Download or read book Electronic Health Records and Medical Big Data written by Sharona Hoffman and published by Cambridge University Press. This book was released on 2016-12-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps readers gain an in-depth understanding of electronic health record (EHR) systems, medical big data, and the regulations that govern them. It analyzes both the shortcomings and benefits of EHR systems, exploring the law's response to the creation of these systems, highlighting gaps in the current legal framework, and developing detailed recommendations for regulatory, policy, and technological improvements. Electronic Health Records and Medical Big Data addresses not only privacy and security concerns but also other important challenges, such as those related to data quality and data analysis. In addition, the author formulates a large body of recommendations to improve the technology's safety, security, and efficacy for both clinical and secondary (such as research) uses of medical data.
Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Big Data in Healthcare written by Farrokh Alemi and published by . This book was released on 2019 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Healthcare: Statistical Analysis of the Electronic Health Record provides the statistical tools that healthcare leaders need to organize and interpret their data. Designed for accessibility to those with a limited mathematics background, the book demonstrates how to leverage EHR data for applications as diverse as healthcare marketing, pay for performance, cost accounting, and strategic management. Topics include:* Using real-world data to compare hospitals' performance. * Measuring the prognosis of patients through massive data* Distinguishing between fake claims and true improvements* Comparing the effectiveness of different interventions using causal analysis* Benchmarking different clinicians on the same set of patients* Remove confounding in observational dataThis book can be used in introductory courses on hypothesis testing, intermediate courses on regression, and advanced courses on causal analysis. It can also be used to learn SQL language. Its extensive online instructor resources include course syllabi, PowerPoint and video lectures, Excel exercises, individual and team assignments, answers to assignments, and student-organized tutorials. Big Data in Healthcare applies the building blocks of statistical thinking to the basic challenges that healthcare leaders face every day. Prepare for those challenges with the clear understanding of your data that statistical analysis can bring--and make the best possible decisions for maximum performance in the competitive field of healthcare.
Download or read book Electronic Health Records and Medical Big Data written by Sharona Hoffman and published by Cambridge University Press. This book was released on 2016-12-07 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides interdisciplinary analysis of electronic health record systems and medical big data, offering a wealth of technical, legal, and policy insights.
Download or read book Registries for Evaluating Patient Outcomes written by Agency for Healthcare Research and Quality/AHRQ and published by Government Printing Office. This book was released on 2014-04-01 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Download or read book Big Data Health Law and Bioethics written by I. Glenn Cohen and published by Cambridge University Press. This book was released on 2018-03-08 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: When data from all aspects of our lives can be relevant to our health - from our habits at the grocery store and our Google searches to our FitBit data and our medical records - can we really differentiate between big data and health big data? Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise? This timely, groundbreaking volume explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors.
Download or read book Big Data in Healthcare written by Farrokh Alemi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces health administrators, nurses, physician assistants, medical students, and data scientists to statistical analysis of electronic health records (EHRs). The future of medicine depends on understanding patterns in EHRs. This book shows how to use EHRs for precision and predictive medicine"--
Download or read book Electronic Health Records written by Richard Gartee and published by Prentice Hall. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resource added for the Health Information Technology program 105301.
Download or read book Big Data Analytics in Healthcare written by Anand J. Kulkarni and published by Springer Nature. This book was released on 2019-10-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Medical Big Data and Internet of Medical Things written by Aboul Ella Hassanien and published by CRC Press. This book was released on 2018-10-25 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems. Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.
Download or read book Statistics and Machine Learning Methods for EHR Data written by Hulin Wu and published by CRC Press. This book was released on 2020-12-09 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
Download or read book Big Data Enabled Nursing written by Connie W. Delaney and published by Springer. This book was released on 2017-11-02 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors.
Download or read book Real World Evidence Generation and Evaluation of Therapeutics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-07-05 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume and complexity of information about individual patients is greatly increasing with use of electronic records and personal devices. Potential effects on medical product development in the context of this wealth of real-world data could be numerous and varied, ranging from the ability to determine both large-scale and patient-specific effects of treatments to the ability to assess how therapeutics affect patients' lives through measurement of lifestyle changes. In October 2016, the National Academies of Sciences, Engineering, and Medicine held a workshop to facilitate dialogue among stakeholders about the opportunities and challenges for incorporating real-world evidence into all stages in the process for the generation and evaluation of therapeutics. Participants explored unmet stakeholder needs and opportunities to generate new kinds of evidence that meet those needs. This publication summarizes the presentations and discussions from the workshop.
Download or read book Our Bodies Our Data written by Adam Tanner and published by Beacon Press. This book was released on 2017-01-10 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: How the hidden trade in our sensitive medical information became a multibillion-dollar business, but has done little to improve our health-care outcomes Hidden to consumers, patient medical data has become a multibillion-dollar worldwide trade industry between our health-care providers, drug companies, and a complex web of middlemen. This great medical-data bazaar sells copies of the prescription you recently filled, your hospital records, insurance claims, blood-test results, and more, stripped of your name but possibly with identifiers such as year of birth, gender, and doctor. As computing grows ever more sophisticated, patient dossiers become increasingly vulnerable to reidentification and the possibility of being targeted by identity thieves or hackers. Paradoxically, comprehensive electronic files for patient treatment—the reason medical data exists in the first place—remain an elusive goal. Even today, patients or their doctors rarely have easy access to comprehensive records that could improve care. In the evolution of medical data, the instinct for profit has outstripped patient needs. This book tells the human, behind-the-scenes story of how such a system evolved internationally. It begins with New York advertising man Ludwig Wolfgang Frohlich, who founded IMS Health, the world’s dominant health-data miner, in the 1950s. IMS Health now gathers patient medical data from more than 45 billion transactions annually from 780,000 data feeds in more than 100 countries. Our Bodies, Our Data uncovers some of Frohlich’s hidden past and follows the story of what happened in the following decades. This is both a story about medicine and medical practice, and about big business and maximizing profits, and the places these meet, places most patients would like to believe are off-limits. Our Bodies, Our Data seeks to spark debate on how we can best balance the promise big data offers to advance medicine and improve lives while preserving the rights and interests of every patient. We, the public, deserve a say in this discussion. After all, it’s our data.
Download or read book Leveraging Data Science for Global Health written by Leo Anthony Celi and published by Springer Nature. This book was released on 2020-07-31 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Best Care at Lower Cost written by Institute of Medicine and published by National Academies Press. This book was released on 2013-05-10 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: America's health care system has become too complex and costly to continue business as usual. Best Care at Lower Cost explains that inefficiencies, an overwhelming amount of data, and other economic and quality barriers hinder progress in improving health and threaten the nation's economic stability and global competitiveness. According to this report, the knowledge and tools exist to put the health system on the right course to achieve continuous improvement and better quality care at a lower cost. The costs of the system's current inefficiency underscore the urgent need for a systemwide transformation. About 30 percent of health spending in 2009-roughly $750 billion-was wasted on unnecessary services, excessive administrative costs, fraud, and other problems. Moreover, inefficiencies cause needless suffering. By one estimate, roughly 75,000 deaths might have been averted in 2005 if every state had delivered care at the quality level of the best performing state. This report states that the way health care providers currently train, practice, and learn new information cannot keep pace with the flood of research discoveries and technological advances. About 75 million Americans have more than one chronic condition, requiring coordination among multiple specialists and therapies, which can increase the potential for miscommunication, misdiagnosis, potentially conflicting interventions, and dangerous drug interactions. Best Care at Lower Cost emphasizes that a better use of data is a critical element of a continuously improving health system, such as mobile technologies and electronic health records that offer significant potential to capture and share health data better. In order for this to occur, the National Coordinator for Health Information Technology, IT developers, and standard-setting organizations should ensure that these systems are robust and interoperable. Clinicians and care organizations should fully adopt these technologies, and patients should be encouraged to use tools, such as personal health information portals, to actively engage in their care. This book is a call to action that will guide health care providers; administrators; caregivers; policy makers; health professionals; federal, state, and local government agencies; private and public health organizations; and educational institutions.
