were identified. Custom columns were created to pull data from across the EMR into a central list. Column data was validated to ensure accuracy and confirm improved efficiency via timed chart reviews. A Mann-Whitney test was used to compare means pre and post-review. Of note, different reviewers performed pre and post reviews. Results: A stroke tracking list was successfully implemented. Chart review in the previous process averaged 173.2 seconds (STD 40.6) vs. 121.3 seconds (STD 17.8) in the new process (p<0.05). Patient identification times were not compared, though identifying charts in the old workflow ranged from 12 to 15 minutes per chart. Conclusions: Creation of a central tracking list within the electronic medical record is feasible and can improve patient identification, coordination of critical data sharing, and data collection efficiency. Utilization decreased data collection times and prompted creation of similar tracking lists for other core measure data. While sample sizes were small and pre and post reviewers were different, this work demonstrates feasibility and early foundational work.

Abstract WMP35: What's Your Time Worth?: Improving Efficiency in Tracking Core Measures Compliance in Real Time

Background: Stroke program development and expansion requires excellent patient care and continued quality improvement via ongoing data collection. Substantial time and effort are required to identify patient records and manually assess metrics within charts. While real-time assessment of this data is ideal, achieving this is challenging. Purpose: We sought to create a real-time, electronic medical record (EMR) derived patient tracking list to improve accuracy, efficiency, and timeliness of stroke patient data collection. Methods: Ischemic stroke patient identification was optimized utilizing current patient lists, which over capture, and applying filters based on EMR entries applicable to stroke patients. Patient capture was reviewed to ensure adequate sensitivity and specificity. Subsequently, key stroke quality data points were identified. Custom columns were created to pull data from across the EMR into a central list. Column data was validated to ensure accuracy and confirm improved efficiency via timed chart reviews. A Mann-Whitney test was used to compare means pre and post-review. Of note, different reviewers performed pre and post reviews. Results: A stroke tracking list was successfully implemented. Chart review in the previous process averaged 173.2 seconds (STD 40.6) vs. 121.3 seconds (STD 17.8) in the new process (p<0.05). Patient identification times were not compared, though identifying charts in the old workflow ranged from 12 to 15 minutes per chart. Conclusions: Creation of a central tracking list within the electronic medical record is feasible and can improve patient identification, coordination of critical data sharing, and data collection efficiency. Utilization decreased data collection times and prompted creation of similar tracking lists for other core measure data. While sample sizes were small and pre and post reviewers were different, this work demonstrates feasibility and early foundational work.