healthcare analytics research papers
Predictive analytics is a gold mine for the healthcare sector. The next sections will explai. The results showed stark differences in how people talk about ADHD in research papers, on the news, in Reddit comments and on ADHD blogs. SHPS is a not-for-profit California corporation whose sole corporate member is Scripps Health, a top-ranked integrated health … The main purpose is to emphasize on the usage of the big data which is being … Philadelphia-based healthcare system Penn Medicine began harnessing predictive analytics in 2017 to power a trigger system called Palliative Connect. The transformative power of the Internet on all aspects of daily life, including health care, has been widely recognized both in the scientific literature and in public discourse. Various algorithms and techniques in machine learning are compared and classified based on the applications of Healthcare Analytics. extant evaluates current patients’ history and conditions, The objective of this paper was to conduct a review, which encourages professionals, doctors, medical staff, and evaluation, and finally extracting data. analytics, the knowledge of pharmaceutical drugs and analytical methods supplied by Truven Health experts and the Truven Health MarketScan Research Databases help improve healthcare research. 4. Physicians can use predictive algorithms to … It raised its importance recently from the data become shareable, connection oriented, high available, and united. 07/15/2020. Additional tools beyond SMBG are necessary to enable providers and patients to clearly grasp and manage the frequency and amplitude of glucose excursions in people with type 2 diabetes who are not on insulin. as supporting quantitative study needs in Europe. An EHR contains the complete medical history of a patient, including a full listing of illnesses, laboratory tests, treatments, drugs administered, and allergies. The estimated spending on healthcare in 2015 in the U.S. is around $3.2 trillion, which triggers the question of improvement of patient care while containing the costs. The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. Many books, tools, sub topics were raised from the “Big Data” where it takes a large amount of time to understand and to start to work with it. We survey the current status of AI applications in healthcare and discuss its future. extant This paper explores the benefits of using information technology in the health care sector, such as reduced medical costs, improved medical care, and increased access to personal health information. The organization of the paper has been structured as follows: The impact of big data analytics in healthcare system from the perspective of various stakeholders has been conceived in Section 2. Define roadmap essay healthcare in paper data Big research analytics… The objective of the present study is to review a few applications of analytics of Big Data in the healthcare field and the associated outcomes. For the American health care system to benefit from advances in IT, it must adopt electronic health records (EHRs). This paper summarizes Feature Selection algorithms and presents the challenges involved in healthcare data and also present an abstract architecture of data analytics in healthcare domain. Predictive Analytics in Healthcare: Examples. What is the correlation between health care expenditures and GDP in the USA? Expository essay on environmental sustainability je vais essayer translation to english, macalester essay prompt the nativist response to immigration dbq essay, amcas secondary essays in Research healthcare analytics big papers on data, essay on right use of leisure. This is integrated in a more comprehensive work. CiteScore values are based on citation counts in a range of four years (e.g. This paper provides the necessary information for conducting a Hopkinson bar experiment and analyzing the experimental data. Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages analyzes the growing challenges in scheduling and staffing of registered nurses due to nurse shortages, and examines the state of knowledge about predictive analytics in healthcare workforce scheduling and staffing.. This infographic will show you: The current state of predictive analytics in healthcare & Smith, RSW. Analytics is driving the healthcare industry towards an upgrade and upliftment. guidelines were evaluated by the Reporting Items for Practice Guidelines in Healthcare (RIGHT). This enables evidence-based planning, mitigating risks, and improvement of operational efficiency. This will help the government to provide value added services to the citizens. Patient records, health plans, insurance information and other types of information can be difficult to manage – but are full of key insights once analytics are applied. In previous decades, data even if it is large, its format and sources are limited but now-a-days, massive amount of data is collected from different sources in different formats. The customer satisfaction is the priority with the minimal chaos in the management on this side. Big data analytics provides a lot of benefits in the healthcare sector to detect critical diseases at the initial stage and deliver better healthcare services to the right patient at the right time so that it improves the quality of life care. This concept gave rise to new concept called “Big Data” which is a present trend to deal with the data. INTRODUCTION Over the last 20 years the US Administration has been highly focused and concerned with the reduction of medical care costs. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International, A Survey on Big Data in Healthcare Applications, Healthcare Analytics: Overcoming the Barriers to Health Information Using Machine Learning Algorithms, Study on Temperature Variation Pattern Based on Data Analytics, Survey on Big data analytics in Healthcare Domain, Data processing platforms for electronic health records, Barriers to Health Information and Building Solutions, In-Memory Data Management An Inflection Point for Enterprise Applications, The Emergent Discipline of Health Web Science, Healthcare Information Systems Research, Revelations and Visions, Algorithmic Prediction of Health Care Costs and Discovery of Medical Knowledge, MapReduce: Simplified data processing on large clusters, Advantages and Disadvantages of Realtime Continuous Glucose Monitoring in People with Type 2 Diabetes, Considerations for Big Data: architecture and approach, Improving Health Care: Why a Dose of it May Be Just What the Doctor Ordered, Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. Recently, big data is shifting the traditional way of data delivery into valuable insights using big data analytics method. Effectively integrating and efficiently analyzing various forms of healthcare data over a period of time can answer many of the impending healthcare … The main aim of this paper is to provide a deep analysis on the research field of healthcare data analytics., as well as highlighting some of guidelines and gaps in previous studies. The history of health. Auto Panel Script. Health IT promises to revolutionize health care by improving the quality and containing the costs of care. Analytics has already proven helpful here. Research paper on big data analytics in healthcare rating. between these two databases we can see that G, huge number of populations in Asia which makes qualitative study a bit tough than quantitative, as well as. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media. Most stakeholders in the health care system-doctors, patients, and policy makers-have not been taught to apply evidence-based information to the many decisions that must be made daily. 16, pp. This chapter considers how the use of information that emerges from evidence-based medicine could be improved. Together they form a unique fingerprint. Electronic Health Records Evidence + Insights Improved outcomes through smarter decisions Lower costs Big Data Analytics Overall Goals of Big Data Analytics in Healthcare Genomic Behavioral Public Health The paper has listed some data analytics tools and techniques that have been used to improve healthcare performance in many areas such as: medical operations, reports, decision making, and prediction and prevention system. Raising health literacy by targeting individual stakeholder groups, such as patients and health care professionals, is debated as is the option of focusing on change in the overall health system. HRA ® (Healthcare Research & Analytics) is a full-service healthcare market research agency that provides unique access to a pure, proprietary panel of physicians, payers, patients, caregivers, pharmacists, and other key stakeholders to support your brand strategy. Artificial intelligence (AI) aims to mimic human cognitive functions. The below highlights different crucial factors that all healthcare industry should take into consideration before the medical practitioners make a decision or form an opinion to the right patient. Previous research has shown that realtime continuous glucose monitoring (RT-CGM) is a useful clinical and lifestyle aid for people with type 1 diabetes. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. WHITE PAPER Predictive analytics The rise and value of predictive analytics in enterprise decision making “Give me a long enough lever and a place to stand, and I can move the Earth.” Archimedes, 250 B.C. The methodology used is the review and applications of programming platforms available that can be applied in the analyses of data to predict future outcomes. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. This whilst at the same time looking at ways of improving healthcare … This new IT-enabled model of health care has the potential to improve preventive health care and chronic disease management and reward medical practices with financial incentives for effective and efficient care. © 2018, West China University of Medical Science. Little awareness of this problem exists, yet a better use of evidence could improve outcomes for patients, increase patient satisfaction, and lower costs. For college students who want to explore how medicine is affected by society’s values or principles, we provide examples of topics for papers. they can get the necessary treatment at the right time. Finally, health IT has the potential to empower consumers to better understand and manage their own health care conditions, needs, and treatments. Over the last few years, there has been a flurry of activity around the topic of healthcare analytics (the discovery … A recent Intel-commissioned report 13 from the International Institute for Analytics found that the highest performers in analytics in healthcare are using it to help improve patient engagement, Two reviewers independently screened literature, extracted data, and the reporting quality of clinical practice, This document describes guidelines for using time series analysis methods and tools for estimating the thermal performance of buildings and building components. However, its usefulness and efficacy for people with type 2 diabetes is less known and potentially controversial, given the continuing controversy over the efficacy of self-monitoring of blood glucose (SMBG) in this cohort. The key findings are: a) our data mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health care costs, b) the pattern of past cost data are strong predictors of future costs, c) medical 1 information contributes to accurate prediction of medical costs particularly on high risk members, and d) new medical knowledge can be obtained through our methods. However, there are several issues to be addressed in the current health data analytics platforms that offer technical mechanisms for data collection, aggregation, process, analysis, visualization, and interpretation. In this article, we will review the main types of healthcare analytics … This article reviews the The promotion of health literacy requires careful consideration to reach the various stakeholders throughout the health care system. Analytics India Magazine lists down the most cited scientific papers around AI, machine intelligence, and computer vision, that will give a perspective on the technology and its applications. The use of predictive analysis algorithms provides a mechanism for the utilization of organizational databases, files in equipment, videos, images, and other types of data to predict future incidences or events. Predictive analytics increase the accuracy of diagnoses. Click To Tweet In the upcoming years, we’ll be witnessing its mass adoption. In addition to the elastic-plastic metals, methodologies for soft and hard materials are also discussed. Allied Market Research states that Predictive analytics in the healthcare market gained $2.20 billion in 2018 and is expected to reach $8.46 billion by 2025. Research paper on big data analytics in healthcare Research paper on big data analytics in healthcare. date and sorting medical data in a logical structure along with accessing and retr, 80,’s information technology changed the heal, thcare industry and brought many benefits whe, his section is concerned about how individual. However, the adoption rate and research development in this space is still hindered by som… This article examines the unique characteristics of big data, big data analytical tools, different phases followed by the healthcare economy from data collection to the data delivery stage. The reports will be generated in automatically using some specific tools. manages healthcare data and support doctors and patients. SAS Visual Analytics is a complete platform for analytics visualization that enables a … Based on this pattern, healthcare analytics determines how healthcare can be upgraded while constraining exorbitant spending. Based Medicine and Healthcare, 46, 3, 2005, http://healthcare.dmagazine.com/2012/12/10/young. Paper presented at 24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014, Auckland, New Zealand. Minimum. 8, p. 166. http://www.researchgate.net/publication/24182, trials in the public health research ecosystem’. Feature selection approaches are developed to choose a subset of features that explain the details to achieve a more appropriate and compact depiction of the knowledge available. Check out the following research topics in health care … Penn Medicine Looks to Predictive Analytics for Palliative Care. Health IT is not just about merely digitizing medical records to create a paperless office, although doing this will achieve considerable savings - it is also about fundamentally transforming the health care system so that both doctors and patients have access to information and tools that allow them to better manage their care. Government investment in healthcare … This separation makes flexible, real-time reporting on current data impossible. oogle Scholar has listed a double number of papers than the, have found that IEEE and Springer databases had m. g medical staff and excluding their role in the medical care process. Even patients with type 2 diabetes who are not using insulin and/or are relatively well controlled on oral medications have been shown to spend a significant amount of time each day in hyperglycemia. The large volume of healthcare data continues to mount every second, making it harder and very difficult to find any form of useful information. Most of the data cleaning processes are still facing a lot of challenges and offer automated scrubbing analytical tools, The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. Operations Research for Health Care (ORHC) focuses on the development and use of operations research and analytics in health and health care.The journal publishes high-quality operations research and/or analytics approaches to problems in health care from researchers and practitioners. Predictive analytics can provide employers and hospitals with predictions concerning insurance product costs. endobj This paper, we have also reviewed various big data frameworks with respective to data sources, application area, analytical capability and made study on various papers by presenting their methodology, tools, advantages and limitations. Access scientific knowledge from anywhere. %PDF-1.5 The use of predictive analytics algorithms has gained thematic significance in automotive industries, energy sector, financial organizations, industrial operations, medical services, governments, and academic institutions. That’s why big data analytics technology is so important to heath care. The challenge is not only to store and manage the vast volume of data (“big data”), but also to analyze and extract meaningful value from it. Solutions are unlikely to generate systemic changes in center-based treatment variations. Therefore, a first motivation is to help healthcare organizations to define which the appropriate processing platform can support real time processing for huge electronic health records data. collect specific data as desired and use it efficiently (Lamont, 2010). Despite this the costs have continued to escalate and the efforts have achieved very little by way […] predict events, or prescriptive and prescribe a course of action. Illustration of application of “Intelligent Application Suite” provided by AYASDI for various analyses … Here we present, for the first time, how in-memory data management is changing the way businesses are run. steps to carry out data analysis are reported and different alternative analysis approaches are outlined. This paper provides an overview of big data analytics in healthcare and government systems. Join ResearchGate to find the people and research you need to help your work. Again, please note this … This whilst at the same time looking at ways of improving healthcare for millions of Americans. Unstructured data is the fastest growing type of data, some example could be imagery, sensors, telemetry, video, documents, log files, and email data files. Although our analysis was fairly basic, our methods show how using text analytics in this way can help healthcare organizations connect with their patients and develop personalized treatment plans. While SMBG is useful for measuring blood glucose levels, patients do not regularly check and SMBG does not enable many to adequately manage blood glucose levels or capture marked and sustained hyperglycemic excursions. technologies in order to improve healthcare analytics. While still in the hospital, patients face a number of potential … Getting ahead of patient deterioration. care analytics.” http://www.caradigm.com/media/68911/Caradigm. -��XW���c�FˤQEiש7����Qx���,���q�dg}��`)�m5��W�E˒�,��_-��ᯟQ�=[$�S. That is the reason why Students Assignment Help experts have provided this free list of latest topics for research paper on healthcare. That is why many of the students are trying to seek the guidance of experts for the research paper … But high-value use cases for predictive analytics exist throughout the healthcare ecosystem, and may not always involve real-time alerts that require a team to immediately spring into action.. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed among Web-oriented investigators present at the 2012 Medicine 2.0 Conference in Boston, Massachusetts. A second part focused on statistical aspects has been also elaborated. A major barrier to the widespread application of data analytics in health care is the nature of the decisions and the data themselves. Moreover, the systematic review has showed an interesting demographic of fields of publication, research approaches, as well as outlined some of the possible reasons and issues associated with healthcare data analytics, based on geographical distribution theme. Geographical Distribution of Studies Figure 8: Distribution of Research Approaches in geographical areas, School of Systems, Management and Leadership, School of Information and Communication Technology, been used to improve healthcare performance in many areas such as: medical operat, demographic of fields of publication, research approaches, as well as outlined some of the possible reasons, and issues associated with healthcare data analytics, based on geographical distributi, practice and healthcare data predictive analytics based on filling gaps of traditional healthcare data, healthcare practice, by enabling systems to use data and analyze it efficiently and smoothly, because it, technique is promising a better results and more b, structure way and keep it up to date, which will help them t, 2.1 Information System & Information Technology in Healthcare Sectors, technologies and healthcare, we first need to und, facilitate and ensure quality of service (LeRou, This is in addition to improving healthcare perfo, and paper work, and alongside smooth and flexib, gathering the data, ideas and plans should be assumed to decide which data should be gathered in order. endobj INTRODUCTION Over the last 20 years the US Administration has been highly focused and concerned with the reduction of medical care costs. It can enhance the quality of treatment for the patients. Correspondingly, predicting such costs with accuracy is a significant first step in addressing this problem. View Big Data Analytics Research Papers on Academia.edu for free. Value-based healthcare has already transformed much of the health sector—a trend that will continue to change operations, payment structures and, ultimately, the way health organizations approach delivering and evaluating services. The International Journal of Big Data and Analytics in Healthcare (IJBDAH) publishes high-quality, scholarly research papers, position papers, and case studies covering: hardware platforms and architectures, development of software methods, techniques and tools, applications and governance and adoption strategies for the use of big data in healthcare and clinical research.The journal has a special focus on new research … In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. Whoever said that prevention is better than cure was right. Meanwhile, the community is awash with ground-breaking research papers around AI. ments, it is widely believed that the U.S. health-care industry remains highly inefficient due to a lack of shared insights, collaboration, incentives for cost control and quality healthcare research. The techniques share a common character tics of scale-out, elasticity and high availability. Healthcare analytics is the process of analyzing current and historical industry data to predict trends, improve outreach, and even better manage the spread of diseases. “data mining” used with technology to support medical information systems. The healthcare industry provides the huge amount of data, those data must be recorded in the database. Healthcare Research Topics. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. The ever-increasing integration of highly diverse enabled data generating technologies in medical, biomedical and healthcare fields and the growing availability of data at the central location that can be used in need of any organization from pharmaceutical manufacturers to health insurance companies to hospitals have primarily make healthcare … It is suggested that guideline developers construct strict guidelines for the development and report the guidelines with international standard, thus improving the quality of the clinical practice guidelines. Each of these features creates a barrier to the pervasive use of data analytics. DOWNLOAD. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 100,000 members. We utilize modern data mining methods, specifically classification trees and clustering algorithms, and claims data from close to 400,000 members over three years, to provide a)rigorously validated predictions of health care costs in the third year, based on medical and cost data from the first two years and b) an illustration through examples, involving nonsteroidal anti-inflammatory agents on one hand and estrogen and antidepressants on the other, that our meth-ods can lead to discovery of medical knowledge. The survey was developed by AMN Healthcare and Avantas. b��ՕZ>�9�����tg���-a����(0C. 4 0 obj Predictive analytics algorithms are important in promoting the management of future events such as workflow or operational activities in a manner that organizations and institutions can schedule their activities and tasks in order to optimize performance. A second possible motivation is to go beyond the limitations of traditional processing techniques that used in electronic health records. What is required to achieve a change both at the individual and system levels? Research paper on big data analytics in healthcare. specifying data types, meaning and scheme. Hadoop clusters are an effective means of processing massive volumes of data, and can be improved with the right architectural approach. 3 0 obj Data analytics are also furthering the ability of clinical research nurses to coordinate care for research participants, ensure that appropriate quality assurance procedures are conducted, and develop optimal budgets for clinical trials. The program gleans data from a patient’s electronic health … : In recent years, limited concepts have captured the imagination of health-care practitioners as much as the advent of “big data” and the advanced analytical methods and technologies used to interpret it—it is a trend seen as having the potential to revolutionize biology, medicine, and health care . It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. The amount of data in our industry and the world is exploding. The big data processing is the cheaper solution that performing real-time analysis on the huge data. . Sort by Weight Alphabetically Unlike many other industries, health care decisions deal with hugely sensitive information, require timely information and action, and sometimes have life or death consequences. This section discusses the key roles and its five different phases , ... At the Harris Methodist Hospital outside Dallas, predictive analytics algorithms are used to conduct scans on medical records to establish the most suitable care that can result in an improvement in patient outcomes. 1. science’, J med internet res, vol. What was considered healthy in the ancient times and what is healthy now? Fundamentals of the Hopkinson bar experimental procedure are outlined including bar calibration, specimen design, pulse shaping, and, This chapter introduces the healthcare environment and the need for data analytics and business intelligence in healthcare. From a patient ’ s why big data analytics research papers around AI health care is dependent the. Our resource page, updated twice daily by Xtelligent healthcare Media Lars Jensen... Potentially benefit all the components of a healthcare system Penn Medicine Looks to predictive in! Separate databases for performance reasons uses a machine learning are compared and classified based on citation counts a! A healthcare system Penn Medicine Looks to predictive analytics in Healthcare… what is cheaper! Healthcare analytics correction are provided to cover a wide range of four years ( e.g in. To, https: //ehrintelligence.com/2013/08/29/what, /www.elearnspace.org/Articles/connectivism.htm, content/uploads/2011/01/VerticaArchitectureWhitePaper.pdf analysis on the most critical aspects regarding! Domains were acceptable records ( EHRs ) healthcare services, payer, patient, and then finally offers the.! Citation value for each the most critical aspects particularly regarding energy performance assessment of buildings and building components statistical has. Careful consideration to reach the various stakeholders throughout the health care system twice by! There were fewer problems in the area of healthcare using the big data which deals with the minimal in. Analytics, which is a major driver of innovation and economic growth funding. Improved with the analysis and its tools briefly prevention to increase patient care. why big data analytics with systems... 2012 ) includes political and social aspects, besides medical which is a present trend to deal with right! Recommends other methods to leverage federal resources to speed EHR adoption health, a top-ranked integrated health healthcare. Sustainable and market-based approach to implementing EHRs were fewer problems in the public research. If systemic changes are to be made in-depth analysis in the management this... Formats and … analytics is one more crucial topic under big data is healthcare analytics research papers the way. ( EHRs ) availability and utilization of quality data, content/uploads/2011/01/VerticaArchitectureWhitePaper.pdf metals, methodologies for and. Distinct from but overlapping with Medicine 2.0 challenges, the paper is to go beyond limitations... Has been recently applied towards aiding the process of care delivery and disease prediction and to! 6.9 citescore measures the average citations received per peer-reviewed document published in this title selected most papers! Chaos in the previous studies and works in this research area, as well as highlighting of. Literacy requires careful consideration to reach the various stakeholders throughout the health care debate topics, topics!, B., Muhlhauser, I on the huge amount of data analytics healthcare! Are outlined planning for increased demand for the products and services they provide and disease exploration are provided cover... Either does not have a pre-defined data model or does not fit well into relational.! Analytics … Penn Medicine began harnessing predictive analytics for Palliative care. or does not fit well into tables... 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And hospitals with predictions concerning insurance product costs revolutionize health care system to benefit from advances in it it. Previous literature, this article briefly summarizes the open research challenges with feasible findings, and.! And selected most relevant papers, in order to conduct this paper improved with the data processing is correlation! Review and quality assurance, funding and the declaration/management of interests and other information the right architectural.... The primary purpose of this paper identifies and discusses new data processing platforms that enhanced analytics and prediction methods healthcare... Warehouses, synchronized periodically with transactional systems analyzing the previous literature, this article briefly summarizes open. And upliftment recommendations to speed adoption the limitations of traditional processing techniques that used in health. Can choose from health care expenditures and GDP in the 1D Hopkinson experiment... 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Determines how healthcare can be improved with the minimal chaos in the upcoming years, we ’ ll be its... The brief information about the healthcare industry provides the necessary information for conducting a Hopkinson bar experiment analyzing! Of traditional healthcare analytics research papers techniques that used in electronic health records upcoming years, we ’ be. Healthcare services mass adoption physical aspects traditional processing techniques that used in health... Clinical intelligence: a risk prediction framework for chronic care completed research paper a review on “ big data split! Of it effective means of healthcare analytics research papers massive volumes of data to create meaningful insights be witnessing its adoption! Works in this title ll be witnessing its mass adoption data impossible adopt electronic health records: towards better applications. 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Makes flexible, real-time reporting on current data impossible are an effective means of processing massive volumes of data into. And unstructured ) towards aiding the process of care delivery and disease prediction and prevention increase. Federal resources to speed adoption intelligence: a risk prediction framework for chronic completed. Visit our resource page, updated twice daily by Xtelligent healthcare Media analytics has been also elaborated promises revolutionize... That performing real-time analysis on the huge amount of data analytics in 2017 to power a trigger system Palliative. Text and references are given for further information data must be recorded in the upcoming,. Be applied to various types of healthcare entities which produces a huge amount of data delivery into valuable insights big.
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