Implementation Of Platform Research Articles

A smart contract-based decentralized web3 platform for education

The blockchain technology can become a solution in data privacy and protection, being considered a public and decentralized database, in which the identity of a user is masked in the form of an address. Based on this technology, several decentralized protocols such as Bitcoin or Ethereum have been created, with the help of which decentralized applications can be created. In this article, we examine how these technologies can be used to ensure the transparency of a software application, but also the privacy and protection of user data. Other facilities like Non-Fungible Token - NFT and Smart Contract are used. The paper proposes the design and the implementation (including the source code) of a web platform for education / courses incorporating blockchain technology. The results and conclusions are showed. Finally, the contributions of the paper, the limitations and future works are also highlighted.

Modular organization of a decision support system for managing an innovative economy

The paper examines the problems of managing state development programs during the transformation of the economy into an innovative one. A transition from program-target management to program-design management is proposed. This transition is justified by applying the modern theory of economic systems to the entire system of space for the formation and implementation of government programs. Based on this transition, four spirals of development become the basic components of management: objects, processes, environments, and projects. This allows you to formalize management goals and criteria and move on to the construction/development of a decision support system. The work provides the main approach to constructing a platform implementation of management of state development programs.

Software Quality Assurance as a Service: Encompassing the quality assessment of software and services

This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices by awarding standards-based digital badges to software and services. It provides an easy-to-use web-based graphical user interface which facilitates the interaction with server-side components in charge of automatically creating CI/CD (Continuous Integration/Continuous Delivery) pipelines for automated testing of the baseline criteria. The service is in production and has performed over 2800 assessments, awarding more than 125 digital badges across several scientific disciplines.

Design and implementation of a monitoring platform based on beidou high precision positioning technology

Traditional BDS positioning technology has limitations in accuracy, robustness, and availability in certain real-time monitoring applications. Therefore, this article designs and implements a monitoring platform based on Beidou high-precision positioning technology to solve the limitations of traditional BDS positioning technology and provide more accurate, reliable, and real-time positioning services. The research first analyzes and optimizes the positioning algorithms of the existing Beidou system to improve the accuracy of position measurement. Then apply the optimized algorithm to the system architecture of the monitoring platform to achieve high-precision positioning function. Finally, design a suitable user interface and data processing module so that users can easily obtain and analyze positioning data. Through experimental verification, this platform has achieved high accuracy and stability in position measurement. Users can obtain real-time location information through this platform and further analyze and process it.

Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD)

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.

System for Tracking and Assessing Issues of Students in College Life

This project explores the development and implementation of a web-based platform designed to connect mentors and mentees, fostering meaningful interactions through messaging and appointment scheduling. The platform aims to bridge the gap between individuals seeking guidance (mentees) and experienced professionals willing to share their expertise (mentors). By offering a user-friendly interface and various functionalities, the website seeks to streamline the mentorship process, creating a conducive environment for communication and learning. The platform's primary features include comprehensive user profiles, a secure messaging system enabling direct communication between mentors and mentees, and an intuitive appointment scheduling mechanism. Mentors can showcase their expertise and availability, while mentees can search for mentors based on specific criteria and request appointments at their convenience. The project delves into the technological and design considerations essential for the platform's success, emphasizing the significance of a secure messaging system, personalized user experiences, efficient appointment scheduling, and reliable data privacy measures. Additionally, the implementation of a robust review and feedback system fosters trust and transparency, allowing mentees to make informed decisions when selecting mentors. Key Words: Web platform, mentorship,

DISHARMONY AND STRUGGLE: MANAGEMENT AND MOBILIZATION OF DIGITAL-BASED ECONOMIC RESOURCES IN TOURIST VILLAGE

The development of digital platforms has not fully reached tourist villages optimally, apart from the fact that their management still contains conflicts of interest between stakeholders. This research examines the role of community empowerment, implementation of sustainable village development programs, and implementation of digital platforms on the management and mobilization of economic resources. The research type is quantitative and the population consists of 8.402 people in Sudaji tourist village, Buleleng-Bali. A total of 110 people were used as samples (village heads, pokdarwis, traditional kelian, and the community) with a random sampling technique using the Slovin formula. Data collection uses a questionnaire with a Likert scale. The results show that the community empowerment variable does not affect the mobilization of economic resources. In contrast, the sustainable development variable shows different results where it significantly positively impacts the mobilization of economic resources. Finally, the digital platform implementation variable does not affect the mobilization of economic resources. This research focuses on the interests of the community as stakeholders so that they can access economic resources optimally and have an impact so that they can empower them with their capacity to increase economic independence while meeting their basic needs so that they become resource literate with the digital world and sustainable villages can be realized.

지역 기반 시한식료품 타임커머스 플랫폼의 설계와 구현

지역 기반 시한식료품 타임커머스 플랫폼의 설계와 구현

Real-world use and clinical impact of an electronic patient-reported outcome tool in patients with solid tumors treated with immuno-oncology therapy

BackgroundUtilization of electronic patient-reported outcome (ePRO) tools to monitor symptoms in patients undergoing cancer treatment has shown clinical benefits. Tennessee Oncology (TO) implemented an ePRO platform in 2019, allowing patients to report their health status online. We conducted a real-world, multicenter, observational, non-interventional cohort study to evaluate utilization of this platform in adults with solid tumors who initiated immuno-oncology (IO) therapy as monotherapy or in combination at TO clinics.MethodsPatients initiating IO therapy prior to platform implementation were included in a historical control (HC) cohort; those initiating treatment after implementation were included in the ePRO cohort, which was further divided into ePRO users (platform enrollment ≤ 45 days from IO initiation) and non-users. Data were extracted from electronic medical records; patients were followed for up to 6 months (no minimum follow up). Outcomes included patient characteristics, treatment patterns, duration of therapy (DoT), and overall survival (OS).ResultsData were collected for 538 patients in the HC and 1014 in the ePRO cohort; 319 in the ePRO cohort were ePRO users (uptake rate 31%). Baseline age was higher, more patients had stage IV disease at diagnosis, and more received monotherapy (82 vs 52%, respectively) in the HC vs the ePRO cohort. Median follow-up was 181.0 days (range 0.0–182.6) in the HC and 175.0 (0.0–184.0) in the ePRO cohort. Median DoT of index IO regimen was 5.1 months (95% confidence interval [CI], 4.4–NE) in the HC cohort vs not estimable (NE) in the ePRO cohort. Multivariable regression adjusting for baseline differences confirmed lower risk of treatment discontinuation in the ePRO vs HC cohort: hazard ratio (HR) 0.83 (95% CI, 0.71–0.97); p < 0.05. The estimated 6-month OS rate was 65.5% in the HC vs 72.4% in the ePRO cohort (p < 0 .01). Within the ePRO cohort, DoT of index IO regimen and OS did not differ between users and non-users. In ePRO users, patient platform use was durable over 6 months.ConclusionImprovements in DoT and OS were seen after ePRO platform implementation. Conclusions are limited by challenges in separating the impact of platform implementation from other changes affecting outcomes.

First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Transformers are a very important asset in the electrical transmission grid, and they can suffer from destructive events—e.g., rare transformer fires. Unfortunately, destructive events often lead to a lack of data available for investigators during post-event forensics and failure analysis. This fact has motivated our design and implementation of a robotic multi-sensor platform and cloud backend solution for the lifecycle monitoring, inspection, diagnostics, and condition assessment of transformers. The robotic platform collects data from specific viewpoints around the transformer during operation and at specific relevant lifecycle milestones of the transformer (e.g., at the factory acceptance test) in an automated, repetitive, precise, and reliable manner. The acquired data are stored in the cloud backend, which also provides computing resources and data access to relevant in- and off-premises services (e.g., respectively, SCADA systems, and weather reports). In this paper, we present the results of our first measurement campaign to showcase the value of our solution for transformer lifecycle monitoring, for anomaly detection, and as a crucial tool for post-event forensics in the case of destructive events.

A review of time-memory trade-off techniques

This paper delves into the discussion of time-memory trade-off techniques in the field of cryptanalysis. The method was initially introduced by Hellman in 1980, subsequently, DP trade-off, rainbow trade-off, and checkpoint trade-off have been proposed to enhance the efficiency of cryptographic attacks. This paper elaborates on the concept of rainbow trade-off and their variants and presents optimizations in terms of storage and runtime speed for time-memory trade--off methods. Ingenious storage optimization significantly reduces the storage overhead of pre-computed tables, and the rapid advancement of implementation platforms achieves speed optimization for the online phase. Through these optimization measures, time-memory trade-off methods exhibit even more remarkable performance in practical applications. For researchers and practitioners in the field of cryptography, the content of this paper provides valuable references and insights for their work.

Optical evaluation of dansyl derivatives and their implementation in low-cost and flexible dye-doped PMMA platforms for efficient detection of hazardous chemical vapours

This work investigates the creation and analysis of six novel dansyl derivatives (named L1 to L6) and their incorporation into PMMA polymers for environmental sensing purposes. The properties of these derivatives were extensively examined in different solvents and solid states. Notably, they exhibited a significant change in color depending on the solvent's polarity, suggesting their potential to assess microenvironmental polarity. Moreover, these derivatives showed promising capabilities in selectively detecting Cu2+ and Hg2+ metal ions in acetonitrile solution, forming individual species with these metals. Furthermore, their responsiveness to changes in acidity and basicity was explored both in acetonitrile solution and within a PMMA polymer matrix, indicating their potential for pH-sensitive applications. By integrating these derivatives into PMMA polymers, acid-base sensitive materials were produced, suitable for monitoring environmental conditions such as hazardous gas detection and pH level changes. This integration addresses the solubility challenges of dansyl compounds and broadens their use in various sensing applications.

IoTwins: Implementing Distributed and Hybrid Digital Twins in Industrial Manufacturing and Facility Management Settings

A Digital Twin (DT) refers to a virtual representation or digital replica of a physical object, system, process, or entity. This concept involves creating a detailed, real-time digital counterpart that mimics the behavior, characteristics, and attributes of its physical counterpart. DTs have the potential to improve efficiency, reduce costs, and enhance decision-making by providing a detailed, real-time understanding of the physical systems they represent. While this technology is finding application in numerous fields, such as energy, healthcare, and transportation, it appears to be a key component of the digital transformation of industries fostered by the fourth Industrial revolution (Industry 4.0). In this paper, we present the research results achieved by IoTwins, a European research project aimed at investigating opportunities and issues of adopting DTs in the fields of industrial manufacturing and facility management. Particularly, we discuss a DT model and a reference architecture for use by the research community to implement a platform for the development and deployment of industrial DTs in the cloud continuum. Guided by the devised architectures’ principles, we implemented an open platform and a development methodology to help companies build DT-based industrial applications and deploy them in the so-called Edge/Cloud continuum. To prove the research value and the usability of the implemented platform, we discuss a simple yet practical development use case.

Multipurpose platform for analog quantum simulation

Atom-based quantum simulators have had many successes in tackling challenging quantum many-body problems, owing to the precise and dynamical control that they provide over the systems' parameters. They are, however, often optimized to address a specific type of problem. Here, we present the design and implementation of a Li6-based quantum gas platform that provides wide-ranging capabilities and is able to address a variety of quantum many-body problems. Our two-chamber architecture relies on a robust combination of gray molasses and optical transport from a laser-cooling chamber to a glass cell with excellent optical access. There, we first create unitary Fermi superfluids in a three-dimensional axially symmetric harmonic trap and characterize them using thermometry, reaching temperatures below 20 nK. This allows us to enter the deep superfluid regime with samples of extreme diluteness, where the interparticle spacing is sufficiently large for direct single-atom imaging. Second, we generate optical lattice potentials with triangular and honeycomb geometry in which we study diffraction of molecular Bose-Einstein condensates, and show how going beyond the Kapitza-Dirac regime allows us to unambiguously distinguish between the two geometries. With the ability to probe quantum many-body physics in both discrete and continuous space, and its suitability for bulk and single-atom imaging, our setup represents an important step towards achieving a wide-scope quantum simulator. Published by the American Physical Society 2024

Virtual first: implementation of a novel sleep telehealth platform in the United States military

BackgroundThere is a gross shortage of sleep specialist providers within the military health system. Telehealth and mobile health represent promising approaches to increase access to high quality, cost-effective care in the U.S military.ObjectivesThis paper reports findings from a mixed-methods clinical implementation study of a novel sleep telehealth platform at two military treatment facilities in the National Capitol Region. The platform includes a mobile app and integrated wearable sensors (i.e., a commercial off-the-shelf sleep tracker [Fitbit]). The primary purpose was to evaluate the implementation of a 10-day remote monitoring assessment and provision of evidence-based sleep treatment recommendations to patients and providers. In addition, we sought to observe, in an exploratory manner, subsequent engagement with the app during 5 days of personalized sleep education and training.MethodsPatients with sleep problems completed an intensive 10-day remote monitoring assessment that included a baseline intake questionnaire, daily sleep diaries, twice daily symptom surveys, and Fitbit. Based on this assessment, patients received personalized assessment results. Concurrently, a provider report was generated that included provisional diagnoses and evidence-based treatment recommendations. Next, participants gained access to personalized sleep education and trainings within the mobile app. Within an established implementation science framework, outcomes were assessed via behavioral adherence (engagement with the app) and separate questionnaires for patients and providers. Last, we conducted four focus groups with patients and 12 key informant interviews with primary care managers (PCMs) and economic stakeholders to seek feedback and recommendations for future directions.ResultsTwo hundred and seventy patients participated in the study. Using validated research questionnaires, participants reported high-risk for obstructive sleep apnea (65.6%), moderate to severe insomnia (38.2%), and moderate to severe daytime sleepiness (38.5%), and moderate to severe anxiety (14.1%) and depressive (20.4%) symptoms. Total sleep time was 6.6 (SD = 1.8) h based on sleep diaries and 6.1 (SD = 1.8) h based on Fitbit. Regarding implementation, reach, effectiveness, adoption, implementation, and maintenance were all notably high, based on quantitative and qualitative data from participants and PCMs.ConclusionsSleep telehealth and mobile health represent promising approaches to increase access to cost-effective, evidence-based care for sleep disorders in the U.S. military.

AI platform model on 4IR megatrend challenges: complex thinking by active and transformational learning

PurposeThe objective of this study is to propose a model for the implementation of a technological platform for participants to develop solutions to problems related to the Fourth Industrial Revolution (4IR) megatrends, and taking advantage of artificial intelligence (AI) to develop their complex thinking through co-creation work.Design/methodology/approachThe development of the model is based on a combination of participatory action research and user-centered design (UCD) methodologies, seeking to ensure that the platform is user-oriented and based on the experiences of the authors. The model itself is structured around the active and transformational learning (ATL) framework.FindingsThis study highlights the importance of addressing 4IR megatrends in education to prepare students for a technology-driven world. The proposed model, based on ATL and supported by AI, integrates essential competencies for tackling challenges and generating innovative solutions. The integration of AI into the platform fosters personalized learning, collaboration and reflection and enhances creativity by offering new insights and tools, whereas UCD ensures alignment with user needs and expectations.Originality/valueThis research presents an innovative educational model that combines ATL with AI to foster complex thinking and co-creation of solutions to problems related to 4IR megatrends. Integrating ATL ensures engagement with real-world problems and critical thinking while AI provides personalized content, tutoring, data analysis and creative support. The collaborative platform encourages diverse perspectives and collective intelligence, benefiting other researchers to better conceive learner-centered platforms promoting 21st-century skills and co-creation.

Feasibility, Acceptability, and Preliminary Effectiveness of a Combined Digital Platform and Community Health Worker Intervention for Patients With Heart Failure: Protocol for a Randomized Controlled Trial.

Interventions focused on remote monitoring and social needs care have shown promise in improving clinical outcomes for patients with heart failure (HF). However, patient willingness to use technology as well as concerns about access in underresourced settings have limited digital platform implementation and adoption. There is little research in HF populations examining the effect of a combined digital and social needs care intervention that could enhance patient engagement in digital platform use while closing gaps in care related to social determinants of health. Here, we describe the protocol for a clinical trial of a digitally enabled community health worker intervention designed for patients with HF. This study aims to describe the protocol for a randomized controlled trial assessing the acceptability, feasibility, and preliminary effectiveness of an intervention that combines remote monitoring with a digital platform and community health worker (CHW) social needs care for patients with HF who are transitioning from hospital to home. Given the elevated morbidity and mortality, identifying comprehensive and patient-centered interventions at the time of hospital care transitions that can improve clinical outcomes, impact cost, and augment the quality of care for this cohort is a priority. This trial randomized adult inpatient participants (n=50) with a diagnosis of HF receiving care at a single academic health care institution to the 30-day intervention (digital platform+CHW pairing+usual care) or the 30-day control (CHW pairing+usual care) arms. All study participants completed baseline questionnaires and 30-day exit interviews and questionnaires. The primary outcomes will be acceptability, feasibility, and preliminary effectiveness. This clinical trial opened for enrollment in September 2022 and was completed in June 2023. Initial results are expected to be published in the spring of 2024, and analysis is currently underway. Feasibility outcome measures will include the use rates of the biometric sensor (average hours per day), the digital blood pressure monitor (average times per day), the weight scale (average times per day), and the completion of the symptoms questionnaire (average times per day). The acceptability outcome will be measured by the patients' response to the truthfulness of the statement that they would be willing to use the digital platform in the future (response options: very true, somewhat true, or not true). Preliminary effectiveness will be measured by tracking 30-day clinical outcomes (hospital readmissions, emergency room visits, and missed primary care and cardiology appointments). The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of supportive home-based social needs care to enhance engagement and the potential effectiveness of clinically focused digital platforms. These results may inform the construction of a future multi-institutional trial designed to test the true effectiveness of this intervention in HF. ClinicalTrials.gov NCT05130008; https://clinicaltrials.gov/study/NCT05130008. DERR1-10.2196/55687.

Abstract TMP37: Improved Outcomes for Acute Ischemic Stroke Patients After Implementation of the RapidAI Platform in a Comprehensive Stroke Center in Santiago, Chile

Introduction: In Latin America (LATAM), the prevalence of stroke is alarmingly high. The advancement of artificial intelligence (AI) technologies has potential to dramatically improve healthcare outcomes. RapidAI is a software solution that utilizes AI to quickly analyze imaging data to identify vascular occlusions and salvageable tissue. Physicians receive notifications and can review source images on the Rapid mobile app (RMA). Methods: This was a retrospective analysis of 1,672 consecutive acute stroke patients (AIS) treated at Clinica Alemana de Santiago between 2014-2023. Primary outcomes were the number of AIS patients treated with thrombolysis (IV tPA), mechanical thrombectomy (MT), or decompressive craniectomies as well as time to treatment. Secondary outcomes included discharge Modified Rankin score (mRS) and hospital length of stay (LOS). Results: This analysis included 824 patients pre-RapidAI installation and 848 after installation of Rapid CTP with hypodensity, CTA, ASPECTS, LVO and RMA communication. The baseline NIHSS and last know well to hospital arrival was balanced between the 2 study cohorts. Median time to IV tPA improved by 42 minutes (80 to 38 minutes, p &lt; 0.0001). IV tPA utilization increased from 23% to 30% post-Rapid (p = 0.0033). MT procedure utilization increased from 5% pre-Rapid to 10% (p &lt; 0.0001). Decompressive craniectomies were reduced from 13 to 4, LOS was unchanged. There was a favorable shift in the distribution of mRS scores at discharge in post-Rapid patients (p = 0.031) and a significant increase in mRS 0-1 outcomes (61% vs 45%). Discussion: Following implementation of the AI software platform in a comprehensive stroke center like Clinica Alemana, there was a significant increase in patients treated with IV tPA, faster door to tPA and/or MT times, a decrease in the number of DCTs, and improved discharge mRS scores. This work supports future studies expanding the use of AI technology for improving stroke outcomes in LATAM.

Abstract WP126: Improved Stroke Outcomes Following Implementation of RapidAI Platform at Ascension-Illinois

Introduction: The rapid advancement of artificial intelligence (AI) technologies holds promise for improving healthcare outcomes. RapidAI is a software solution that utilizes AI to rapidly analyze imaging data to identify vascular occlusions and salvageable tissue. Physicians receive notifications and can review source images on the Rapid mobile app (RMA). Methods: Data for this study were obtained from the Get with the Guidelines Stroke database. The study focused on adult patients (18 years or older) admitted to Alexian Brothers Medical Center in Illinois between 2019-2022 who were diagnosed with acute ischemic stroke. Primary outcomes were the number of AIS patients treated with thrombolysis (IV tPA or IV TNK) and/or mechanical thrombectomy (MT) and time to treatment. Door to MT puncture times for transfer patients were analyzed separately from direct presentation patients. Discharge Modified Rankin score (mRS) was assessed as a secondary clinical outcome. Results: This analysis included 1,732 patients, 1,193 prior to installation of the RapidAI platform and 539 after installation of Rapid CTP, CTA, ASPECTS, LVO and RMA. Median door to femoral puncture time for MT cases decreased from 2 hr 28 min to 1 hr 58 min (p=0.012) post-Rapid for non-transfer patients. Door to femoral puncture times for transfer patients were unchanged. MT procedure volume increased from 9.6% to 14.5% (p=0.007). IV tPA/TNK volume and door-to-needle (DTN) times were unchanged; 15% treated with tPA/TNK pre-Rapid and 14% post-Rapid, DTN 48 vs 47 min. There was an improvement in the distribution of discharge mRS scores in post-Rapid patients (p&lt;0.0001). Discussion: Following implementation of the AI software platform there was a significant increase in patients treated with MT, faster door to MT times for non-transfer patients, and improved discharge mRS scores. These data support future studies expanding the use of AI technology to improve stroke outcomes.

Abstract WP134: Door to Angiography in Large Vessel Occlusions Pre and Post Implementation of Automated Image Interpretation and Sharing Platform: A Single Center Study

Background: Viz LVO is an FDA approved software that uses artificial intelligence to identify large vessel occlusions (LVOs) on computed tomography angiography (CTA) and facilitate communication between providers in a hub-and-spoke network. Whether this software expedites patient evaluation for treatment is unexplored. Methods: A single center retrospective registry was queried for patients with LVO of the internal carotid, proximal middle cerebral, or basilar artery in a stroke network (8 spokes, 1 hub). Eligible patients were in a 6-month period before (pre-Viz) and following implementation (post-Viz) of Viz LVO. Descriptive statistics and robust regression modeling were used to describe time from first contact to angiography (primary outcome) and other intervals between pre-Viz and post-Viz study groups. Results: Of 132 included patients (n=58 pre-Viz), small differences were found in demographics and non-significantly fewer patients underwent endovascular therapy (EVT) in the post-Viz period (73.0% vs. 86.2%, p=0.07). Time from spoke arrival to hub arrival among transferred patients was non-significantly shorter post-Viz (median 124 vs. 147 min, p=0.15). In patients who received EVT, there was a non-significant reduction in time from first contact to angiography (median 116 vs. 155 min, p=0.10) with shorter intervals for transferred patients (median 142 vs. 169 min, p=0.05) but not hub arrivals (p=0.81). For transferred patients, shorter intervals from initial contact to angiography were observed in adjusted robust regression, accounting for stroke severity, age, Alberta Stroke Program Early Computed Tomography Scale score, and use of perfusion imaging (β -32.4, 95% confidence interval -61.6 to -3.26, p=0.03). Conclusions: Implementation of the Viz LVO platform was associated with shorter intervals between initial hospital contact and neurointervention among transferred patients. Larger datasets are needed to validate these observations.