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Data Driven Approaches for Healthcare by Chengliang Yang,Chris Delcher,Elizabeth Shenkman,Sanjay Ranka Pdf
Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics
Data-Driven Approach for Bio-medical and Healthcare by Nilanjan Dey Pdf
The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.
Data Driven Approaches for Healthcare by Chengliang Yang,Chris Delcher,Elizabeth Shenkman,Sanjay Ranka Pdf
Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics
Healthcare Service Management by Li Tao,Jiming Liu Pdf
Healthcare service systems are of profound importance in promoting the public health and wellness of people. This book introduces a data-driven complex systems modeling approach (D2CSM) to systematically understand and improve the essence of healthcare service systems. In particular, this data-driven approach provides new perspectives on health service performance by unveiling the causes for service disparity, such as spatio-temporal variations in wait times across different hospitals. The approach integrates four methods -- Structural Equation Modeling (SEM)-based analysis; integrated projection; service management strategy design and evaluation; and behavior-based autonomy-oriented modeling -- to address respective challenges encountered in performing data analytics and modeling studies on healthcare services. The thrust and uniqueness of this approach lies in the following aspects: Ability to explore underlying complex relationships between observed or latent impact factors and service performance. Ability to predict the changes and demonstrate the corresponding dynamics of service utilization and service performance. Ability to strategically manage service resources with the adaptation of unpredictable patient arrivals. Ability to figure out the working mechanisms that account for certain spatio-temporal patterns of service utilization and performance. To show the practical effectiveness of the proposed systematic approach, this book provides a series of pilot studies within the context of cardiac care in Ontario, Canada. The exemplified studies have unveiled some novel findings, e.g., (1) service accessibility and education may relieve the pressure of population size on service utilization; (2) functionally coupled units may have a certain cross-unit wait-time relationship potentially because of a delay cascade phenomena; (3) strategically allocating time blocks in operating rooms (ORs) based on a feedback mechanism may benefit OR utilization; (4) patients’ and hospitals’ autonomous behavior, and their interactions via wait times may bear the responsible for the emergence of spatio-temporal patterns observed in the real-world cardiac care system. Furthermore, this book presents an intelligent healthcare decision support (iHDS) system, an integrated architecture for implementing the data-driven complex systems modeling approach to developing, analyzing, investigating, supporting and advising healthcare related decisions. In summary, this book provides a data-driven systematic approach for addressing practical decision-support problems confronted in healthcare service management. This approach will provide policy makers, researchers, and practitioners with a practically useful way for examining service utilization and service performance in various ``what-if" scenarios, inspiring the design of effectiveness resource-allocation strategies, and deepening the understanding of the nature of complex healthcare service systems.
Data-Driven Quality Improvement and Sustainability in Health Care by Patricia L. Thomas, PhD, RN, FAAN, FNAP, FACHE, NEA-BC, ACNS-BC, CNL,James L. Harris, PhD, APRN-BC, MBA, CNL, FAAN,Brian J. Collins, BS, MA Pdf
Data-Driven Quality Improvement and Sustainability in Health Care: An Interprofessional Approach provides nurse leaders and healthcare administrators of all disciplines with a solid understanding of data and how to leverage data to improve outcomes, fuel innovation, and achieve sustained results. It sets the stage by examining the current state of the healthcare landscape; new imperatives to meet policy, regulatory, and consumer demands; and the role of data in administrative and clinical decision-making. It helps the professional identify the methods and tools that support thoughtful and thorough data analysis and offers practical application of data-driven processes that determine performance in healthcare operations, value- and performance-based contracts, and risk contracts. Misuse or inconsistent use of data leads to ineffective and errant decision-making. This text highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls. In addition, chapters feature key points, reflection questions, and real-life interprofessional case exemplars to help the professional draw distinctions and apply principles to their own practice. Key Features: Provides nurse leaders and other healthcare administrators with an understanding of the role of data in the current healthcare landscape and how to leverage data to drive innovative and sustainable change Offers frameworks, methodology, and tools to support quality improvement measures Demonstrates the application of data and how it shapes quality and safety initiatives through real-life case exemplars Highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls
Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizingthe healthcare industry. You have more data available than everbefore, and applying the right analytics can spur growth. Benefitsextend to patients, providers, and board members, and thetechnology can make centralized patient management a reality.Despite the potential for growth, many in the industry andgovernment are questioning the value of data in health care,wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transformingthe Industry tackles the issue and proves why BI is not onlyworth it, but necessary for industry advancement. Healthcare BIguru Laura Madsen challenges the notion that data have little valuein healthcare, and shows how BI can ease regulatory reportingpressures and streamline the entire system as it evolves. Madsenillustrates how a data-driven organization is created, and how itcan transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data moreeffectively Find out how Big Data has transformed other industries, and howit applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transformingthe Industry provides tables, checklists, and forms that allowyou to take immediate action in implementing BI in yourorganization. You can't afford to be behind the curve. The industryis moving on, with or without you. Data-Driven Healthcare: HowAnalytics and BI are Transforming the Industry is your guide toutilizing data to advance your operation in an industry wheredata-fueled growth will be the new norm.
Handbook of Research on Healthcare Administration and Management by Wickramasinghe, Nilmini Pdf
Effective healthcare delivery is a vital concern for citizens and communities across the globe. The numerous facets of this industry require constant re-evaluation and optimization of management techniques. The Handbook of Research on Healthcare Administration and Management is a pivotal reference source for the latest scholarly material on emerging strategies and methods for delivering optimal healthcare opportunities and solutions. Highlighting issues relating to decision making, process optimization, and technological applications, this book is ideally designed for policy makers, administrators, students, professionals, and researchers interested in achieving superior healthcare solutions.
Dive into the cutting-edge world where healthcare meets the transformative power of data science. This insightful book is an essential read for healthcare professionals looking to navigate the complex and rapidly evolving landscape of modern medicine. It offers an in-depth exploration of how data science applications are revolutionizing areas from genomics and epidemiology to public health policies and patient care. Embark on a journey through the multifaceted realms of predictive modeling, big data analytics, and the integration of AI in healthcare. The book meticulously details the impact of these technologies on personalized medicine, providing real-world case studies that demonstrate the practical applications and challenges of data-driven approaches in various health emergencies, including pandemics. Addressing the critical need for ethical considerations and privacy in handling patient data, the book provides a balanced view of the opportunities and responsibilities that come with technological advancements in healthcare. It goes beyond mere theory, offering actionable insights and strategies for healthcare professionals to incorporate data science into their practice effectively. Equipped with a glossary of terms and a wealth of resources for further learning, this book is more than just a guide; it's an indispensable resource for healthcare professionals aspiring to be at the forefront of their field. Whether you're a seasoned practitioner or new to the world of healthcare data science, this book will enlighten, inspire, and empower you to make a profound impact in the ever-changing world of healthcare.
Data-Driven Quality Improvement and Sustainability in Health Care by Patricia Thomas,James Harris,Brian Collins, Bs Ma Pdf
Data-Driven Quality Improvement and Sustainability in Health Care: An Interprofessional Approach provides healthcare leaders and administrators of all disciplines with a solid understanding of data and how to leverage data to improve outcomes, fuel innovation, and achieve sustained results. It sets the stage by examining the current state of the healthcare landscape; new imperatives to meet policy, regulatory, and consumer demands; and the role of data in administrative and clinical decision-making. It helps the reader identify the methods and tools that support thoughtful and thorough data analysis and offers practical application of data-driven processes that determine performance in healthcare operations, value- and performance-based contracts, and risk contracts. Misuse or inconsistent use of data leads to ineffective and errant decision-making. This text highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls. In addition, chapters feature key points, reflection questions, and real-life interprofessional case exemplars to help the reader draw distinctions and apply principles to their own practice. Chapters also outline core competencies and skills required for leaders to master use of data and steer their teams to success. Key Features: Provides an understanding of the role of data in the current healthcare landscape and how to leverage data to drive innovative and sustainable change Offers frameworks, methodology, and tools to support quality improvement measures Demonstrates the application of data and how it shapes quality and safety initiatives through real-life case exemplars Outlines core competencies and skills required for leaders to achieve results Highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls Purchase includes access to the eBook for use on most mobile devices or computers
Data Driven Approach to Enhancing Efficiency and Value in Healthcare by Richard E. Guerrero Ludueña Pdf
"Healthcare is changing, and the era of data-driven healthcare organisations is increasingly popular. Data-Driven approaches (e.g., Machine Learning, Metaheuristics, Modelling and Simulation, Data Analytics, and Data Visualisation) can be used to increase efficiency and value in health services. Despite extensive research and technological development, the evidence impact of those methodologies in the healthcare sector is limited. In this Thesis we argue that an approach without borders in terms of academic societies and field of study could help to tackle this lack of impact to enhance efficiency and value in healthcare. This Thesis is based on solving practical problems in healthcare, with the research drawing upon both theoretical and empirical analysis. The research is organised in four stages. In the first, a variety of techniques from Modelling and Simulation were studied and used to analyse current performance and to model improved and more efficient future states of healthcare systems. The focus was the analysis of capacity, demand, activity, and queues both at hospital and population levels. In the second part, a Genetic Algorithm was used to solve a Routing Home Healthcare problem. In the third part, Social Network Analysis was used to visualise and analyse email networks. In the final part, a new healthcare system performance metric is proposed and implemented using a case study. New frameworks to implement these methodologies in the context of real-world problems are presented throughout the Thesis. In collaboration with University of Southampton, Wessex Academic Health Science Network (AHSN), and NHS England, several projects were developed and implemented for healthcare improvement in the UK. The work aims to increase early detection of cancer and thereby reduce premature mortality. The research was conducted working closely with NHS organisations across the Wessex region in England to produce bespoke endoscopy service modelling, as well as population level models. At a regional level, a Colorectal Cancer Screening Programme model was developed. An analysis of endoscopy activity, capacity and demand across the region was conducted. Future demand for endoscopy services in five years' time was estimated, and we found that the system has enough capacity to attend the expected future activity. A new healthcare system performance metric is presented as a tool to improve healthcare services. Genetic Algorithm metaheuristic was applied in a variant of the Home Health Care Problem (HHCP), focusing on the route planning of clinical homecare. Working with the Hospital del Mar Medical Research Institute of Barcelona and the Agency of Health Quality and Assessment of Catalonia a study was developed to estimate future utilisation scenarios of knee arthroplasty (KA) revision in the Spanish National Health System in the short-term (2015) and long-term (2030) and their impact on primary KA utilisation. One of the findings was that the variation in the number of revisions depended on both the primary utilisation rate and the survival function applied. Future activity and resources needed was estimated. A Social Network Analysis (SNA) project was developed in collaboration with the Wessex AHSN to analyse and extract insight from an organisational email, using SNA and Data Mining. A new healthcare system performance metric - based on the Overall Equipment Effectiveness (OEE) measure - is proposed and evaluated using real data from and Endoscopy Unit from a UK based hospital. To summarise, this work identifies four key techniques to use in the investigation of health data - Machine Learning Algorithms, Metaheuristic, Discrete Event Simulation and Data Analytics & Visualisations. Following a review of the different subjects and associated issues, those four techniques were evaluated and used with an applied-focus to solve healthcare problems. Key learning points from all different studies, as well as challenges and opportunities for the application of data-driven methodologies are discussed in the final chapter of the Thesis." -- TDX.
Big Data Analytics in Healthcare by Anand J. Kulkarni,Patrick Siarry,Pramod Kumar Singh,Ajith Abraham,Mengjie Zhang,Albert Zomaya,Fazle Baki Pdf
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.
Secondary Analysis of Electronic Health Records by MIT Critical Data Pdf
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Artificial Intelligence for Data-Driven Medical Diagnosis by Deepak Gupta,Utku Kose,Bao Le Nguyen,Siddhartha Bhattacharyya Pdf
THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai
Author : Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai Publisher : Springer Nature Page : 471 pages File Size : 45,9 Mb Release : 2020-07-31 Category : Medical ISBN : 9783030479947
Leveraging Data Science for Global Health by Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai Pdf
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Data-Driven Approaches for Effective Managerial Decision Making by Anubha,Sharma, Himanshu Pdf
In todays competitive market, a manager must be able to look at data, understand it, analyze it, and then interpret it to design a smart business strategy. Big data is also a valuable source of information on how customers interact with firms through various mediums such as social media platforms, online reviews, and many more. The applications and uses of business analytics are numerous and must be further studied to ensure they are utilized appropriately. Data-Driven Approaches for Effective Managerial Decision Making investigates management concepts and applications using data analytics and outlines future research directions. The book also addresses contemporary advancements and innovations in the field of management. Covering key topics such as big data, business intelligence, and artificial intelligence, this reference work is ideal for managers, business owners, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
