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| data quality management 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 quality management in healthcare: Crossing the Quality Chasm Institute of Medicine, Committee on Quality of Health Care in America, 2001-07-19 Second in a series of publications from the Institute of Medicine's Quality of Health Care in America project Today's health care providers have more research findings and more technology available to them than ever before. Yet recent reports have raised serious doubts about the quality of health care in America. Crossing the Quality Chasm makes an urgent call for fundamental change to close the quality gap. This book recommends a sweeping redesign of the American health care system and provides overarching principles for specific direction for policymakers, health care leaders, clinicians, regulators, purchasers, and others. In this comprehensive volume the committee offers: A set of performance expectations for the 21st century health care system. A set of 10 new rules to guide patient-clinician relationships. A suggested organizing framework to better align the incentives inherent in payment and accountability with improvements in quality. Key steps to promote evidence-based practice and strengthen clinical information systems. Analyzing health care organizations as complex systems, Crossing the Quality Chasm also documents the causes of the quality gap, identifies current practices that impede quality care, and explores how systems approaches can be used to implement change. |
| data quality management in healthcare: Healthcare Quality Management Zachary Pruitt, PhD, MHA, CPH, Candace S. Smith, PhD, RN, NEA-BC, Eddie Perez-Ruberte, 2020-02-28 Healthcare Quality Management: A Case Study Approach is the first comprehensive case-based text combining essential quality management knowledge with real-world scenarios. With in-depth healthcare quality management case studies, tools, activities, and discussion questions, the text helps build the competencies needed to succeed in quality management. Written in an easy-to-read style, Part One of the textbook introduces students to the fundamentals of quality management, including history, culture, and different quality management philosophies, such as Lean and Six Sigma. Part One additionally explains the A3 problem-solving template used to follow the Plan-Do-Study-Act (PDSA) or Define, Measure, Analyze, Improve, and Control (DMAIC) cycles, that guides your completion of the problem-solving exercises found in Part Two. The bulk of the textbook includes realistic and engaging case studies featuring common quality management problems encountered in a variety of healthcare settings. The case studies feature engaging scenarios, descriptions, opinions, charts, and data, covering such contemporary topics as provider burnout, artificial intelligence, the opioid overdose epidemic, among many more. Serving as a powerful replacement to more theory-based quality management textbooks, Healthcare Quality Management provides context to challenging situations encountered by any healthcare manager, including the health administrator, nurse, physician, social worker, or allied health professional. KEY FEATURES: 25 Realistic Case Studies–Explore challenging Process Improvement, Patient Experience, Patient Safety, and Performance Improvement quality management scenarios set in various healthcare settings Diverse Author Team–Combines the expertise and knowledge of a health management educator, a Chief Nursing Officer at a large regional hospital, and a health system-based Certified Lean Expert Podcasts–Listen to quality management experts share stories and secrets on how to succeed, work in teams, and apply tools to solve problems Quality Management Tools–Grow your quality management skill set with 25 separate quality management tools and approaches tied to the real-world case studies Competency-Based Education Support–Match case studies to professional competencies, such as analytical skills, community collaboration, and interpersonal relations, using case-to-competency crosswalks for health administration, nursing, medicine, and the interprofessional team Comprehensive Instructor’s Packet–Includes PPTs, extensive Excel data files, an Instructor’s Manual with completed A3 problem-solving solutions for each Case Application Exercise, and more! Student ancillaries–Includes data files and A3 template |
| data quality management 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 quality management in healthcare: Healthcare Analytics for Quality and Performance Improvement Trevor L. Strome, 2013-10-02 Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement. |
| data quality management in healthcare: Health Care Quality Management Thomas K. Ross, 2014-01-07 In today’s challenging health care environment, health care organizations are faced with improving patient outcomes, redesigning business processes, and executing quality and risk management initiatives. Health Care Quality Management offers an introduction to the field and practice of quality management and reveals the best practices and strategies health care organizations can adopt to improve patient outcomes and program quality. Filled with illustrative case studies that show how business processes can be restructured to achieve improvements in quality, risk reduction, and other key business results and outcomes Clearly demonstrates how to effectively use process analysis tools to identify issues and causes, select corrective actions, and monitor implemented solutions Includes vital information on the use of statistical process control to monitor system performance (variables) and outcomes (attributes) Also contains multiple data sets that can be used to practice the skills and tools discussed and reviews examples of where and how the tools have been applied in health care Provides information on root cause analysis and failure mode effects analysis and offers, as discussion, the clinical tools and applications that are used to improve patient care By emphasizing the tools of statistics and information technology, this book teaches future health care professionals how to identify opportunities for quality improvement and use the tools to make those improvements. |
| data quality management in healthcare: Medical Quality Management Angelo P. Giardino, Lee Ann Riesenberg, Prathibha Varkey, 2020-08-31 This comprehensive medical textbook is a compendium of the latest information on healthcare quality. The text provides knowledge about the theory and practical applications for each of the core areas that comprise the field of medical quality management as well as insight and essential briefings on the impact of new healthcare technologies and innovations on medical quality and improvement. The third edition provides significant new content related to medical quality management and quality improvement, a user-friendly format, case studies, and updated learning objectives. This textbook also serves as source material for the American Board of Medical Quality in the development of its core curriculum and certification examinations. Each chapter is designed for a review of the essential background, precepts, and exemplary practices within the topical area: Basics of Quality Improvement Data Analytics for the Improvement of Healthcare Quality Utilization Management, Case Management, and Care Coordination Economics and Finance in Medical Quality Management External Quality Improvement — Accreditation, Certification, and Education The Interface Between Quality Improvement and Law Ethics and Quality Improvement With the new edition of Medical Quality Management: Theory and Practice, the American College of Medical Quality presents the experience and expertise of its contributors to provide the background necessary for healthcare professionals to assume the responsibilities of medical quality management in healthcare institutions, provide physicians in all medical specialties with a core body of knowledge related to medical quality management, and serve as a necessary guide for healthcare administrators and executives, academics, directors, medical and nursing students and residents, and physicians and other health practitioners. |
| data quality management in healthcare: Pocket Guide to Quality Improvement in Healthcare Reneè Roberts-Turner, Rahul K. Shah, 2021-05-21 This text will act as a quick quality improvement reference and resource for every role within the healthcare system including physicians, nurses, support staff, security, fellows, residents, therapists, managers, directors, chiefs, and board members. It aims to provide a broad overview of quality improvement concepts and how they can be immediately pertinent to one's role. The editors have used a tiered approach, outlining what each role needs to lead a QI project, participate as a team member, set goals and identify resources to drive improvements in care delivery. Each section of the book targets a specific group within the healthcare organization. Pocket Guide to Quality Improvement in Healthcare will guide the individual, as well as the organization to fully engage all staff in QI, creating a safety culture, and ultimately strengthening care delivery. |
| data quality management in healthcare: Measuring Quality Improvement in Healthcare Raymond G. Carey, Robert C. Lloyd, 2001-09-25 This ground-breaking book addresses the critical, growing need among health care administrators and practitioners to measure the effectiveness of quality improvement efforts. Written by respected healthcare quality professionals, Measuring Quality Improvement in Healthcare covers practical applications of the tools and techniques of statistical process control (SPC), including control charts, in healthcare settings. The authors' straightforward discussions of data collection, variation, and process improvement set the context for the use and interpretation of control charts. Their approach incorporates the voice of the customer as a key element driving the improvement processes and outcomes. The core of the book is a set of 12 case studies that show how to apply statistical thinking to health care process, and when and how to use different types of control charts. The practical, down-to-earth orientation of the book makes it accessible to a wide readership. Only authors who have used statistics and control charts to solve real-world healthcare problems could have written a book so practical and timely. - Barry S. Bader, Publisher The Quality Letter for Healthcare Leaders Many clinicians and other healthcare leaders underestimate the great contributions that better statistical thinking could make toward reducing costs and improving outcomes. This fascinating and timely book is a fine guide for getting started. - Donald M. Berwick, M.D. President and CEO, Institute for Healthcare Improvement Associate Professor of Pediatrics, Harvard Medical School Contents: Planning Your CQI Journey, Preparing to Collect Data, Data Collection, Understanding Variation, Using Run and Control Charts to Analyze Process Variation, Control Chart Case Studies, Developing Improvement Strategies, Using Patient Surveys for CQI, Formulas for Calculating Control Limits |
| data quality management in healthcare: Improving Data Quality Regional Office for the Western Pacific World Health Organization, World Health Organization. Regional Office for the Western Pacific, 2003-01-01 This publication provides a set of guidelines for health care workers, health information managers and administrators to help them focus on improving the timeliness, accuracy and reliability of health care data. They describe key activities and tasks to be considered when addressing the question of data quality in health care, regardless of the setting or size of organisations. |
| data quality management in healthcare: Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making Institute of Medicine, Roundtable on Research and Development of Drugs, Biologics, and Medical Devices, 1999-07-27 In an effort to increase knowledge and understanding of the process of assuring data quality and validity in clinical trials, the IOM hosted a workshop to open a dialogue on the process to identify and discuss issues of mutual concern among industry, regulators, payers, and consumers. The presenters and panelists together developed strategies that could be used to address the issues that were identified. This IOM report of the workshop summarizes the present status and highlights possible strategies for making improvements to the education of interested and affected parties as well as facilitating future planning. |
| data quality management in healthcare: Measuring the Quality of Health Care The National Roundtable on Health Care Quality, Institute of Medicine, 1999-02-23 The National Roundtable on Health Care Quality was established in 1995 by the Institute of Medicine. The Roundtable consists of experts formally appointed through procedures of the National Research Council (NRC) who represent both public and private-sector perspectives and appropriate areas of substantive expertise (not organizations). From the public sector, heads of appropriate Federal agencies serve. It offers a unique, nonadversarial environment to explore ongoing rapid changes in the medical marketplace and the implications of these changes for the quality of health and health care in this nation. The Roundtable has a liaison panel focused on quality of care in managed care organizations. The Roundtable convenes nationally prominent representatives of the private and public sector (regional, state and federal), academia, patients, and the health media to analyze unfolding issues concerning quality, to hold workshops and commission papers on significant topics, and when appropriate, to produce periodic statements for the nation on quality of care matters. By providing a structured opportunity for regular communication and interaction, the Roundtable fosters candid discussion among individuals who represent various sides of a given issue. |
| data quality management in healthcare: Information Technology and Data in Healthcare David Hartzband, 2019-12-09 Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital for hospitals and health systems to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. This book addresses several topics important to the understanding and use of data in healthcare. First, it provides a formal explanation based on epistemology (theory of knowledge) of what data actually is, what we can know about it, and how we can reason with it. The culture of data is also covered and where it fits into healthcare. Then, data quality is addressed, with a historical appreciation, as well as new concepts and insights derived from the author’s 35 years of experience in technology. The author provides a description of what healthcare data analysis is and how it is changing in the era of abundant data. Just as important is the topic of infrastructure and how it provides capability for data use. The book also describes how healthcare information infrastructure needs to change in order to meet current and future needs. The topics of artificial intelligence (AI) and machine learning in healthcare are also addressed. The author concludes with thoughts on the evolution of the role and use of data and information going into the future. |
| data quality management in healthcare: Quality Management in Intensive Care Bertrand Guidet, Andreas Valentin, Hans Flaatten, 2016-02-15 This book is one of the first to comprehensively summarise the latest thinking and research in the rapidly evolving field of quality management in intensive care. Quality indicators and outcome measures are discussed with a practical focus on patient-centred, evidence-based implementation for safer and more effective clinical practice. Chapters on topics such as teambuilding, patient satisfaction, mortality and morbidity, and electronic management systems are organised into three sections, covering quality management at the scale of the individual patient, the intensive care unit, and the national and international level. Written by a team of over forty international experts in the specialty, with editors who have been heavily involved for many years with the European Society of Intensive Care Medicine, the book reflects commonly accepted goals and guidelines for best practice, and will be valuable for practitioners worldwide. The ideal one-stop resource for intensive care physicians as well as ICU and hospital managers. |
| data quality management in healthcare: Healthcare Data Analytics and Management Nilanjan Dey, Amira S. Ashour, Simon James Fong, Chintan Bhatt, 2018-11-15 Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. - Covers data analysis, management and security concepts and tools in the healthcare domain - Highlights electronic medical health records and patient information records - Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining - Includes multidisciplinary contributions in relation to healthcare applications and challenges |
| data quality management 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 quality management in healthcare: Quality Health Care Lloyd, 2017-08-29 Written by an internationally-recognized expert in the field of quality management, this text is an essential guide for understanding how to plan and implement a successful quality measurement program in your healthcare facility. It begins by presenting an overview of the context for quality measurement, the forces influencing the demand for quality reform, how to listen to the voice of the customer, and the characteristics of quality that customers value most. Students will also learn how to select and define indicators to collect data and how to organize data into a dashboard that can provide feedback on progress toward quality measurement. Finally, this book explores how to analyze the data by detailing how variation lives in your data, and whether this variation is acceptable. |
| data quality management in healthcare: Evaluation of the Department of Veterans Affairs Mental Health Services National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Care Services, Committee to Evaluate the Department of Veterans Affairs Mental Health Services, 2018-03-29 Approximately 4 million U.S. service members took part in the wars in Afghanistan and Iraq. Shortly after troops started returning from their deployments, some active-duty service members and veterans began experiencing mental health problems. Given the stressors associated with war, it is not surprising that some service members developed such mental health conditions as posttraumatic stress disorder, depression, and substance use disorder. Subsequent epidemiologic studies conducted on military and veteran populations that served in the operations in Afghanistan and Iraq provided scientific evidence that those who fought were in fact being diagnosed with mental illnesses and experiencing mental healthâ€related outcomesâ€in particular, suicideâ€at a higher rate than the general population. This report provides a comprehensive assessment of the quality, capacity, and access to mental health care services for veterans who served in the Armed Forces in Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn. It includes an analysis of not only the quality and capacity of mental health care services within the Department of Veterans Affairs, but also barriers faced by patients in utilizing those services. |
| data quality management in healthcare: The Health Care Data Guide Lloyd P. Provost, Sandra K. Murray, 2011-12-06 The Health Care Data Guide is designed to help students and professionals build a skill set specific to using data for improvement of health care processes and systems. Even experienced data users will find valuable resources among the tools and cases that enrich The Health Care Data Guide. Practical and step-by-step, this book spotlights statistical process control (SPC) and develops a philosophy, a strategy, and a set of methods for ongoing improvement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for a wide range of applications including evaluating current process performance, searching for ideas for and determining evidence of improvement, and tracking and documenting sustainability of improvement. A comprehensive overview of graphical methods in SPC includes Shewhart charts, run charts, frequency plots, Pareto analysis, and scatter diagrams. Other topics include stratification and rational sub-grouping of data and methods to help predict performance of processes. Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data. Companion Web site: www.josseybass.com/go/provost |
| data quality management in healthcare: Medicare Institute of Medicine, Committee to Design a Strategy for Quality Review and Assurance in Medicare, 1990-02-01 Health care for the elderly American is among our nation's more pressing social issues. Our society wishes to ensure quality health care for all older people, but there is growing concern about our ability to maintain and improve quality in the face of efforts to contain health care costs. Medicare: A Strategy for Quality Assurance answers the U.S. Congress' call for the Institute of Medicine to design a strategic plan for assessing and assuring the quality of medical care for the elderly. This book presents a proposed strategic plan for improving quality assurance in the Medicare program, along with steps and timetables for implementing the plan by the year 2000 and the 10 recommendations for action by Congress. The book explores quality of careâ€how it is defined, measured, and improvedâ€and reviews different types of quality problems. Major issues that affect approaches to assessing and assuring quality are examined. Medicare: A Strategy for Quality Assurance will be immediately useful to a wide audience, including policymakers, health administrators, individual providers, specialists in issues of the older American, researchers, educators, and students. |
| data quality management in healthcare: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data quality management in healthcare: Infonomics Douglas B. Laney, 2017-09-05 Many senior executives talk about information as one of their most important assets, but few behave as if it is. They report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. Corporations typically exhibit greater discipline in tracking and accounting for their office furniture than their data. Infonomics is the theory, study, and discipline of asserting economic significance to information. It strives to apply both economic and asset management principles and practices to the valuation, handling, and deployment of information assets. This book specifically shows: CEOs and business leaders how to more fully wield information as a corporate asset CIOs how to improve the flow and accessibility of information CFOs how to help their organizations measure the actual and latent value in their information assets. More directly, this book is for the burgeoning force of chief data officers (CDOs) and other information and analytics leaders in their valiant struggle to help their organizations become more infosavvy. Author Douglas Laney has spent years researching and developing Infonomics and advising organizations on the infinite opportunities to monetize, manage, and measure information. This book delivers a set of new ideas, frameworks, evidence, and even approaches adapted from other disciplines on how to administer, wield, and understand the value of information. Infonomics can help organizations not only to better develop, sell, and market their offerings, but to transform their organizations altogether. Doug Laney masterfully weaves together a collection of great examples with a solid framework to guide readers on how to gain competitive advantage through what he labels the unruly asset – data. The framework is comprehensive, the advice practical and the success stories global and across industries and applications. Liz Rowe, Chief Data Officer, State of New Jersey A must read for anybody who wants to survive in a data centric world. Shaun Adams, Head of Data Science, Betterbathrooms.com Phenomenal! An absolute must read for data practitioners, business leaders and technology strategists. Doug's lucid style has a set a new standard in providing intelligible material in the field of information economics. His passion and knowledge on the subject exudes thru his literature and inspires individuals like me. Ruchi Rajasekhar, Principal Data Architect, MISO Energy I highly recommend Infonomics to all aspiring analytics leaders. Doug Laney’s work gives readers a deeper understanding of how and why information should be monetized and managed as an enterprise asset. Laney’s assertion that accounting should recognize information as a capital asset is quite convincing and one I agree with. Infonomics enjoyably echoes that sentiment! Matt Green, independent business analytics consultant, Atlanta area If you care about the digital economy, and you should, read this book. Tanya Shuckhart, Analyst Relations Lead, IRI Worldwide |
| data quality management in healthcare: OECD Handbook for Internationally Comparative Education Statistics Concepts, Standards, Definitions and Classifications OECD, 2004-04-23 This handbook aims to facilitate a greater understanding of the OECD statistics and indicators produced and so allow for their more effective use in policy analysis. |
| data quality management in healthcare: Introduction to Healthcare Quality Management, Second Edition Patrice Spath, 2013 Introduction to Healthcare Quality Management, Second Edition, explains the basic principles and techniques of quality management in healthcare. This second edition features a new chapter devoted exclusively to the use of high-reliability concepts that help organizations achieve safety, quality, and efficiency goals. By using this easy-to-read book, complete with helpful charts and diagrams, your students will examine a range of topics, from measuring performance to creating high-quality services that result in satisfied customers. The book is packed with practical examples and case studies that apply quality concepts and tools to real-life situations. Each chapter contains a list of key words and a glossary to help students understand the vocabulary of healthcare quality management. As an added bonus to this edition, each chapter includes an expanded list of websites to find additional resources to customize and enhance your education. Your students will learn about the following topics: Quality characteristics most important to healthcare stakeholders, including payers and consumers Regulatory mandates and accreditation standards that influence healthcare quality activities Proper techniques for gathering and effectively analyzing healthcare performance measurement data New technology-based services that will improve the patient experience Key tactics and strategies that organizational leaders and improvement project teams must implement to accomplish quality goals Methods for redesigning healthcare processes to achieve more reliable performance Patient safety initiatives that reduce harmful medical errors Resource management activities that improve continuity of care and prevent service over and underuse Organizational factors that affect quality management and performance reliability. Instructor resources include a test bank, PowerPoint slides, and answers to in-book questions. A transition guide is available in the tab above. |
| data quality management in healthcare: Quality Management in Health Care: Principles and Methods Donald Lighter, Douglas C. Fair, 2004-02-24 Quality Management in Health Care: Principles and Methods, Second Edition explores quality management processes in health care using specific analytical methods in addition to emphasizing general theory and practical applications. Topics that are examined include: statistical process control and group management, disease management, clinical practice guidelines, and implementation strategies. the writing is clear and understandable, and the text makes effective use of examples, illustrations and case studies to elucidate key concepts. Additionally, each chapter ends with exercises designed to |
| data quality management in healthcare: Patient Safety and Quality Improvement in Healthcare Rahul K. Shah, Sandip A. Godambe, 2020-12-15 This text uses a case-based approach to share knowledge and techniques on how to operationalize much of the theoretical underpinnings of hospital quality and safety. Written and edited by leaders in healthcare, education, and engineering, these 22 chapters provide insights as to where the field of improvement and safety science is with regards to the views and aspirations of healthcare advocates and patients. Each chapter also includes vignettes to further solidify the theoretical underpinnings and drive home learning. End of chapter commentary by the editors highlight important concepts and connections between various chapters in the text. Patient Safety and Quality Improvement in Healthcare: A Case-Based Approach presents a novel approach towards hospital safety and quality with the goal to help healthcare providers reach zero harm within their organizations. |
| data quality management 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 quality management in healthcare: Measuring Data Quality for Ongoing Improvement Laura Sebastian-Coleman, 2012-12-31 The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You'll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. - Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges - Enables discussions between business and IT with a non-technical vocabulary for data quality measurement - Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation |
| data quality management in healthcare: Applying Quality Management in Healthcare Patrice Spath, Diane L. Kelly, 2017 |
| data quality management in healthcare: Exploratory Data Mining and Data Cleaning Tamraparni Dasu, Theodore Johnson, 2003-08-01 Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. |
| data quality management in healthcare: Quality Improvement in Behavioral Health William O'Donohue, Alexandros Maragakis, 2016-06-17 This innovative volume presents a cogent case for quality improvement (QI) in behavioral healthcare as ethical practice, solid science, and good business. Divided between foundational concepts, key QI tools and methods, and emerging applications, it offers guidelines for raising care standards while addressing ongoing issues of treatment validity, staffing and training, costs and funding, and integration with medical systems. Expert contributors review the implications and potential of QI in diverse areas such as treatment of entrenched mental disorders, in correctional facilities, and within the professional context of the American Psychological Association. The insights, examples, and strategies featured will increase in value as behavioral health becomes more prominent in integrated care and vital to large-scale health goals. Included in the coverage: Behavioral health conditions: direct treatment costs and indirect social costs. /liliQuality improvement and clinical psychological science. · Process mapping to improve quality in behavioral health service delivery. · Checklists for quality improvement and evaluation in behavioral health. · Creating a quality improvement system for an integrated care program: the why, what, and how to measure. · Feedback Informed Treatment (FIT): improving the outcome of psychotherapy one person at a time. Quality Improvement i n Behavioral Healthcare gives health psychologists, public health professionals, and health administrators a real-world framework for maintaining quality services in a rapidly evolving health landscape. |
| data quality management in healthcare: To Err Is Human Institute of Medicine, Committee on Quality of Health Care in America, 2000-03-01 Experts estimate that as many as 98,000 people die in any given year from medical errors that occur in hospitals. That's more than die from motor vehicle accidents, breast cancer, or AIDSâ€three causes that receive far more public attention. Indeed, more people die annually from medication errors than from workplace injuries. Add the financial cost to the human tragedy, and medical error easily rises to the top ranks of urgent, widespread public problems. To Err Is Human breaks the silence that has surrounded medical errors and their consequenceâ€but not by pointing fingers at caring health care professionals who make honest mistakes. After all, to err is human. Instead, this book sets forth a national agendaâ€with state and local implicationsâ€for reducing medical errors and improving patient safety through the design of a safer health system. This volume reveals the often startling statistics of medical error and the disparity between the incidence of error and public perception of it, given many patients' expectations that the medical profession always performs perfectly. A careful examination is made of how the surrounding forces of legislation, regulation, and market activity influence the quality of care provided by health care organizations and then looks at their handling of medical mistakes. Using a detailed case study, the book reviews the current understanding of why these mistakes happen. A key theme is that legitimate liability concerns discourage reporting of errorsâ€which begs the question, How can we learn from our mistakes? Balancing regulatory versus market-based initiatives and public versus private efforts, the Institute of Medicine presents wide-ranging recommendations for improving patient safety, in the areas of leadership, improved data collection and analysis, and development of effective systems at the level of direct patient care. To Err Is Human asserts that the problem is not bad people in health careâ€it is that good people are working in bad systems that need to be made safer. Comprehensive and straightforward, this book offers a clear prescription for raising the level of patient safety in American health care. It also explains how patients themselves can influence the quality of care that they receive once they check into the hospital. This book will be vitally important to federal, state, and local health policy makers and regulators, health professional licensing officials, hospital administrators, medical educators and students, health caregivers, health journalists, patient advocatesâ€as well as patients themselves. First in a series of publications from the Quality of Health Care in America, a project initiated by the Institute of Medicine |
| data quality management in healthcare: Laboratory Quality Management System World Health Organization, 2011 Achieving, maintaining and improving accuracy, timeliness and reliability are major challenges for health laboratories. Countries worldwide committed themselves to build national capacities for the detection of, and response to, public health events of international concern when they decided to engage in the International Health Regulations implementation process. Only sound management of quality in health laboratories will enable countries to produce test results that the international community will trust in cases of international emergency. This handbook was developed through collaboration between the WHO Lyon Office for National Epidemic Preparedness and Response, the United States of America Centers for Disease Control and Prevention (CDC) Division of Laboratory Systems, and the Clinical and Laboratory Standards Institute (CLSI). It is based on training sessions and modules provided by the CDC and WHO in more than 25 countries, and on guidelines for implementation of ISO 15189 in diagnostic laboratories, developed by CLSI. This handbook is intended to provide a comprehensive reference on Laboratory Quality Management System for all stakeholders in health laboratory processes, from management, to administration, to bench-work laboratorians. This handbook covers topics that are essential for quality management of a public health or clinical laboratory. They are based on both ISO 15189 and CLSI GP26-A3 documents. Each topic is discussed in a separate chapter. The chapters follow the framework developed by CLSI and are organized as the 12 Quality System Essentials. |
| data quality management in healthcare: The Quality Handbook for Health Care Organizations Yosef D. Dlugacz, Andrea Restifo, Alice Greenwood, 2004-04-02 The Quality Handbook for Health Care Organizations This important book is a practical, theory-based resource on the topic of health care quality management written for health care administrators and practitioners. It offers the tools needed to help managers make decisions, prioritize resources (financial and human), and analyze and improve the care they deliver. The Handbook offers a hands-on approach to specific topics such as the implementation of managerial goals, instructions for developing accurate measurements for evaluating care, the utilization of data as a basis for process improvement, exploration of quality management tools and techniques, guidelines for the complex integration of collaborative services in health care, and methods for effective communication and improving accountability. In addition, the book is filled with illustrative examples of methods for ensuring appropriate oversight of clinical and quality activities, offers solutions for addressing and preventing adverse events, and explores the important people-to-people interactions that ultimately define excellence in medical care. Praise for The Quality Handbook for Health Care Organizations The Quality Handbook for Health Care Organizations offers students of health care policy and management a unique opportunity to learn firsthand from one of the nation’s leading experts in health care quality. Dr. Dlugacz’s passion for promoting the highest ideals of quality in health care should inspire future generations of health care professionals. –Alan M. Leiken, chair, Department of Health Care Policy and Management, School of Health Care Policy and Management, School of Health Technology and Management, Health Sciences Center, State University of New York at Stony Brook This outstanding book combines both the quantitative aspects of data collection and analysis with the critical human behaviors that make up a health care institution’s culture. Only when these two factors combine do true ‘quality’ decisions and patient care result. –Stuart R. Levine, author, The Six Fundamentals of Success The Quality Handbook reflects the authors’ combination of technical know-how, years of experience, and the enthusiasm for the complex challenge of their work. –Margaret E. O’Kane, president, National Committee of Quality Assurance |
| data quality management in healthcare: Epidemiology and the Delivery of Health Care Services Denise M. Oleske, 2012-12-06 In this introductory textbook to epidemiology, students will discover the knowledge and skills required for managing population-based health care under health reform. Fundamental epidemiological techniques are presented teaching students to assess the health status of populations served; determine appropriate interventions based upon knowledge of factors which affect health status; and evaluate the impact of health care systems, programs, technologies, and policies on the health status of populations. Each chapter includes case studies and discussion questions. |
| data quality management in healthcare: Statistical Methods for Healthcare Performance Monitoring Alex Bottle, Paul Aylin, 2016-08-05 Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions. Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists. Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits. |
| data quality management in healthcare: Digital Technology Advancements in Knowledge Management Gyamfi, Albert, Williams, Idongesit, 2021-06-18 Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage. |
| data quality management in healthcare: Information Quality Management Latif Al-Hakim, 2007-01-01 Technologies such as the Internet and mobile commerce bring with them ubiquitous connectivity, real-time access, and overwhelming volumes of data and information. The growth of data warehouses and communication and information technologies has increased the need for high information quality management in organizations. Information Quality Management: Theory and Applications provides solutions to information quality problems becoming increasingly prevalent.Information Quality Management: Theory and Applications provides insights and support for professionals and researchers working in the field of information and knowledge management, information quality, practitioners and managers of manufacturing, and service industries concerned with the management of information. |
| data quality management in healthcare: Data and Information Quality Carlo Batini, Monica Scannapieco, 2016-03-23 This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms. |
| data quality management 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 and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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