Electronic Health Records and Medical Big Data

Electronic Health Records and Medical Big Data PDF

Author: Sharona Hoffman

Publisher: Cambridge University Press

Published: 2016-12-07

Total Pages: 227

ISBN-13: 1107166543

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This book provides interdisciplinary analysis of electronic health record systems and medical big data, offering a wealth of technical, legal, and policy insights.

Secondary Analysis of Electronic Health Records

Secondary Analysis of Electronic Health Records PDF

Author: MIT Critical Data

Publisher: Springer

Published: 2016-09-09

Total Pages: 427

ISBN-13: 3319437429

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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.

Big Data in Healthcare

Big Data in Healthcare PDF

Author: Farrokh Alemi

Publisher:

Published: 2019

Total Pages: 553

ISBN-13: 9781640550636

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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.

Registries for Evaluating Patient Outcomes

Registries for Evaluating Patient Outcomes PDF

Author: Agency for Healthcare Research and Quality/AHRQ

Publisher: Government Printing Office

Published: 2014-04-01

Total Pages: 396

ISBN-13: 1587634333

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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.

Big Data in Healthcare

Big Data in Healthcare PDF

Author: Farrokh Alemi

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9781640550674

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"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"--

Fundamentals of Clinical Data Science

Fundamentals of Clinical Data Science PDF

Author: Pieter Kubben

Publisher: Springer

Published: 2018-12-21

Total Pages: 219

ISBN-13: 3319997130

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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.

Best Care at Lower Cost

Best Care at Lower Cost PDF

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2013-05-10

Total Pages: 437

ISBN-13: 0309282810

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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.

Big Data Analytics in Healthcare

Big Data Analytics in Healthcare PDF

Author: Anand J. Kulkarni

Publisher: Springer Nature

Published: 2019-10-01

Total Pages: 187

ISBN-13: 3030316726

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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.

Statistics and Machine Learning Methods for EHR Data

Statistics and Machine Learning Methods for EHR Data PDF

Author: Hulin Wu

Publisher: CRC Press

Published: 2020-12-09

Total Pages: 329

ISBN-13: 1000260941

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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.

Clinical Research Informatics

Clinical Research Informatics PDF

Author: Rachel Richesson

Publisher: Springer Science & Business Media

Published: 2012-02-15

Total Pages: 415

ISBN-13: 1848824475

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The purpose of the book is to provide an overview of clinical research (types), activities, and areas where informatics and IT could fit into various activities and business practices. This book will introduce and apply informatics concepts only as they have particular relevance to clinical research settings.