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| business intelligence in healthcare industry: Theory and Practice of Business Intelligence in Healthcare Khuntia, Jiban, Ning, Xue, Tanniru, Mohan, 2019-12-27 Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration. |
| business intelligence in healthcare industry: Healthcare Business Intelligence Laura Madsen, 2012 This book will be constructed as a guidebook for healthcare organizations that are attempting BI/DW. It will address the primary functions of a business intelligence capability and how BI can ease the increasing regulatory reporting pressures on all healthcare organizations. Also included will be tables, checklists and a few forms. Tenative chapter contents: Chapter 1: What is Healthcare BI? Chapter 2: The Five Disciplines of Business Intelligence Chapter 3: The Importance of ETL Chapter 4: Starting with Data Governance Chapter 5: Creating a BI team Chapter 6: Data Modeling for Healthcare Chapter 7: Gaining Support for your BI program Chapter 8: Ensuring good User Adoption Chapter 9: Marketing Your BI Program Chapter 10: Maintaining Your BI Program-- |
| business intelligence in healthcare industry: Healthcare Business Intelligence Laura Madsen, 2012-07-20 Solid business intelligence guidance uniquely designed for healthcare organizations Increasing regulatory pressures on healthcare organizations have created a national conversation on data, reporting and analytics in healthcare. Behind the scenes, business intelligence (BI) and data warehousing (DW) capabilities are key drivers that empower these functions. Healthcare Business Intelligence is designed as a guidebook for healthcare organizations dipping their toes into the areas of business intelligence and data warehousing. This volume is essential in how a BI capability can ease the increasing regulatory reporting pressures on all healthcare organizations. Explores the five tenets of healthcare business intelligence Offers tips for creating a BI team Identifies what healthcare organizations should focus on first Shows you how to gain support for your BI program Provides tools and techniques that will jump start your BI Program Explains how to market and maintain your BI Program The risk associated with doing BI/DW wrong is high, and failures are well documented. Healthcare Business Intelligence helps you get it right, with expert guidance on getting your BI program started and successfully keep it going. |
| business intelligence in healthcare industry: Healthcare Business Intelligence, + Website Laura Madsen, 2012-09-04 Solid business intelligence guidance uniquely designed for healthcare organizations Increasing regulatory pressures on healthcare organizations have created a national conversation on data, reporting and analytics in healthcare. Behind the scenes, business intelligence (BI) and data warehousing (DW) capabilities are key drivers that empower these functions. Healthcare Business Intelligence is designed as a guidebook for healthcare organizations dipping their toes into the areas of business intelligence and data warehousing. This volume is essential in how a BI capability can ease the increasing regulatory reporting pressures on all healthcare organizations. Explores the five tenets of healthcare business intelligence Offers tips for creating a BI team Identifies what healthcare organizations should focus on first Shows you how to gain support for your BI program Provides tools and techniques that will jump start your BI Program Explains how to market and maintain your BI Program The risk associated with doing BI/DW wrong is high, and failures are well documented. Healthcare Business Intelligence helps you get it right, with expert guidance on getting your BI program started and successfully keep it going. |
| business intelligence in healthcare industry: Applying Business Intelligence Initiatives in Healthcare and Organizational Settings Miah, Shah J., Yeoh, William, 2018-07-13 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings incorporates emerging concepts, methods, models, and relevant applications of business intelligence systems within problem contexts of healthcare and other organizational boundaries. Featuring coverage on a broad range of topics such as rise of embedded analytics, competitive advantage, and strategic capability, this book is ideally designed for business analysts, investors, corporate managers, and entrepreneurs seeking to advance their understanding and practice of business intelligence. |
| business intelligence in healthcare industry: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| business intelligence in healthcare industry: Applying Business Intelligence to Clinical and Healthcare Organizations Machado, José, Abelha, António, 2016-02-10 Business intelligence (BI) tools are capable of working with healthcare data in an efficient manner to generate real-time information and knowledge relevant to the success of healthcare organizations. Further, BI tools benefit healthcare professionals making critical decisions within hospitals, clinics, and physicians’ offices. Applying Business Intelligence to Clinical and Healthcare Organizations presents new solutions for data analysis within the healthcare sector in order to improve the quality of medical care and patient quality of life. Business intelligence models and techniques are explored and their benefits for the healthcare sector exposed in this timely research-based publication comprised of chapters written by professionals and researchers from around the world. Hospital administrators, healthcare professionals, biomedical engineers, informatics engineers, and students in graduate-level healthcare management programs will find this publication essential to their professional development and research needs. |
| business intelligence in healthcare industry: Business Intelligence in Healthcare with IBM Watson Analytics Shilpa Balan, 2017-09-06 Healthcare generates enormous amounts of data. Left unstructured and unanalyzed, this data does little to help hospitals and healthcare facilities offer better care or develop more effective practices. Big data isn't an issue confined to the healthcare industry-many other industries struggle to capitalize on the possibilities lurking within unmined data. In response to this need, analytical software such as IBM(R) Watson(TM)Analytics has become vital for long-term success. Working with a sample Medicaid data set, business intelligence experts Dr. Shilpa Balan and Dr. Joseph Otto reveal how to use IBM Watson Analytics to analyze healthcare data and transform unstructured data into actionable information. Balan and Otto break down the complex task of data analysis into easily understood sections, demonstrating vital aspects of data set analysis, such as creating a cloud-based data environment, data refinement, data exploration, predictive analytics, dashboard building, and social media data analysis. Used correctly, healthcare data sets can provide valuable insight into costs and claims, research and development, patient behavior, and medical records. Let Balan and Otto provide your facility with the data analysis techniques and skills needed to uncover the secrets of your facility's data collection. |
| business intelligence in healthcare industry: Analytics in Healthcare and the Life Sciences Dwight McNeill, Thomas H. Davenport, 2014 Make healthcare analytics work: leverage its powerful opportunities for improving outcomes, cost, and efficiency.This book gives you thepractical frameworks, strategies, tactics, and case studies you need to go beyond talk to action. The contributing healthcare analytics innovators survey the field's current state, present start-to-finish guidance for planning and implementation, and help decision-makers prepare for tomorrow's advances. They present in-depth case studies revealing how leading organizations have organized and executed analytic strategies that work, and fully cover the primary applications of analytics in all three sectors of the healthcare ecosystem: Provider, Payer, and Life Sciences. Co-published with the International Institute for Analytics (IIA), this book features the combined expertise of IIA's team of leading health analytics practitioners and researchers. Each chapter is written by a member of the IIA faculty, and bridges the latest research findings with proven best practices. This book will be valuable to professionals and decision-makers throughout the healthcare ecosystem, including provider organization clinicians and managers; life sciences researchers and practitioners; and informaticists, actuaries, and managers at payer organizations. It will also be valuable in diverse analytics, operations, and IT courses in business, engineering, and healthcare certificate programs. |
| business intelligence in healthcare industry: Data-Driven Healthcare Laura B. Madsen, 2014-09-23 Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data more effectively Find out how Big Data has transformed other industries, and how it applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transforming the Industry provides tables, checklists, and forms that allow you to take immediate action in implementing BI in your organization. You can't afford to be behind the curve. The industry is moving on, with or without you. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry is your guide to utilizing data to advance your operation in an industry where data-fueled growth will be the new norm. |
| business intelligence in healthcare industry: Data-Driven Healthcare Laura B. Madsen, 2014-10-27 Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data more effectively Find out how Big Data has transformed other industries, and how it applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transforming the Industry provides tables, checklists, and forms that allow you to take immediate action in implementing BI in your organization. You can't afford to be behind the curve. The industry is moving on, with or without you. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry is your guide to utilizing data to advance your operation in an industry where data-fueled growth will be the new norm. |
| business intelligence in healthcare industry: Transforming Healthcare Analytics Michael N. Lewis, Tho H. Nguyen, 2020-03-24 Real-life examples of how to apply intelligence in the healthcare industry through innovative analytics Healthcare analytics offers intelligence for making better healthcare decisions. Identifying patterns and correlations contained in complex health data, analytics has applications in hospital management, patient records, diagnosis, operating and treatment costs, and more. Helping healthcare managers operate more efficiently and effectively. Transforming Healthcare Analytics: The Quest for Healthy Intelligence shares real-world use cases of a healthcare company that leverages people, process, and advanced analytics technology to deliver exemplary results. This book illustrates how healthcare professionals can transform the healthcare industry through analytics. Practical examples of modern techniques and technology show how unified analytics with data management can deliver insight-driven decisions. The authors—a data management and analytics specialist and a healthcare finance executive—share their unique perspectives on modernizing data and analytics platforms to alleviate the complexity of the healthcare, distributing capabilities and analytics to key stakeholders, equipping healthcare organizations with intelligence to prepare for the future, and more. This book: Explores innovative technologies to overcome data complexity in healthcare Highlights how analytics can help with healthcare market analysis to gain competitive advantage Provides strategies for building a strong foundation for healthcare intelligence Examines managing data and analytics from end-to-end, from diagnosis, to treatment, to provider payment Discusses the future of technology and focus areas in the healthcare industry Transforming Healthcare Analytics: The Quest for Healthy Intelligence is an important source of information for CFO’s, CIO, CTO, healthcare managers, data scientists, statisticians, and financial analysts at healthcare institutions. |
| business intelligence in healthcare industry: Adaptive Business Intelligence Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac, 2006-12-02 Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling. |
| business intelligence in healthcare industry: Modeling Risk Johnathan Mun, 2006-07-21 This completely revised and updated edition of Applied Risk Analysis includes new case studies in modeling risk and uncertainty as well as a new risk analysis CD-ROM prepared by Dr. Mun. On the CD-ROM you'll find his Risk Simulator and Real Options Super Lattice Solver software as well as many useful spreadsheet models. Johnathan Mun's book is a sparkling jewel in my finance library. Mun demonstrates a deep understanding of the underlying mathematical theory in his ability to reduce complex concepts to lucid explanations and applications. For this reason, he's my favorite writer in this field. —Janet Tavakoli, President, Tavakoli Structured Finance, Inc. and author of Collateralized Debt Obligations and Structured Finance A must-read for product portfolio managers . . . it captures the risk exposure of strategic investments, and provides management with estimates of potential outcomes and options for risk mitigation. —Rafael E. Gutierrez, Executive Director of Strategic Marketing and Planning, Seagate Technology, Inc. Once again, Dr. Mun has created a 'must-have, must-read' book for anyone interested in the practical application of risk analysis. Other books speak in academic generalities, or focus on one area of risk application. [This book] gets to the heart of the matter with applications for every area of risk analysis. You have a real option to buy almost any book?you should exercise your option and get this one! —Glenn Kautt, MBA, CFP, EA, President and Chairman, The Monitor Group, Inc. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. |
| business intelligence in healthcare industry: Demystifying Big Data and Machine Learning for Healthcare Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz, 2017-02-15 Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them. |
| business intelligence in healthcare industry: Artificial Intelligence and Big Data Analytics for Smart Healthcare Miltiadis Lytras, Akila Sarirete, Anna Visvizi, Kwok Tai Chui, 2021-10-22 Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate. The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry. - Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine - Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them - Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers |
| business intelligence in healthcare industry: Analytics in Healthcare Christo El Morr, Hossam Ali-Hassan, 2019-01-21 This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field. |
| business intelligence in healthcare industry: Progress in Advanced Computing and Intelligent Engineering Khalid Saeed, Nabendu Chaki, Bibudhendu Pati, Sambit Bakshi, Durga Prasad Mohapatra, 2017-12-21 The book focuses on both theory and applications in the broad areas of communication technology, computer science and information security. This two volume book contains the Proceedings of International Conference on Advanced Computing and Intelligent Engineering. These volumes bring together academic scientists, professors, research scholars and students to share and disseminate information on knowledge and scientific research works related to computing, networking, and informatics to discuss the practical challenges encountered and the solutions adopted. The book also promotes translation of basic research into applied investigation and convert applied investigation into practice. |
| business intelligence in healthcare industry: Applications of Artificial Intelligence in Business, Education and Healthcare Allam Hamdan, Aboul Ella Hassanien, Reem Khamis, Bahaaeddin Alareeni, Anjum Razzaque, Bahaa Awwad, 2021-07-12 This book focuses on the implementation of Artificial Intelligence in Business, Education and Healthcare, It includes research articles and expository papers on the applications of Artificial Intelligence on Decision Making, Entrepreneurship, Social Media, Healthcare, Education, Public Sector, FinTech, and RegTech. It also discusses the role of Artificial Intelligence in the current COVID-19 pandemic, in the health sector, education, and others. It also discusses the impact of Artificial Intelligence on decision-making in vital sectors of the economy. |
| business intelligence in healthcare industry: Healthcare Analytics for Quality and Performance Improvement Trevor L. Strome, 2013-10-02 Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement. |
| business intelligence in healthcare industry: Global Business Leadership Development for the Fourth Industrial Revolution Smith, Peter, Cockburn, Tom, 2020-09-25 As the world has adapted to the age of digital technology, present day business leaders are required to change with the times as well. Addressing and formatting their business practices to not only encompass digital technologies, but expand their capabilities, the leaders of today must be flexible and willing to familiarize themselves with all types of global business practices. Global Business Leadership Development for the Fourth Industrial Revolution is a collection of advanced research on the methods and tactics utilized to succeed as a leader in the digital age. While highlighting topics including data privacy, corporate governance, and risk management, this book is ideally designed for business professionals, administrators, managers, executives, researchers, academicians, and business students who want to improve their understanding of the strategic role of digital technologies in the global economy, in networks and organizations, in teams and work groups, in information systems, and at the level of individuals as actors in digitally networked environments |
| business intelligence in healthcare industry: Business Intelligence: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-12-29 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries. |
| business intelligence in healthcare industry: Principles and Applications of Business Intelligence Research Herschel, Richard T., 2012-12-31 This book provides the latest ideas and research on advancing the understanding and implementation of business intelligence within organizations--Provided by publisher. |
| business intelligence in healthcare industry: Internet of Things in Business Transformation Parul Gandhi, Surbhi Bhatia, Abhishek Kumar, Mohammad Ali Alojail, Pramod Singh Rathore, 2021-02-03 The objective of this book is to teach what IoT is, how it works, and how it can be successfully utilized in business. This book helps to develop and implement a powerful IoT strategy for business transformation as well as project execution. Digital change, business creation/change and upgrades in the ways and manners in which we work, live, and engage with our clients and customers, are all enveloped by the Internet of Things which is now named Industry 5.0 or Industrial Internet of Things. The sheer number of IoT(a billion+), demonstrates the advent of an advanced business society led by sustainable robotics and business intelligence. This book will be an indispensable asset in helping businesses to understand the new technology and thrive. |
| business intelligence in healthcare industry: Big Data Analytics in Healthcare Anand J. Kulkarni, Patrick Siarry, Pramod Kumar Singh, Ajith Abraham, Mengjie Zhang, Albert Zomaya, Fazle Baki, 2019-10-01 This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms. |
| business intelligence in healthcare industry: Explainable AI in Healthcare and Medicine Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge, 2020-11-02 This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence. |
| business intelligence in healthcare industry: Artificial Intelligence in Healthcare and Medicine Kayvan Najarian, Delaram Kahrobaei, Enrique Dominguez, Reza Soroushmehr, 2022-04-06 This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor. |
| business intelligence in healthcare industry: Healthcare Analytics Made Simple Vikas (Vik) Kumar, 2018-07-31 Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare. |
| business intelligence in healthcare industry: Big Data and Health Analytics Katherine Marconi, Harold Lehmann, 2014-12-20 This book provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery. Written for health care professionals and executives, this book presents the current thinking of academic and industry researchers and leaders from around the world. Using non-technical language, it includes case studies that illustrate the business processes that underlie the use of big data and health analytics to improve health care delivery. |
| business intelligence in healthcare industry: Hierarchical Decision Modeling Tugrul U. Daim, 2015-07-25 This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into five parts: Technology Policy Planning, Strategic Technology Planning, Technology Assessment, Application Extensions, and Methodology Extensions. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach. Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field. This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students. |
| business intelligence in healthcare industry: Profiles in Performance Howard Dresner, 2009-10-09 Too many organizations invest in performance management and business intelligence projects, without first establishing the needed conditions to ensure success. But the organizations that lay the groundwork for effective change first reap the benefits. In Profiles in Performance: Business Intelligence Journeys and the Road Map for Change, Howard Dresner (author of The Performance Management Revolution) worked with several extraordinary organizations to understand their thriving performance-directed culture. In doing so, he developed a unique maturity model-which served as both a filter to select candidates and as a lens to examine accomplishments. Interviews with people from all sides of the organization: business users, finance, senior management and the IT department Provides a complete picture of their progress from inception to current state The models, analyses and real world accounts from these cases will be an invaluable resource to any organization hoping to improve or initiate their own performance-directed culture. |
| business intelligence in healthcare industry: Artificial Intelligence Sandeep Reddy, 2020-12-02 The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains. |
| business intelligence in healthcare industry: Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare Christoph Thuemmler, Chunxue Bai, 2017-01-07 This book describes how the creation of new digital services—through vertical and horizontal integration of data coming from sensors on top of existing legacy systems—that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world. · Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems; · Provides overviews of technologies in addition to possible application scenarios and market conditions; · Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner. |
| business intelligence in healthcare industry: Third International Congress on Information and Communication Technology Simon Sherratt, Nilanjan Dey, Amit Joshi, 2019 The book includes selected high-quality research papers presented at the Third International Congress on Information and Communication Technology held at Brunel University, London on February 27-28, 2018. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IOT), and e-mining. Written by experts and researchers working on ICT, the book is suitable for new researchers involved in advanced studies. |
| business intelligence in healthcare industry: Data Analytics in Medicine Information Resources Management Association, 2019-11-18 This book examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations-- |
| business intelligence in healthcare industry: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| business intelligence in healthcare industry: Machine Learning and Analytics in Healthcare Systems Himani Bansal, Balamurugan Balusamy, T. Poongodi, Firoz Khan KP, 2021-06-30 This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications. |
| business intelligence in healthcare industry: Deep Medicine Eric Topol, 2019-03-12 A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved. |
| business intelligence in healthcare industry: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| business intelligence in healthcare industry: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
The role of business intelligence tools in improving healthcare …
The role of Business Intelligence tools in the healthcare sector is indispensable. They not only enhance operational efficiency and patient outcomes but also play a critical role in regulatory …
The Impact of Business Intelligence on Healthcare Delivery in …
This paper aims to bring the reader up-to-date with the current literature on two basic topics; business intelligence and healthcare delivery and form the basis for the justification of …
Analysis of Business Intelligence Applications in Healthcare …
Findings from our study provide a foundation for future research agenda on BI in Healthcare. We conclude by highlighting the shortcomings of current BI practice in the HC domain in the …
Tenets of Healthcare BI - Wiley Online Library
To get started, you must focus where BI can provide the organization with the most value. Much of what we discuss from a value perspective concerns the delivery of reports, dashboards, and …
An overview of Business Intelligence research in healthcare ...
With the aim of identifying the most attentive themes, topics, and research priorities, this review endeavors to perform a literature analysis on research articles addressing business …
Review of Business Intelligence Implementation in Healthcare
The use of BI in healthcare, like any other technology-based approach, is to resolve business challenges and streamline operations. As discussed earlier, challenges in the healthcare …
Business Intelligence's Effect On US Healthcare Delivery
of the current literature on two fundamental topics: business intelligence and healthcare delivery. In order to accomplish that, we look at how BI is being used in the healthcare sector, discuss …
90 Healthcare Business Intelligence A Primer - ijtsrd.com
To succeed in a modern digital world, healthcare industry must be data-driven. Hospitals and healthcare institutions desire to make their workflows more efficient in order to meet demand. …
Justifying Business Intelligence Systems Adoption: A Literature …
In the healthcare industry explicitly, data is generated daily through the use of medical devices and other Enterprise Resource Planning (ERP) software or also known as Hospital Information …
Business Intelligence in Healthcare Industry - IJSR
The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; …
BUSINESS INTELLIGENCE FOR HEALTHCARE INDUSTRY
The current paper highlights the advantages of big data analytics and business intelligence in the healthcare industry.
Repository Istituzionale dei Prodotti della Ricerca del …
Business Intelligence (BI) is concerned with the extraction, analysis, and presentation of data to make decisions and improve the management of firms. This becomes particularly relevant in …
An assessment of business intelligence in public hospitals
One sector that generates large amounts of data is the healthcare industry owing to its need to meet requirements related to patient records, compliance, and patient care [4]. Therefore, use …
Implementation of Business Intelligence in an Electronic …
To conduct this type of analysis, healthcare organisations use business intelligence tools to gain data-driven insights. This approach, which aims to deliver improved patient care, is known as …
An Integrated Business Intelligence Framework for Healthcare …
introducing an integrated BI framework for healthcare analytics will lead to better decisions in healthcare organizations which is the main objective of this paper.
Predictive Analytics in Healthcare - Intel
healthcare are using it to help improve patient engagement, population health, quality of care and business operations – areas that map closely to the Quadruple Aims.
The adoption of business intelligence systems in small and …
the deadly global pandemic of COVID-19, healthcare organizations are compelled to embrace new technologies for their routine decision-making. The Business Intelligence System (BIS) is …
Examining the Drivers and Performance Impact of Business …
healthcare industry. Integrating the TOE framework and RBV theory provides a robust analytical framework for examining the internal and external factors that impact BI adoption of business …
Data Warehouse Design Considerations for a Healthcare …
Abstract— This paper deals with the importance of Business Intelligence (BI) in healthcare and its vital perspectives. It is an effort to identify the difference between traditional BI approaches …
The role of business intelligence tools in improving …
The role of Business Intelligence tools in the healthcare sector is indispensable. They not only enhance operational efficiency and patient outcomes but also play a critical role in regulatory …
The Impact of Business Intelligence on Healthcare Delivery …
This paper aims to bring the reader up-to-date with the current literature on two basic topics; business intelligence and healthcare delivery and form the basis for the justification of research …
Understanding business intelligence in the context of …
In today’s fast changing healthcare sector, decision makers are facing a growing demand for both clinical and administrative information in order to comply with legal and customer-specifi c …
Analysis of Business Intelligence Applications in Healthcare …
Findings from our study provide a foundation for future research agenda on BI in Healthcare. We conclude by highlighting the shortcomings of current BI practice in the HC domain in the context …
Tenets of Healthcare BI - Wiley Online Library
To get started, you must focus where BI can provide the organization with the most value. Much of what we discuss from a value perspective concerns the delivery of reports, dashboards, and …
An overview of Business Intelligence research in healthcare ...
With the aim of identifying the most attentive themes, topics, and research priorities, this review endeavors to perform a literature analysis on research articles addressing business intelligence …
Review of Business Intelligence Implementation in Healthcare
The use of BI in healthcare, like any other technology-based approach, is to resolve business challenges and streamline operations. As discussed earlier, challenges in the healthcare sector …
Business Intelligence's Effect On US Healthcare Delivery
of the current literature on two fundamental topics: business intelligence and healthcare delivery. In order to accomplish that, we look at how BI is being used in the healthcare sector, discuss …
90 Healthcare Business Intelligence A Primer - ijtsrd.com
To succeed in a modern digital world, healthcare industry must be data-driven. Hospitals and healthcare institutions desire to make their workflows more efficient in order to meet demand. …
Justifying Business Intelligence Systems Adoption: A …
In the healthcare industry explicitly, data is generated daily through the use of medical devices and other Enterprise Resource Planning (ERP) software or also known as Hospital Information …
Business Intelligence in Healthcare Industry - IJSR
The potential benefits of business intelligence programs include accelerating and improving decision making; optimizing internal business processes; increasing operational efficiency; …
BUSINESS INTELLIGENCE FOR HEALTHCARE INDUSTRY
The current paper highlights the advantages of big data analytics and business intelligence in the healthcare industry.
Repository Istituzionale dei Prodotti della Ricerca del …
Business Intelligence (BI) is concerned with the extraction, analysis, and presentation of data to make decisions and improve the management of firms. This becomes particularly relevant in the …
An assessment of business intelligence in public hospitals
One sector that generates large amounts of data is the healthcare industry owing to its need to meet requirements related to patient records, compliance, and patient care [4]. Therefore, use of …
Implementation of Business Intelligence in an Electronic …
To conduct this type of analysis, healthcare organisations use business intelligence tools to gain data-driven insights. This approach, which aims to deliver improved patient care, is known as …
An Integrated Business Intelligence Framework for …
introducing an integrated BI framework for healthcare analytics will lead to better decisions in healthcare organizations which is the main objective of this paper.
Predictive Analytics in Healthcare - Intel
healthcare are using it to help improve patient engagement, population health, quality of care and business operations – areas that map closely to the Quadruple Aims.
The adoption of business intelligence systems in small and …
the deadly global pandemic of COVID-19, healthcare organizations are compelled to embrace new technologies for their routine decision-making. The Business Intelligence System (BIS) is one of …
Examining the Drivers and Performance Impact of Business …
healthcare industry. Integrating the TOE framework and RBV theory provides a robust analytical framework for examining the internal and external factors that impact BI adoption of business …
Data Warehouse Design Considerations for a Healthcare …
Abstract— This paper deals with the importance of Business Intelligence (BI) in healthcare and its vital perspectives. It is an effort to identify the difference between traditional BI approaches and …
