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| business intelligence in healthcare: 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: 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: 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: 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: 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: 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: Analytics in Healthcare and the Life Sciences Thomas H. Davenport, Dwight McNeill, 2013-11-04 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: 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: 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: 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: 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: 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: 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: 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: Next-Generation Mobile and Pervasive Healthcare Solutions Machado, Jose, Abelha, António, Santos, Manuel Filipe, Portela, Filipe, 2017-08-10 Technology is changing the practice of healthcare by the ways medical information is stored, shared, and accessed. With mobile innovations, new strategies are unfolding to further advance processes and procedures in medical settings. Next-Generation Mobile and Pervasive Healthcare Solutions is an advanced reference source for the latest research on emerging progress and applications within mobile health initiatives and health informatics. Featuring coverage on a broad range of topics and perspectives such as electronic health records (EHR), clinical decision support systems, and medical ontologies, this publication is ideally designed for professionals and researchers seeking scholarly material on the increased use of mobile health applications. |
| business intelligence in healthcare: 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: Better Data, Better Decisions Nate Moore, Mona Reimers, 2013 Data flows into medical practices daily from practice management systems, electronic medical record (EMR) systems, accounting systems and many other sources. Too many practices extract only the bare minimum of data to file claims and meet reporting obligations, without recognizing the value in the flood of data that passes through the practice. |
| business intelligence in healthcare: 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: Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs Tan, Joseph, 2012-06-30 Clinical decision support systems, medical applications, and electronic health records each help to ensure the provision of efficient, accurate healthcare services, thereby providing patients with a better experience and overall reducing health care costs. Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs is a prime resource for both academic researchers and practitioners looking to advance their knowledge of the interdisciplinary areas of healthcare information technology and management research. This book addresses innovative concepts and critical issues in the emerging field of health information systems and informatics, with an emphasis on sustainable computer information systems, ensuring healthcare efficiency, and denoising MRI and ECG outputs. |
| business intelligence in healthcare: 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: 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: 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: Big Data Analytics and Intelligence Poonam Tanwar, Vishal Jain, Chuan-Ming Liu, Vishal Goyal, 2020-09-30 Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval. |
| business intelligence in healthcare: 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: 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: Multiple Perspectives on Artificial Intelligence in Healthcare Mowafa Househ, Elizabeth Borycki, Andre Kushniruk, 2021-08-05 This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare. It does so by approaching the topic from multiple perspectives, e.g. the nursing, consumer, medical practitioner, healthcare manager, and data analyst perspective. It covers human factors research, discusses patient safety issues, and addresses ethical challenges, as well as important policy issues. By reporting on cutting-edge research and hands-on experience, the book offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes. It will also benefit students and researchers whose work involves artificial intelligence-related research issues in healthcare. |
| business intelligence in healthcare: 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: 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: Data Analytics in Biomedical Engineering and Healthcare Kun Chang Lee, Sanjiban Sekhar Roy, Pijush Samui, Vijay Kumar, 2020-10-18 Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks |
| business intelligence in healthcare: Process Mining in Healthcare Ronny S. Mans, Wil M. P. van der Aalst, Rob J. B. Vanwersch, 2015-03-12 What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis. |
| business intelligence in healthcare: Health Analytics Jason Burke, 2013-07-10 A hands-on, analytics road map for health industry leaders The industry-wide transformation taking place across the health and life sciences ecosystem is mandating that organizations adopt new decision-making capabilities, based on science and real-world information. Analytics will be a required competency for the modern health enterprise; this book is about how to cross the chasm. The ultimate analytics guide for the health industry leader, this essential book equips business leaders with little-to-no experience in analytics to understand how to incorporate analytics as a cornerstone of their 21st century competitive business strategy. Paints the picture for a new health enterprise, one focused on the patient Explores the financial components of this new operating model, using analytics to optimize the tradeoffs between cost and value Deals with the rising role of the consumer, using analytics to create a completely new health engagement model with individual recipients of care Looks at how analytics can drive innovations in care practice, patient-experienced medical outcomes, and analytically driven novel therapies optimized for the individual patient Presents a variety of text, tables, and graphics illustrating the various concepts being described Within each section and chapter, Health Analytics assesses the current landscape, proposing a new model/concept, sharing real-world stories of how the old and new world come together, and framing a how-to for the reader in terms of growing that particular set of capabilities in their own enterprises. |
| business intelligence in healthcare: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| business intelligence in healthcare: 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: Foundations of Artificial Intelligence in Healthcare and Bioscience Louis J. Catania, 2020-11-25 Foundational Handbook of Artificial Intelligence in Healthcare and Bioscience: A User Friendly Guide for IT Professionals, Healthcare Providers, Researchers, and Clinicians uses color-coded illustrations to explain AI from its basics to modern technologies. Other sections cover extensive, current literature research and citations regarding AI's role in the business and clinical aspects of health care. The book provides readers with a unique opportunity to appreciate AI technology in practical terms, understand its applications, and realize its profound influence on the clinical and business aspects of health care. Artificial Intelligence is a disruptive technology that is having a profound and growing influence on the business of health care as well as medical diagnosis, treatment, research and clinical delivery. The AI relationships in health care are complex, but understandable, especially when discussed and developed from their foundational elements through to their practical applications in health care. - Provides an illustrated, foundational guide and comprehensive descriptions of what Artificial Intelligence is and how it functions - Integrates a comprehensive discussion of AI applications in the business of health care - Presents in-depth clinical and AI-related discussions on diagnostic medicine, therapeutic medicine, and prevalent disease categories with an emphasis on immunology and genetics, the two categories most influenced by AI - Includes comprehensive coverage of a variety of AI treatment applications, including medical/pharmaceutical care, nursing care, stem cell therapies, robotics, and 10 common disease categories with AI applications |
| business intelligence in healthcare: Global Business Analytics Models Hokey Min, 2016-03-05 THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy Analytical opportunities to solve key managerial problems in global enterprises Written for working managers: packed with realistic, useful examples This guide helps global managers use modern analytics to gain reliable, actionable, and timely business intelligence–and use it to manage risk, build winning strategies, and solve urgent problems. Dr. Hokey Min offers a practical, easy-to-understand overview of business analytics in a global context, focusing especially on managerial and strategic implications. After demystifying today’s core quantitative tools, he demonstrates them at work in a wide spectrum of global applications. You’ll build models to help segment global markets, forecast demand, assess risk, plan financing, optimize supply chains, and more. Along the way, you’ll find practical guidance for developing analytic thinking, operationalizing Big Data in global environments, and preparing for future analytical innovations. Whether you’re a global executive, strategist, analyst, marketer, supply chain professional, student or researcher, this book will help you drive real value from analytics–in smarter decisions, improved strategy, and better management. In today’s global business environments characterized by growing complexity, volatility, and uncertainty, business analytics has become an indispensable tool for managing these challenges. Specifically, global managers need analytics expertise to solve problems, identify opportunities, shape strategy, mitigate risk, and improve their day-to-day operational efficiency. Now, for the first time, there’s an analytics guide designed specifically for decision-makers in global organizations. Leveraging his experience teaching a number of students and training hundreds of managers and executives, Dr. Hokey Min demystifies the principles and tools of modern business analytics, and demonstrates their real-world use in global business. First, Dr. Min identifies key success factors and mindsets, helping you establish the preconditions for effective analysis. Next, he walks you through the practicalities of collecting, organizing, and analyzing Big Data, and developing models to transform them into actionable insight. Building on these foundations, he illustrates core analytical applications in finance, healthcare, and global supply chains. He concludes by previewing emerging trends in analytics, including the newest tools for automated decision-making. Compare today’s key quantitative tools Stats, data mining, OR, and simulation: how they work, when to use them Get the right data... ...and get the data right Predict the future... ...and sense its arrival sooner than others can |
| business intelligence in healthcare: 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: Artificial Intelligence and Machine Learning in Public Healthcare KC Santosh, Loveleen Gaur, 2022-01-01 This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions. |
| business intelligence in healthcare: 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: Digitalization in Healthcare Patrick Glauner, Philipp Plugmann, Guido Lerzynski, 2021-03-13 Digital technologies are currently dramatically changing healthcare. This book introduces the reader to the latest digital innovations in healthcare in fields such as artificial intelligence, points out new ways in patient care and describes the limits of its application. It also offers essential guidance in the form of structured and authoritative contributions by domain experts spanning from artificial intelligence to hospital management to radiology to dentistry to preventive medicine. Furthermore, it shares ideas and experiences of industry veterans, in particular on how IT-driven solutions could solve long-standing issues in the fields of healthcare and hospitalization. It also gives advice on what new digital technologies to consider for becoming a healthcare market leader in the future. Taken together, these contributions provide a “road map” to guide decision makers, physicians, academics, industry representatives and other interested readers to understand the large impact of digital technology on healthcare today and its enormous potential for future development. |
| business intelligence in healthcare: Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering Management Association, Information Resources, 2021-05-28 Decision support systems (DSS) are widely touted for their effectiveness in aiding decision making, particularly across a wide and diverse range of industries including healthcare, business, and engineering applications. The concepts, principles, and theories of enhanced decision making are essential points of research as well as the exact methods, tools, and technologies being implemented in these industries. From both a standpoint of DSS interfaces, namely the design and development of these technologies, along with the implementations, including experiences and utilization of these tools, one can get a better sense of how exactly DSS has changed the face of decision making and management in multi-industry applications. Furthermore, the evaluation of the impact of these technologies is essential in moving forward in the future. The Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering explores how decision support systems have been developed and implemented across diverse industries through perspectives on the technology, the utilizations of these tools, and from a decision management standpoint. The chapters will cover not only the interfaces, implementations, and functionality of these tools, but also the overall impacts they have had on the specific industries mentioned. This book also evaluates the effectiveness along with benefits and challenges of using DSS as well as the outlook for the future. This book is ideal for decision makers, IT consultants and specialists, software developers, design professionals, academicians, policymakers, researchers, professionals, and students interested in how DSS is being used in different industries. |
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
