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Atrial fibrillation ablation workflow optimization facilitated by high-power short-duration ablation and high-resolution mapping

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EUROPACE 2024年第3期26卷 euae067页

作者： Fink, Thomas Sciacca, Vanessa Nischik, Flemming Bergau, Leonard Guckel, Denise El Hamriti, Mustapha Khalaph, Moneeb Braun, Martin Winnik, Stephan Didenko, Maxim Imnadze, Guram Sommer, Philipp Sohns, Christian Herz & Diabeteszentrum NRW Clin Electrophysiol Georgstr 11 D-32545 Bad Oeynhausen Germany Univ Hosp Zurich Univ Heart Ctr Dept Cardiol Zurich Switzerland

Aims Pulmonary vein isolation (PVI) for catheter ablation of atrial fibrillation (AF) is a time-demanding procedure. High-power short-duration (HPSD) ablation protocols and high-density mapping catheters have recently been introduced to clinical practice. We investigated the impact of high-density mapping and HPSD ablation protocols on procedural timing, efficacy, and safety by comparing different standardized set-ups. Methods and results Three electrophysiology (EP) laboratory set-ups were analysed: (i) circular catheter for mapping and HPSD ablation with 30/35 W guided by an ablation index (AI);(ii) pentaspline catheter for mapping an HPSD ablation with 50 W guided by an AI;and (iii) pentaspline catheter for mapping and HPSD ablation with 90 W over 4 s using a novel ablation catheter. All patients underwent PVI without additional left atrial ablation strategies. Procedural data and operating intervals in the EP laboratory were systematically analysed. Three hundred seven patients were analysed (30/35 W AI: n = 102, 50 W AI: n = 102, 90 W/4 s: n = 103). Skin-to-skin times [105.3 +/- 22.7 (30/35 W AI) vs. 81.4 +/- 21.3 (50 W AI) vs. 69.5 +/- 12.2 (90 W/4 s) min, P <= 0.001] and total laboratory times (132.8 +/- 42.1 vs. 107.4 +/- 25.7 vs. 95.2 +/- 14.0 min, P < 0.001) significantly differed among the study groups. Laboratory interval analysis revealed significant shortening of mapping and ablation times. Arrhythmia-free survival after 12 months was not different among the study groups (log-rank P = 0.96). Conclusion The integration of high-density mapping and HPSD protocols into an institutional AF ablation process resulted in reduced procedure times without compromising safety or efficacy. [GRAPHICS]

关键词： Atrial fibrillation Pulmonary vein isolation High power short duration High-density mapping workflow optimization Laboratory analysis

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Business workflow optimization Through Process Model Redesign

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年第6期69卷 3068-3084页

作者： Kumar, Akhil Liu, Rong Penn State Univ Smeal Coll Business State Coll PA 16802 USA Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

Business process redesign has garnered much interest because such processes must be continuously improved to realize greater efficiency in the midst of evolving changes in work practices, environment, and technology. This helps a business to stay competitive. A process model may be redesigned by a rearrangement of various activities as per best practices to satisfy predefined business rules and constraints for a specific objective. This objective is stated in terms of functional goals like desired or acceptable process behavior, and nonfunctional goals such as cost, time, quality of service, and flexibility. Thus, many potential improvement possibilities that emerge through restructuring can be explored. When many such alternatives exist, there is a need for a formal methodology for evaluating these designs on various metrics to gain a deeper understanding of the tradeoffs involved. In this article, we describe a novel, formal methodology for optimizing the cost-benefit considerations within a space of possible redesigns to develop a cost vs. time efficient frontier. We also illustrate our approach with two case studies, one based on a large, real dataset, and describe a proposal for implementing it. Several managerial insights emerge from our work.

关键词： Task analysis optimization Measurement Best practices Automobiles Routing Business process management mixed integer programming process redesign redesign matrix workflow optimization

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Machine learning-driven implementation of workflow optimization in cloud computing for IoT applications

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INTERNET TECHNOLOGY LETTERS 2025年第3期8卷

作者： Jamal, Md Khalid Faisal, Mohammad Integral Univ Dept Comp Applicat Lucknow India

The optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML-driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large-scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

关键词： adaptive systems cloud computing internet of things (IoT) machine learning scalability workflow optimization

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Use case-based evaluation of workflow optimization strategy in real-time computation system

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JOURNAL OF SUPERCOMPUTING 2020年第1期76卷 708-725页

作者： Ahmad, Saima Gulzar Khan, Hikmat Ullah Ijaz, Samia Munir, Ehsan Ullah COMSATS Univ Islamabad Wah Campus Islamabad Pakistan

With the start of big data era, data stream computing has emerged as a well-known approach to optimize data-intensive workflows. Apache STORM is an open-source real-time distributed computation system for processing data streams and has been opted by famous organizations such as Twitter, Yahoo, Alibaba, Baidu, Groupon. The workflows are implemented as topologies in STORM. The main aspect that controls the execution performance of a workflow in STORM is the strategy of scheduling the topology components (spout and bolts). In this paper, we evaluate and analyze the performance of our algorithm Partition-based Data-intensive workflow optimization Algorithm (PDWA) in Apache STORM using a use case workflow, EURExpressII. It is a real-world application-based workflow that builds a transcriptome-wide atlas of gene expression for the developing mouse embryo established by ribonucleic acid (RNA) in situ hybridization. Our proposed algorithm, PDWA, partitions the application task graph so that the data movement between partitions is minimum. Each partition is then mapped on one machine for the execution of tasks of that partition. It provides minimum execution time for that particular partition. Partial task duplication is also part of this algorithm that enhances the performance. A STORM-based computing cluster is developed in OpenStack cloud which is used as a computing environment. The performance of PDWA-based optimizer is evaluated with the data sets of different sizes. The achieved results show that PDWA performs with 21% improved average execution time for different sizes of data sets and varying execution nodes. In addition, the comparative results show that on average the efficiency of PDWA is 20.4% higher as compared to STORM default scheduler (SDS).

关键词： workflow optimization STORM topology Partitions Data intensive Stream data processing

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Shortening door-to-puncture time and improving patient outcome with workflow optimization in patients with acute ischemic stroke associated with large vessel occlusion

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BMC EMERGENCY MEDICINE 2022年第1期22卷 136-136页

作者： Yang, Shuiquan Yao, Weiping Siegler, James E. Mofatteh, Mohammad Wellington, Jack Wu, Jiale Liang, Wenjun Chen, Gan Huang, Zhou Yang, Rongshen Chen, Juanmei Yang, Yajie Hu, Zhaohui Chen, Yimin Foshan Sanshui Dist Peoples Hosp Dept Neurol 16 Guanghaidadaoxi Foshan 528100 Guangdong Provi Peoples R China Foshan Sanshui Dist Peoples Hosp Adv Natl Stroke Ctr 16 Guanghaidadaoxi Foshan 528100 Guangdong Provi Peoples R China Foshan Sanshui Dist Peoples Hosp Dean Off & Adv Natl Stroke Ctr Foshan Guangdong Peoples R China Cooper Univ Hosp Cooper Neurol Inst Camden NJ USA Queens Univ Belfast Sch Med Dent & Biomed Sci Belfast Antrim North Ireland Cardiff Univ Sch Med Cardiff Wales Foshan Sanshui Dist Peoples Hosp Foshan Guangdong Peoples R China Shaoguan Univ Sch Med Shaoguan Guangdong Peoples R China Foshan Sanshui Dist Peoples Hosp Dept Radiol Foshan Guangdong Peoples R China Guangzhou Med Univ Clin Coll 2 Guangzhou Guangdong Peoples R China Southern Med Univ Sch Clin Med 1 Guangzhou Guangdong Peoples R China Foshan Sanshui Dist Peoples Hosp Med Dept Foshan Guangdong Peoples R China Foshan Sanshui Dist Peoples Hosp Adv Natl Stroke Ctr Foshan Guangdong Peoples R China

Objective We aimed to evaluate door-to-puncture time (DPT) and door-to-recanalization time (DRT) without directing healthcare by neuro-interventionalist support in the emergency department (ED) by workflow optimization and improving patients' outcomes. Methods Records of 98 consecutive ischemic stroke patients who had undergone endovascular therapy (EVT) between 2018 to 2021 were retrospectively reviewed in a single-center study. Patients were divided into three groups: pre-intervention (2018-2019), interim-intervention (2020), and post-intervention (January 1(st) 2021 to August 16(th), 2021). We compared door-to-puncture time, door-to-recanalization time (DRT), puncture-to-recanalization time (PRT), last known normal time to-puncture time (LKNPT), and patient outcomes (measured by 3 months modified Rankin Scale) between three groups using descriptive statistics. Results Our findings indicate that process optimization measures could shorten DPT, DRT, PRT, and LKNPT. Median LKNPT was shortened by 70 min from 325 to 255 min(P < 0.05), and DPT was shortened by 119 min from 237 to 118 min. DRT shortened by 132 min from 338 to 206 min, and PRT shortened by 33 min from 92 to 59 min from the pre-intervention to post-intervention groups (all P < 0.05). Only 21.4% of patients had a favorable outcome in the pre-intervention group as compared to 55.6% in the interventional group (P= 0.026). Conclusion This study demonstrated that multidisciplinary cooperation was associated with shortened DPT, DRT, PRT, and LKNPT despite challenges posed to the healthcare system such as the COVID-19 pandemic. These practice paradigms may be transported to other stroke centers and healthcare providers to improve endovascular time metrics and patient outcomes.

关键词： Door-to-puncture time Door-to-recanalization time Puncture-to-recanalization time Ischemic stroke Endovascular therapy workflow optimization

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Volumetric modulated craniospinal irradiation workflow optimization through quantitative analytics: a single-institution case study comparing pediatric and adult settings

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JOURNAL OF RADIATION ONCOLOGY 2020年第3-4期9卷 113-121页

作者： Nguyen, Steven M. Weng, Julius K. Sison, Julian Waxer, Jonathan Dholakia, Nimit Lam, Barbara Marques, Christophe Olch, Arthur J. Ballas, Leslie Chang, Eric L. Wong, Kenneth K. Univ Cent Florida Coll Med Orlando FL 32816 USA Univ Texas Hlth Sci Ctr Houston Dept Internal Med Houston TX 77030 USA Robert Wood Johnson Med Sch Piscataway NJ USA Southern Calif Permanente Med Grp Dept Radiat Oncol Los Angeles CA USA St Barnabas Hosp Dept Radiol Livingston NJ USA Keck Sch Med USC Los Angeles CA USA Keck Sch Med USC Dept Radiat Oncol Los Angeles CA USA Childrens Hosp Los Angeles Dept Radiat Oncol Los Angeles CA 90027 USA

Objective We used quantitative analytics to assess the efficiency of the clinical workflow for radiation therapy delivery in children and adults focusing on our experience with volumetric modulated arc therapy (VMAT) craniospinal irradiation (CSI). Methods We retrospectively collected clinical and treatment specific data between June 2013 and November 2018 for 614 VMAT CSI sessions of 41 pediatric patients and 48 VMAT CSI sessions of 6 adult patients treated at two hospitals within the same academic institution. Descriptive statistics were generated and a multiple linear regression model was created to predict total radiation time (TRT) based on these predictors. Prolonged treatment sessions were filtered using the interquartile range outlier detection method for quality assurance review. Results The median pediatric TRT for all VMAT CSI treatment sessions was 21.07 min (interquartile range (IQR) 9.05). Pediatric patient height, fraction number (fractions 1-3 vs. fractions > 3), and number of isocenters were significant predictors for TRT (p < 0.01). In the case of adult patients, the median TRT for all VMAT CSI treatment sessions was significantly longer at 37.23 min (IQR 45.92,p < 0.05), even in subgroup analyses comparing plans adjusted for the same number of isocenters and IGRT modality in non-sedated patients. Only in pediatric patients did the median TRT decrease after the first three treatments. Qualitative causal factor analysis revealed plausible causes for prolonged TRT as anxiety, anticipatory nausea and vomiting, challenging setups, anesthesia tolerance, and delay to allow for physician online IGRT review of setup. Conclusions Simple descriptive statistics and regression modeling can be used to assess workflow efficiency and estimate radiation treatment times based on patient and technique-specific factors. We modeled VMAT CSI treatment delivery times at our pediatric and adult hospitals to set time expectations for IGRT and beam delivery. workflow analy

关键词： Craniospinal irradiation Volumetric modulated arc therapy Pediatrics workflow optimization

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Private Hospital workflow optimization via Secure k-Means Clustering

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JOURNAL OF MEDICAL SYSTEMS 2020年第1期44卷 8-8页

作者： Spini, Gabriele van Heesch, Maran Veugen, Thijs Chatterjea, Supriyo TNO Unit ICT The Hague Netherlands CWI Dept Cryptol Amsterdam Netherlands Philips Res Data Sci Grp Eindhoven Netherlands

Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure solution to analyze the joined location data of patients and staff, by means of an innovative cryptographic technique called Secure Multi-Party Computation, in which an additional entity that the staff members can trust, such as a labour union, takes care of the staff data. The hospital, owning location data of patients, and the labour union perform a two-party protocol, in which they securely cluster the staff members by means of the frequency of their patient facing times. We describe the secure solution in detail, and evaluate the performance of our proof-of-concept. This work thus demonstrates the feasibility of secure multi-party clustering in this setting.

关键词： Secure multi-party computation Hospital workflow optimization Privacy Real-time locating system Clustering k-means

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The many faces of data-centric workflow optimization: a survey

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018年第2期6卷 81-107页

作者： Kougka, Georgia Gounaris, Anastasios Simitsis, Alkis Aristotle Univ Thessaloniki Thessaloniki Greece HP Labs Palo Alto CA USA

workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a key aspect is data passing through and getting manipulated by a sequence of steps. The large volume and variety of data, the complexity of operations performed, and the long time such workflows take to compute give rise to the need for optimization. In general, data-centric workflow optimization is a technology in evolution. This survey focuses on techniques applicable to workflows comprising arbitrary types of data manipulation steps and semantic inter-dependencies between such steps. Further, it serves a twofold purpose: firstly, to present the main dimensions of the relevant optimization problems and the types of optimizations that occur before flow execution and secondly, to provide a concise overview of the existing approaches with a view to highlighting key observations and areas deserving more attention from the community.

关键词： Data flows workflow optimization workflow management systems Data analysis Data science

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workflow optimization in PAW 37

Workflow Optimization in PAW

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37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Filatov, Maxim Kantere, Verena Kiringa, Iluju Univ Geneva Geneva Switzerland Natl Tech Univ Athens Athens Greece Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9781538617915

Many industrial applications, from domains such as telecommunication, web and sales, require to perform complex analytics across several data processing systems. The performance of such analytics is usually expressed in workflows, and it is a task that is both labor-intensive and time-consuming. At the same time, with increasing amounts of data to be analysed, the optimization of analytics workflows becomes crucial for satisfying business objectives. This paper focuses on workflow optimization with respect to time efficiency, over multiple execution engines, such as a traditional DBMS, a MapReduce engine, and a scripting engine. This configuration is emerging as a common paradigm used to combine analysis of unstructured and structured data. We propose a novel optimization technique as part of our system called PAW (Platform for Analytics workflows). This technique creates alternative workflow structures and their execution plans based on equivalent combinations and orders of operators. The technique employs an exhaustive and a heuristic algorithm to search efficiently the space of equivalent workflow structures and select the one with the optimal execution plan. We present a thorough experimental study and we showcase the efficiency of the proposed optimization technique in a fully fledged multi engine system, applied on three real-world applications and their data, as well as on a synthetic benchmark.

关键词： Analytics workflows Multi-engine Systems workflow optimization

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Design of a workflow optimization System for Urban Water Utility Maintenance

Design of a Workflow Optimization System for Urban Water Uti...

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Systems and Information Engineering Design Symposium (SIEDS)

作者： Khan, Adnan Orozco, Rebekah Khattab, Mohamed Alqahtani, Fahad Laboratoire Recherche Informatique (LRI-CNRS UMR 8623) Université Paris-Sud Orsay France Laboratoire d’Informatique Université Paris Descartes Paris France Department of Information Engineering Wuhan University of Technology Wuhan Hubei China

ISBN: (纸本)9781538618486

The Water Distribution System (WDS) in the District of Columbia is one of the most complex in the world, spanning 1,440 dense urban miles. Providing water to 700,000 residents and 16.6 million annual visitors, the system consists of approximately 40,000 valves, 9,500 fire hydrants and 130,000 service connections. Originally developed in the 1800s, the median age of pipe in the WDS has reached 78 years. Ruptures in water main pipes carrying over 200 psi of pressure can buckle roadways, flood streets and cause widespread service disruptions. In winter months, this can create icy conditions which are extremely hazardous. The District's water utility reports approximately 500 water main breaks per year. Since the implementation of a priority system designed to expedite processing of high-impact emergency failures, the utility's operational data has identified high response times for incidents classified as low priority. The average is approximately 84 days, nearly six times higher than emergency incidents. The scheduling phase of the workflow is responsible for 98 percent of the total delay, indicating the need for a streamlined scheduling methodology. Emergency incident volume increases twofold in winter months, with sporadic failures caused by temperature fluctuations. This has forced the utility to increase reliance on contracted crews by 20 percent, and indicated an urgent need for weather-dependent failure forecasting tools. A queueing model of the utility's workflow was used to derive standard scheduling protocols for groups of low priority incident types. Results indicate that achieving performance goals is feasible without additional resources. Recommended workflow configurations and operational improvements reduced the low priority response time to 12.4 days. Additionally, an Artificial Neural Network (ANN) was trained and deployed to generate main break forecasts for scheduling purposes. Validation tests using the utility's observed main breaks reported R-2 =

关键词： Water Distribution System Failure Prediction workflow optimization Main Breaks Water Utility Priority System Artificial Neural Network

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