Data Mapping In Healthcare

  data mapping in healthcare: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 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.
  data mapping in healthcare: Statistical, Mapping and Digital Approaches in Healthcare Gilles Maignant, Pascal Staccini, 2018-11-19 Statistical, Mapping and Digital Approaches in Healthcare addresses all health territories, starting from the analysis of geographical data (health data, population data, health data systems and environmental data), to new health areas (Health 3.0), i.e. digital health territories. Specific tools are used to question environmental changes, such as health statistics, mapping, mathematical models, optimization models and serious games. - Uniquely combines the approaches of mathematicians, geographers and physician to the analysis of health territories - Presents views that are based on an interdisciplinary framework, proposing a new look on health - Ideal for both clinicians and policymakers
  data mapping in healthcare: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 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.
  data mapping in healthcare: Race, Ethnicity, and Language Data Institute of Medicine, Board on Health Care Services, Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement, 2009-12-30 The goal of eliminating disparities in health care in the United States remains elusive. Even as quality improves on specific measures, disparities often persist. Addressing these disparities must begin with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by collecting health care quality information stratified by race, ethnicity and language data. Then attention can be focused on where interventions might be best applied, and on planning and evaluating those efforts to inform the development of policy and the application of resources. A lack of standardization of categories for race, ethnicity, and language data has been suggested as one obstacle to achieving more widespread collection and utilization of these data. Race, Ethnicity, and Language Data identifies current models for collecting and coding race, ethnicity, and language data; reviews challenges involved in obtaining these data, and makes recommendations for a nationally standardized approach for use in health care quality improvement.
  data mapping in healthcare: Value Stream Mapping for Healthcare Made Easy Cindy Jimmerson, 2017-07-26 In no industry is the concept of quality more essential than it is in healthcare, which is why the lean quality principles learned through the example of the Toyota Production System are so applicable. Two fundamental principles of Toyota‘s push for excellence are especially relevant to healthcare: ensuring quality at every step and keeping improve
  data mapping in healthcare: Integrating Social Care into the Delivery of Health Care National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Care Services, Committee on Integrating Social Needs Care into the Delivery of Health Care to Improve the Nation's Health, 2020-01-30 Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health was released in September 2019, before the World Health Organization declared COVID-19 a global pandemic in March 2020. Improving social conditions remains critical to improving health outcomes, and integrating social care into health care delivery is more relevant than ever in the context of the pandemic and increased strains placed on the U.S. health care system. The report and its related products ultimately aim to help improve health and health equity, during COVID-19 and beyond. The consistent and compelling evidence on how social determinants shape health has led to a growing recognition throughout the health care sector that improving health and health equity is likely to depend †at least in part †on mitigating adverse social determinants. This recognition has been bolstered by a shift in the health care sector towards value-based payment, which incentivizes improved health outcomes for persons and populations rather than service delivery alone. The combined result of these changes has been a growing emphasis on health care systems addressing patients' social risk factors and social needs with the aim of improving health outcomes. This may involve health care systems linking individual patients with government and community social services, but important questions need to be answered about when and how health care systems should integrate social care into their practices and what kinds of infrastructure are required to facilitate such activities. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health examines the potential for integrating services addressing social needs and the social determinants of health into the delivery of health care to achieve better health outcomes. This report assesses approaches to social care integration currently being taken by health care providers and systems, and new or emerging approaches and opportunities; current roles in such integration by different disciplines and organizations, and new or emerging roles and types of providers; and current and emerging efforts to design health care systems to improve the nation's health and reduce health inequities.
  data mapping in healthcare: Patient Safety Institute of Medicine, Board on Health Care Services, Committee on Data Standards for Patient Safety, 2003-12-20 Americans should be able to count on receiving health care that is safe. To achieve this, a new health care delivery system is needed †a system that both prevents errors from occurring, and learns from them when they do occur. The development of such a system requires a commitment by all stakeholders to a culture of safety and to the development of improved information systems for the delivery of health care. This national health information infrastructure is needed to provide immediate access to complete patient information and decision-support tools for clinicians and their patients. In addition, this infrastructure must capture patient safety information as a by-product of care and use this information to design even safer delivery systems. Health data standards are both a critical and time-sensitive building block of the national health information infrastructure. Building on the Institute of Medicine reports To Err Is Human and Crossing the Quality Chasm, Patient Safety puts forward a road map for the development and adoption of key health care data standards to support both information exchange and the reporting and analysis of patient safety data.
  data mapping in healthcare: The Dartmouth Atlas of Health Care Dartmouth Medical School. Center for the Evaluative Clinical Sciences, 1996
  data mapping in healthcare: Anonymizing Health Data Khaled El Emam, Luk Arbuckle, 2013-12-11 Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. Understand different methods for working with cross-sectional and longitudinal datasets Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy Use methods to anonymize unstructured free-form text data Minimize the risks inherent in geospatial data, without omitting critical location-based health information Look at ways to anonymize coding information in health data Learn the challenge of anonymously linking related datasets
  data mapping in healthcare: Healthcare Transformation with Informatics and Artificial Intelligence J. Mantas, P. Gallos, E. Zoulias, 2023-07-27 Artificial intelligence (AI) is once again in the news, with many major figures urging caution as developments in the technology accelerate. AI impacts all aspects of our lives, but perhaps the discipline of Biomedical Informatics is more affected than most, and is an area where the possible pitfalls of the technology might have particularly serious consequences. This book presents the papers delivered at ICIMTH 2023, the 21st International Conference on Informatics, Management, and Technology in Healthcare, held in Athens, Greece, from 1-3 July 2023. The ICIMTH conferences form a series of scientific events which offers a platform for scientists working in the field of biomedical and health informatics from all continents to gather and exchange research findings and experience. The title of the 2023 conference was Healthcare Transformation with Informatics and Artificial Intelligence, reflecting the importance of AI to healthcare informatics. A total of 252 submissions were received by the Program Committee, of which 149 were accepted as full papers, 13 as short communications, and 14 as poster papers after review. The papers cover a wide range of technologies, and topics include imaging, sensors, biomedical equipment, and management and organizational aspects, as well as legal and social issues. The book provides a timely overview of informatics and technology in healthcare during this time of extremely fast developments, and will be of interest to all those working in the field.
  data mapping in healthcare: Geospatial Health Data Paula Moraga, 2019-11-26 Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.
  data mapping in healthcare: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.
  data mapping 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
  data mapping in healthcare: Improving Diagnosis in Health Care National Academies of Sciences, Engineering, and Medicine, Institute of Medicine, Board on Health Care Services, Committee on Diagnostic Error in Health Care, 2015-12-29 Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.
  data mapping in healthcare: Building the Data Warehouse W. H. Inmon, 2002-10-01 The data warehousing bible updated for the new millennium Updated and expanded to reflect the many technological advances occurring since the previous edition, this latest edition of the data warehousing bible provides a comprehensive introduction to building data marts, operational data stores, the Corporate Information Factory, exploration warehouses, and Web-enabled warehouses. Written by the father of the data warehouse concept, the book also reviews the unique requirements for supporting e-business and explores various ways in which the traditional data warehouse can be integrated with new technologies to provide enhanced customer service, sales, and support-both online and offline-including near-line data storage techniques.
  data mapping in healthcare: Leveraging Data Science for Global Health Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai, 2020-07-31 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 mapping 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.
  data mapping 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
  data mapping in healthcare: Data Science for Healthcare Sergio Consoli, Diego Reforgiato Recupero, Milan Petković, 2019-02-23 This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.
  data mapping in healthcare: Sharing Clinical Research Data Institute of Medicine, Board on Health Care Services, Board on Health Sciences Policy, Roundtable on Translating Genomic-Based Research for Health, National Cancer Policy Forum, Forum on Neuroscience and Nervous System Disorders, Forum on Drug Discovery, Development, and Translation, 2013-06-07 Pharmaceutical companies, academic researchers, and government agencies such as the Food and Drug Administration and the National Institutes of Health all possess large quantities of clinical research data. If these data were shared more widely within and across sectors, the resulting research advances derived from data pooling and analysis could improve public health, enhance patient safety, and spur drug development. Data sharing can also increase public trust in clinical trials and conclusions derived from them by lending transparency to the clinical research process. Much of this information, however, is never shared. Retention of clinical research data by investigators and within organizations may represent lost opportunities in biomedical research. Despite the potential benefits that could be accrued from pooling and analysis of shared data, barriers to data sharing faced by researchers in industry include concerns about data mining, erroneous secondary analyses of data, and unwarranted litigation, as well as a desire to protect confidential commercial information. Academic partners face significant cultural barriers to sharing data and participating in longer term collaborative efforts that stem from a desire to protect intellectual autonomy and a career advancement system built on priority of publication and citation requirements. Some barriers, like the need to protect patient privacy, pre- sent challenges for both sectors. Looking ahead, there are also a number of technical challenges to be faced in analyzing potentially large and heterogeneous datasets. This public workshop focused on strategies to facilitate sharing of clinical research data in order to advance scientific knowledge and public health. While the workshop focused on sharing of data from preplanned interventional studies of human subjects, models and projects involving sharing of other clinical data types were considered to the extent that they provided lessons learned and best practices. The workshop objectives were to examine the benefits of sharing of clinical research data from all sectors and among these sectors, including, for example: benefits to the research and development enterprise and benefits to the analysis of safety and efficacy. Sharing Clinical Research Data: Workshop Summary identifies barriers and challenges to sharing clinical research data, explores strategies to address these barriers and challenges, including identifying priority actions and low-hanging fruit opportunities, and discusses strategies for using these potentially large datasets to facilitate scientific and public health advances.
  data mapping in healthcare: IEEE Standard Computer Dictionary , 1990
  data mapping in healthcare: The Synonym Finder J. I. Rodale, 2016-04-22 Originally published in 1961 by the founder of Rodale Inc., The Synonym Finder continues to be a practical reference tool for every home and office. This thesaurus contains more than 1 million synonyms, arranged alphabetically, with separate subdivisions for the different parts of speech and meanings of the same word.
  data mapping in healthcare: Mapping "Race" Laura E. Gómez, Nancy López, 2013-08-12 Researchers commonly ask subjects to self-identify their race from a menu of preestablished options. Yet if race is a multidimensional, multilevel social construction, this has profound methodological implications for the sciences and social sciences. Race must inform how we design large-scale data collection and how scientists utilize race in the context of specific research questions. This landmark collection argues for the recognition of those implications for research and suggests ways in which they may be integrated into future scientific endeavors. It concludes on a prescriptive note, providing an arsenal of multidisciplinary, conceptual, and methodological tools for studying race specifically within the context of health inequalities. Contributors: John A. Garcia, Arline T. Geronimus, Laura E. Gómez, Joseph L. Graves Jr., Janet E. Helms, Derek Kenji Iwamoto, Jonathan Kahn, Jay S. Kaufman, Mai M. Kindaichi, Simon J. Craddock Lee, Nancy López, Ethan H. Mereish, Matthew Miller, Gabriel R. Sanchez, Aliya Saperstein, R. Burciaga Valdez, Vicki D. Ybarra
  data mapping in healthcare: Machine Learning for Healthcare Applications Sachi Nandan Mohanty, G. Nalinipriya, Om Prakash Jena, Achyuth Sarkar, 2021-04-13 When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
  data mapping 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.
  data mapping in healthcare: Improving Healthcare Quality in Europe Characteristics, Effectiveness and Implementation of Different Strategies OECD, World Health Organization, 2019-10-17 This volume, developed by the Observatory together with OECD, provides an overall conceptual framework for understanding and applying strategies aimed at improving quality of care. Crucially, it summarizes available evidence on different quality strategies and provides recommendations for their implementation. This book is intended to help policy-makers to understand concepts of quality and to support them to evaluate single strategies and combinations of strategies.
  data mapping in healthcare: Mapping Uncertainty in Medicne Avril Danczak, Alison Lea, Geraldine Murphy, 2016-02-28 Uncertainty is the norm in medical practice, yet often gives rise to distress in clinicians, who fear they will make shameful or guilt inducing errors. This book offers a succinct method to clinicians for classifying uncertainty and finding the right skills to manage different types of uncertainty successfully. Every clinician experiences moments when 'they don't know what to do'. Modern medicine is increasingly complex and training has also become more complicated. The days of 'see one, do one, teach one' are over. Yet, both younger clinicians and senior practitioners describe uncertainty as one of the most challenging and stressful aspects of clinical work. If uncertainty is uncomfortable or threatening to individual practitioners, it also provides complex educational challenges. How can we learn to cope with uncertainty effectively ourselves? How can we teach others to understand and manage uncertainty? In this ground breaking book, the authors propose ways to cut through uncertainty, which is explored as an inevitable (and even desirable) component of clinical practice. A Map of Uncertainty in Medicine (MUM) is used to classify uncertainty and to define the skills that will help find a way though practical difficulties. It is always good to have your MUM with you in a tricky situation!
  data mapping in healthcare: Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare Christoph Thuemmler, Chunxue Bai, 2017-01-07 This book describes how the creation of new digital services—through vertical and horizontal integration of data coming from sensors on top of existing legacy systems—that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world. · Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems; · Provides overviews of technologies in addition to possible application scenarios and market conditions; · Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.
  data mapping in healthcare: Health Services Research and Analytics Using Excel Nalin Johri, PhD, MPH, 2020-02-01 Your all-in-one resource for quantitative, qualitative, and spatial analyses in Excel® using current real-world healthcare datasets. Health Services Research and Analytics Using Excel® is a practical resource for graduate and advanced undergraduate students in programs studying healthcare administration, public health, and social work as well as public health workers and healthcare managers entering or working in the field. This book provides one integrated, application-oriented resource for common quantitative, qualitative, and spatial analyses using only Excel. With an easy-to-follow presentation of qualitative and quantitative data, students can foster a balanced decision-making approach to financial data, patient statistical data and utilization information, population health data, and quality metrics while cultivating analytical skills that are necessary in a data-driven healthcare world. Whereas Excel is typically considered limited to quantitative application, this book expands into other Excel applications based on spatial analysis and data visualization represented through 3D Maps as well as text analysis using the free add-in in Excel. Chapters cover the important methods and statistical analysis tools that a practitioner will face when navigating and analyzing data in the public domain or from internal data collection at their health services organization. Topics covered include importing and working with data in Excel; identifying, categorizing, and presenting data; setting bounds and hypothesis testing; testing the mean; checking for patterns; data visualization and spatial analysis; interpreting variance; text analysis; and much more. A concise overview of research design also provides helpful background on how to gather and measure useful data prior to analyzing in Excel. Because Excel is the most common data analysis software used in the workplace setting, all case examples, exercises, and tutorials are provided with the latest updates to the Excel software from Office365 ProPlus® and newer versions, including all important “Add-ins” such as 3D Maps, MeaningCloud, and Power Pivots, among others. With numerous practice problems and over 100 step-by-step videos, Health Services Research and Analytics Using Excel® is an extremely practical tool for students and health service professionals who must know how to work with data, how to analyze it, and how to use it to improve outcomes unique to healthcare settings. Key Features: Provides a competency-based analytical approach to health services research using Excel Includes applications of spatial analysis and data visualization tools based on 3D Maps in Excel Lists select sources of useful national healthcare data with descriptions and website information Chapters contain case examples and practice problems unique to health services All figures and videos are applicable to Office365 ProPlus Excel and newer versions Contains over 100 step-by-step videos of Excel applications covered in the chapters and provides concise video tutorials demonstrating solutions to all end-of-chapter practice problems Robust Instructor ancillary package that includes Instructor’s Manual, PowerPoints, and Test Bank
  data mapping in healthcare: Mental Health Atlas 2017 World Health Organization, 2018-08-09 Collects together data compiled from 177 World Health Organization Member States/Countries on mental health care. Coverage includes policies, plans and laws for mental health, human and financial resources available, what types of facilities providing care, and mental health programmes for prevention and promotion.
  data mapping in healthcare: Healthcare Fraud Rebecca S. Busch, 2012-03-23 An invaluable tool equipping healthcare professionals, auditors, and investigators to detect every kind of healthcare fraud According to private and public estimates, billions of dollars are lost per hour to healthcare waste, fraud, and abuse. A must-have reference for auditors, fraud investigators, and healthcare managers, Healthcare Fraud, Second Edition provides tips and techniques to help you spot—and prevent—the red flags of fraudulent activity within your organization. Eminently readable, it is your go-to resource, equipping you with the necessary skills to look for and deal with potential fraudulent situations. Includes new chapters on primary healthcare, secondary healthcare, information/data management and privacy, damages/risk management, and transparency Offers comprehensive guidance on auditing and fraud detection for healthcare providers and company healthcare plans Examines the necessary background that internal auditors should have when auditing healthcare activities Managing the risks in healthcare fraud requires an understanding of how the healthcare system works and where the key risk areas are. With health records now all being converted to electronic form, the key risk areas and audit process are changing. Read Healthcare Fraud, Second Edition and get the valuable guidance you need to help combat this critical problem.
  data mapping in healthcare: The Computer-Based Patient Record Committee on Improving the Patient Record, Institute of Medicine, 1997-10-28 Most industries have plunged into data automation, but health care organizations have lagged in moving patients' medical records from paper to computers. In its first edition, this book presented a blueprint for introducing the computer-based patient record (CPR). The revised edition adds new information to the original book. One section describes recent developments, including the creation of a computer-based patient record institute. An international chapter highlights what is new in this still-emerging technology. An expert committee explores the potential of machine-readable CPRs to improve diagnostic and care decisions, provide a database for policymaking, and much more, addressing these key questions: Who uses patient records? What technology is available and what further research is necessary to meet users' needs? What should government, medical organizations, and others do to make the transition to CPRs? The volume also explores such issues as privacy and confidentiality, costs, the need for training, legal barriers to CPRs, and other key topics.
  data mapping in healthcare: Blockchain Technologies for Sustainability Subramanian Senthilkannan Muthu, 2021-10-24 This book highlights the applications of blockchain technologies to foster sustainable development in different fields. The concept of Sustainability has grown widespread in today’s context and there are many requirements to achieve Sustainability in any industrial sector including mapping, tracing the supply chain to ensure sustainable supply chain management. Reliable and transparent, efficient data is one of the key requirements for Sustainability in today’s advanced industrial context. Achievement of Sustainability objectives in this advanced era demands various technological advancements such as Blockchain technologies. The core competencies of blockchain technology namely transparency, data auditability, privacy, value transfer, and process efficiency and automation are very much essential for achieving the multifold objectives under sustainability.​
  data mapping in healthcare: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications Om Prakash Jena, Bharat Bhushan, Utku Kose, 2022-02-25 Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.
  data mapping in healthcare: Service Design Practices for Healthcare Innovation Mario A. Pfannstiel, Nataliia Brehmer, Christoph Rasche, 2022-01-19 This book offers an overview of service design practices for healthcare and hospital management. It explores how these practices can help to generate innovations in healthcare and contribute to the improvement of patient-centered care. Respected experts, including scholars from various disciplines and practitioners from healthcare institutions, share essential insights into established research areas, fields of work and work structures, and discuss successful approaches, methods and tools. By illustrating innovative services, products, processes, systems, and technologies, as well as their application in practice, the authors highlight the role of participating stakeholders in service design projects and the added value that comes from sharing, communicating, networking and collaborating. This book is a must-read for scholars and practitioners in the hospital and healthcare sector. It will also appeal to anyone interested in organizational development, service business model innovation, customer involvement and perceptions, and service experience.
  data mapping in healthcare: Clinical Informatics Study Guide John T. Finnell, Brian E. Dixon, 2022-04-22 This completely updated study guide textbook is written to support the formal training required to become certified in clinical informatics. The content has been extensively overhauled to introduce and define key concepts using examples drawn from real-world experiences in order to impress upon the reader the core content from the field of clinical informatics. The book groups chapters based on the major foci of the core content: health care delivery and policy; clinical decision-making; information science and systems; data management and analytics; leadership and managing teams; and professionalism. The chapters do not need to be read or taught in order, although the suggested order is consistent with how the editors have structured their curricula over the years. Clinical Informatics Study Guide: Text and Review serves as a reference for those seeking to study for a certifying examination independently or periodically reference while in practice. This includes physicians studying for board examination in clinical informatics as well as the American Medical Informatics Association (AMIA) health informatics certification. This new edition further refines its place as a roadmap for faculty who wish to go deeper in courses designed for physician fellows or graduate students in a variety of clinically oriented informatics disciplines, such as nursing, dentistry, pharmacy, radiology, health administration and public health.
  data mapping in healthcare: Cash-Pay Healthcare Stewart Gandolf Mba, Mark J Tager, 2018-11-13 This is a book for every healthcare practitioner-from every discipline-who is seeking to create a more meaningful, direct, and satisfying type of interaction with patients. At its foundation lies cash-pay healthcare and a return to the basic principles of commerce. You deliver services and products, and an experience that patients feel good about paying for with their hard-earned cash. This may involve a new payment structure, such as membership, concierge, hybrid, or direct pay; or it may be augmenting your business by adding new profit streams. It's simple, but not easy.In this breakthrough book, Dr. Mark Tager and Stewart Gandolf provide a practitioner's step-by-step guide to starting, growing and profiting from cash-pay healthcare. You'll find checklists, bulleted lists, helpful examples, and a guide to the best resources to help you along the way. No matter where you are along the continuum of generating additional revenue, you'll come away more confident and committed to growing your practice and serving your patients.
  data mapping in healthcare: Quality Improvement Research Richard Grol, Richard W. Baker, Fiona Moss, 2003-11-21 Quality improvement in health care is now a stated objective of health services worldwide, yet effective delivery is not always apparent. This book discusses research methods that should help to improve the delivery of quality.
  data mapping 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.
  data mapping in healthcare: Computational Science and Its Applications – ICCSA 2021 Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blečić, David Taniar, Bernady O. Apduhan, Ana Maria A. C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, 2021-09-09 ​​The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, blockchain, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, applied mathematics human computer interaction, software design engineering, and others. Part IX of the set includes the proceedings of the following events: ​​13th International Symposium on Software Engineering Processes and Applications (SEPA 2021); International Workshop on Sustainability Performance Assessment: models, approaches and applications toward interdisciplinary and integrated solutions (SPA 2021).
Data Mapping Best Practices (2016 update) - Indian Hills …
Data Mapping Best Practices (2016 update) This practice brief supersedes the November 2013 practice brief “Data Mapping Best Practices.” The healthcare industry collects vast amounts of …

Common Data Model Harmonization (CDMH) and Open …
In this report describing the development process and approach taken to harmonize the CDMs by leveraging standards and controlled terminologies, we include publicly available resources that …

Healthcare Data Governance - AHIMA
Organizations must establish the basic framework of collection, retention, use, accessibility and sharing of healthcare data. This framework may consist of policies, procedures, standards, …

Data Mapping [What I know about it, anyhow]
Mapping is the process of defining a set of maps. Maps are developed in accordance with a documented rationale, for a given purpose and as a result there may be different maps between …

Transforming Healthcare with GIS: A Strategic Blueprint for
Healthcare systems can make critical determinations about where to site new facilities, expand existing operations, or deploy modern service delivery models such as telehealth services or …

Data Mapping and Its Impact on Data Integrity
This thought leadership paper explores the relationship of data mapping and data integrity assurance by providing guidance to avoid adverse outcomes involving the use of maps. …

Data Mapping using Machine Learning - lexjansen.com
This paper talks about capturing source to destination data mapping as metadata into centralized libraries and applying Machine Learning algorithms to streamline and predict mapping for newer …

Data Mapping Healthcare
book provides pragmatic and hands on solutions for working with healthcare data from data extraction to cleaning and harmonization to feature engineering Author Andrew Nguyen covers …

A Taxonomy of Definitions for the Health Data Ecosystem
Understanding the evolving health data ecosystem presents new challenges for policymakers and industry ranging from health insurance companies already deeply entrenched in the health …

Data Governance Handbook | Implementing Data Management …
It was found that the productivity reports were programmed to run off a list of visit-types from a master file. The new visit type had not been added to this file.

Common Data Model Harmonization
Generation, or Common Data Model Harmonization (CDMH) project, aims to support research and analyses across multiple data networks by harmonizing several existing CDMs.

AHIMA Public Policy Statement: Data Quality and Integrity
One of the main tenets of data quality and integrity is the completeness of data. Policy should support the development and implementation of consistent data standards to support data …

A blueprint for success in healthcare data and analytics (D&A)
Against that backdrop, the challenge for healthcare organizations is threefold. First, they must genuinely understand the clinical and operational questions they are trying to answer through D&A.

Open-Source Big Data Analytics in Healthcare - OHDSI
Patients and clinicians and other decision-makers around the world use OHDSI tools and evidence every day. Transparent: All work products within OHDSI will be open source and publicly …

Data Elements in Electronic Health Records (EHRs) - Centers …
To produce data that can be used appropriately for health statistics, it is suggested that we re-focus on the population health questions that need to be answered first, then ascertain what data …

Maximizing Data Interoperability and Integration to Support
Public and private payer initiatives are reforming health care by providing incentives to deliver higher quality and lower cost care through value-based care models.

DATA FOR BETTER HEALTH: DESIGNING SOLUTIONS FOR …
health and healthcare outcomes. Through this effort, AHIMA is providing tools, resources, and education to health information professionals, thought leaders, policymakers, and the public that …

Health Data Governance Summit Pre-read: The health data …
Jun 30, 2021 · Data landscaping can help to identify the data stewards responsible for managing and ensuring access to a dataset, the different types of data users, and the relationships …

Mapping Healthcare Data Sources in India - SAGE Journals
During the last decade, India has witnessed a sharp rise in the number of healthcare data sources as identified in this review. These sources have increased data availability in multiple data deficient …

Health Care Facilities Mapping and Database Creation Using …
Abstract - The study investigated the spatial distribution of health care centres in Chikun local government area of Kaduna state, Nigeria with a view to use Geographic Information Systems …

Data Mapping Best Practices (2016 update) - Indian Hills Com…
Data Mapping Best Practices (2016 update) This practice brief supersedes the November 2013 practice brief “Data Mapping Best …

Common Data Model Harmonization (CDMH) and O…
In this report describing the development process and approach taken to harmonize the CDMs by leveraging standards and …

Healthcare Data Governance - AHIMA
Organizations must establish the basic framework of collection, retention, use, accessibility and sharing of healthcare …

Data Mapping [What I know about it, anyhow]
Mapping is the process of defining a set of maps. Maps are developed in accordance with a documented rationale, for a given …

Transforming Healthcare with GIS: A Strategic Blueprint for
Healthcare systems can make critical determinations about where to site new facilities, expand existing operations, or …