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| excel for clinical data management: Practical Guide to Clinical Data Management Susanne Prokscha, 1999-01-31 Clinical data management (CDM) has changed from being an essentially clerical task in the late 1970s and early 1980s to a highly computerized, highly specialized field today. And clinical data manages have had to adapt their data management systems and processes accordingly. Practical Guide to Clinical Data Management steers you through a basic understanding of the role of data management in clinical trials and includes more advanced topics such as CDM systems, SOPs, and quality assurance. This book helps you ensure GCP, manage laboratory data, and deal with the kinds of clinical data that can cause difficulties in database applications. With the tools this book provides, you'll learn how to: Ensure that your DMB system is in compliance with federal regulations Build a strategic data management and databsing plan Track and record CRFs Deal with problem data, adverse event data, and legacy data Manage and store lab data Identify and manage discrepancies Ensure quality control over reports Choose a CDM system that is right for your company Create and implement a system validation plan and process Set up and enforce data collection standards Develop test plans and change control systems This book is your guide to finding the most successful and practical options for effective clinical data management. |
| excel for clinical data management: 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 |
| excel for clinical data management: Practical Guide to Clinical Data Management, Third Edition Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers. |
| excel for clinical data management: Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare Dwivedi, Ashish N., 2009-05-31 This book presents theoretical and empirical research on the value of information technology in healthcare--Provided by publisher. |
| excel for clinical data management: Clinical Data Management Richard K. Rondel, Sheila A. Varley, Colin F. Webb, 2000-02-03 Extensively revised and updated, with the addition of new chapters and authors, this long-awaited second edition covers all aspects of clinical data management. Giving details of the efficient clinical data management procedures required to satisfy both corporate objectives and quality audits by regulatory authorities, this text is timely and an important contribution to the literature. The volume: * is written by well-known and experienced authors in this area * provides new approaches to major topics in clinical data management * contains new chapters on systems software validation, database design and performance measures. It will be invaluable to anyone in the field within the pharmaceutical industry, and to all biomedical professionals working in clinical research. |
| excel for clinical data management: A Manager's Guide to the Design and Conduct of Clinical Trials Phillip I. Good, 2003-05-14 This engaging and non-technical guide to clinical trials covers issues study design, organization, management, analysis, recruitment, reporting, software, and monitoring. Free from the jargon-laden treatment of other books, A Manager’s Guide to the Design and Conduct Clinical Trials is built upon the formula of first planning, then implementing, and finally performing essential checks. Offers an executive level presentation of managerial guidelines as well as handy checklists accompanied by extracts from submitted protocols Includes checklists, examples, and tips, as well as a useful appendix on available software Covers e-submissions and use of computers for direct data acquisition Incorporates humorous yet instructive and true anecdotes to illustrate common pitfalls |
| excel for clinical data management: Clinical Technologies: Concepts, Methodologies, Tools and Applications Management Association, Information Resources, 2011-05-31 This multi-volume book delves into the many applications of information technology ranging from digitizing patient records to high-performance computing, to medical imaging and diagnostic technologies, and much more-- |
| excel for clinical data management: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2023-01-18 Praise for the first edition: DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan |
| excel for clinical data management: 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. |
| excel for clinical data management: Clinical Analytics and Data Management for the DNP Martha L. Sylvia, PhD, MBA, RN, Mary F. Terhaar, PhD, RN, ANEF, FAAN, 2018-03-28 Praise for the First Edition: “DNP students may struggle with data management, since their projects are not research, but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects. Score: 98, 5 Stars --Doody's Medical Reviews This is the only text to deliver the strong data management knowledge and skills that are required competencies for all DNP students. It enables readers to design data tracking and clinical analytics in order to rigorously evaluate clinical innovations/programs for improving clinical outcomes, and to document and analyze change. The second edition is greatly expanded and updated to address major changes in our health care environment. Incorporating faculty and student input, it now includes modalities such as SPSS, Excel, and Tableau to address diverse data management tasks. Eleven new chapters cover the use of big data analytics, ongoing progress towards value-based payment, the ACA and its future, shifting of risk and accountability to hospitals and clinicians, advancement of nursing quality indicators, and new requirements for Magnet certification. The text takes the DNP student step by step through the complete process of data management from planning to presentation, and encompasses the scope of skills required for students to apply relevant analytics to systematically and confidently tackle the clinical interventions data obtained as part of the DNP student project. Of particular value is a progressive case study illustrating multiple techniques and methods throughout the chapters. Sample data sets and exercises, along with objectives, references, and examples in each chapter, reinforce information. Key Features: Provides extensive content for rigorously evaluating DNP innovations/projects Takes DNP students through the complete process of data management from planning through presentation Includes a progressive case study illustrating multiple techniques and methods Offers very specific examples of application and utility of techniques Delivers sample data sets, exercises, PowerPoint slides and more, compiled in Supplemental Materials and an Instructor Manual |
| excel for clinical data management: Clinical Data Manager - The Comprehensive Guide VIRUTI SHIVAN, In the fast-evolving world of healthcare research, the role of a Clinical Data Manager has never been more critical. This guidebook serves as the ultimate roadmap for professionals aiming to excel in this challenging and rewarding field. Without the distraction of images or illustrations, Clinical Data Manager: The Comprehensive Guide dives deep into the core of managing clinical data with precision and strategic insight. The book unfolds the intricacies of data integrity, patient privacy, regulatory compliance, and technological advancements, tailored for both novices and seasoned professionals. Its pages are filled with actionable strategies, expert tips, and real-world scenarios that bring to light the profound impact of effective data management on healthcare outcomes. Stepping beyond conventional resources, this guide emphasizes the transformative role of data management in facilitating groundbreaking research and improving patient care. Through a unique blend of theoretical foundations and practical applications, it arms you with the knowledge and skills to navigate the complexities of clinical trials and big data analytics. It also addresses the current absence of visuals by engaging the reader's imagination and encouraging a deeper understanding through thought-provoking questions and exercises. As a beacon for aspiring and established data managers alike, this book promises not just to educate but to inspire a new wave of innovation in the field of healthcare research. |
| excel for clinical data management: 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. |
| excel for clinical data management: Thesis Writing for Master's and Ph.D. Program Subhash Chandra Parija, Vikram Kate, 2018-11-03 This book on Thesis Writing for Master’s and Ph.D. program focuses on the difficulties students encounter with regard to choosing a guide; selecting an appropriate research title considering the available resources; conducting research; and ways to overcome the hardships they face while researching, writing and preparing their dissertation for submission. Thesis writing is an essential skill that medical and other postgraduates are expected to learn during their academic career as a mandatory partial requirement in order to receive the Master’s degree. However, at the majority of medical schools, writing a thesis is largely based on self-learning, which adds to the burden on students due to the tremendous amount of time spent learning the writing skills in addition to their exhausting clinical and academic work. Due to the difficulties faced during the early grooming years and lack of adequate guidance, acquiring writing skills continues to be a daunting task for most students. This book addresses these difficulties and deficiencies and provides comprehensive guidance, from selecting the research title to publishing in a scientific journal. |
| excel for clinical data management: Biomedical Research and Integrated Biobanking: An Innovative Paradigm for Heterogeneous Data Management Massimiliano Izzo, 2016-03-17 This doctoral thesis reports on an innovative data repository offering adaptive metadata management to maximise information sharing and comprehension in multidisciplinary and geographically distributed collaborations. It approaches metadata as a fluid, loosely structured and dynamical process rather than a fixed product, and describes the development of a novel data management platform based on a schemaless JSON data model, which represents the first fully JSON-based metadata repository designed for the biomedical sciences. Results obtained in various application scenarios (e.g. integrated biobanking, functional genomics and computational neuroscience) and corresponding performance tests are reported on in detail. Last but not least, the book offers a systematic overview of data platforms commonly used in the biomedical sciences, together with a fresh perspective on the role of and tools for data sharing and heterogeneous data integration in contemporary biomedical research. |
| excel for clinical data management: Advance Concepts of Clinical Research Guidance for Industry Dr. Gayatri Ganu, Book is useful for the industrial experts who engage in clinical trials, also for students and research scholar who come in contact with clinical terms. |
| excel for clinical data management: Introduction to Computers for Healthcare Professionals Irene Joos, Ramona Nelson, Marjorie J. Smith, 2013-08-21 An ideal resource for introductory computer courses for healthcare professionals, the text provides a comprehensive approach to digital literacy with the incorporation of social media tools. The Sixth Edition features an extensive revision of each chapter to reflect Microsoft Office® 2010 and Windows® 7 updates, as well as computer-assisted communication--Back cover. |
| excel for clinical data management: Clinical Research Robert D. Toto, Michael J. McPhaul, 2012-03-28 This book will serve as a road map for students and junior researchers seeking to successfully design, implement, and publish clinical research. It covers the basic elements of research proposals and implementation including regulatory approvals, continuing regulatory oversight, investigational new drug and device applications, monitoring patient safety, recruitment, clinical assessments, laboratory assessments, provision of treatment, and on-going quality control. The authors provide instruction on how to integrate research resources to successfully conduct a clinical research project, and offer guidelines on collection, quality control, and analysis of data. A companion website will include the fully searchable text and links to Journal of Investigative Medicine's Research Tools and Issues feature. |
| excel for clinical data management: Practical Guide to Clinical Data Management Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, |
| excel for clinical data management: Practical Approaches to Applied Research and Program Evaluation for Helping Professionals Casey A. Barrio Minton, A. Stephen Lenz, 2019-05-01 Practical Approaches to Applied Research and Program Evaluation for Helping Professionals is a comprehensive textbook that presents master’s-level counseling students with the skills and knowledge they need to successfully evaluate the effectiveness of mental health services and programs. Each chapter, aligned with 2016 Council for Accreditation of Counseling and Related Educational Programs (CACREP) standards, guides counseling students through study design and evaluation fundamentals that will help them understand existing research and develop studies to best assess their own applied research questions. Readers will learn the basics of research concepts as applied to evaluative tasks, the art of matching evaluative methods to questions, specific considerations for practice-based evaluative tasks, and practical statistical options matched to practice-based tasks. Readers can also turn to the book’s companion website to access worksheets for practitioner and student planning exercises, spreadsheets with formulas for basic data analysis, a sample database, PowerPoint outlines , and discussion questions and activities aligned to each chapter. |
| excel for clinical data management: German Medical Data Sciences: Visions and Bridges R. Röhrig, A. Timmer, H. Binder, 2017-09-26 We live in an age characterized by computerized information, but ubiquitous information technology has profoundly changed our healthcare systems and, if not adequately trained to deal with it, healthcare professionals can all too easily be overwhelmed by the complexity and magnitude of the data. This demands new skills from physicians as well as novel ways to provide medical knowledge. Selecting and assessing relevant information presents a challenge which can only be met by bridging the various disciplines in healthcare and the data sciences. This book presents the proceedings of the 62nd annual meeting of the German Association of Medical Informatics, Biometry and Epidemiology (German Medical Data Sciences – GMDS 2017): Visions and Bridges, held in Oldenburg, Germany, in September 2017. The 242 submissions to the conference included 77 full papers, of which 42 were accepted for publication here after rigorous review. These are divided into 7 sections: teaching and training; epidemiological surveillance, screening and registration; research methods; IT infrastructure for biomedical research/data integration centers; healthcare information systems; interoperability – standards, terminologies, classification; and biomedical informatics, innovative algorithms and signal processing. The book provides a vision for healthcare in the information age, and will be of interest to all those concerned with improving clinical decision making and the effectiveness and efficiency of health systems using data methods and technology. |
| excel for clinical data management: Drug Discovery and Clinical Research SK Gupta, 2011-06 The Drug Discovery and Clinical Research bandwagon has been joined by scientists and researchers from all fields including basic sciences, medical sciences, biophysicists, biotechnologists, statisticians, regulatory officials and many more. The joint effort and contribution from all is translating into the fast development of this multi-faceted field. At the same time, it has become challenging for all stakeholders to keep abreast with the explosion in information. The race for the finish-line leaves very little time for the researchers to update themselves and keep tabs on the latest developments in the industry. To meet these challenges, this book entitled Drug Discovery and Clinical Research has been compiled. All chapters have been written by stalwarts of the field who have their finger on the pulse of the industry. The aim of the book is to provide succinctly within one cover, an update on all aspects of this wide area. Although each of the chapter dealt here starting from drug discovery and development, clinical development, bioethics, medical devices, pharmacovigilance, data management, safety monitoring, patient recruitment, etc. are topics for full-fledged book in themselves, an effort has been made via this book to provide a bird’s eye view to readers and help them to keep abreast with the latest development despite constraints of time. It is hoped that the book will contribute to the growth of readers, which should translate into drug discovery and clinical research industry’s growth. |
| excel for clinical data management: Fundamental Concepts for New Clinical Trialists Scott Evans, Naitee Ting, 2015-11-04 Fundamental Concepts for New Clinical Trialists describes the core scientific concepts of designing, data monitoring, analyzing, and reporting clinical trials as well as the practical aspects of trials not typically discussed in statistical methodology textbooks. The first section of the book provides background information about clinical trials. I |
| excel for clinical data management: Validating Clinical Trial Data Reporting with SAS Carol I. Matthews, Brian C. Shilling, 2008 This indispensable guide focuses on validating programs written to support the clinical trial process from after the data collection stage to generating reports and submitting data and output to the Food and Drug Administration. |
| excel for clinical data management: Clinical Research in Asia U Sahoo, 2012-05-25 Asia is increasingly taking on a leading role in the fields of Good Clinical Practice (GCP) and ethics, two areas that are central to clinical research practices worldwide. Clinical research in Asia examines the evolution of these key sectors in the Asian countries where the greatest developments are taking place, offering valuable perspectives on a wide range of issues affecting clinical research. Following an introduction that provides an overview of the topic and its strengths and weaknesses, each chapter of the book is devoted to clinical research in a specific country, focusing on issues including the history and evolution of clinical research, clinical trials and regulatory aspects. The chapters also offer a perspective on future trends in clinical research in each country. The book concludes with a discussion of the importance of political, economic, socio-cultural, technological, legal and environmental factors (PESTLE analysis). - Analysis from a leading and highly respected professional in the sector - An overview of country-specific regulatory environments - Discussion of challenges and solutions for clinical research |
| excel for clinical data management: Building the Clinical Research Workforce: Challenges, Capacities and Competencies Carolynn Thomas Jones, Barbara E. Bierer, Stephen Sonstein, Hazel Ann Smith, Denise Snyder, 2024-08-05 This is an unprecedented time for clinical research. The number and complexity of clinical research studies have increased significantly in the last decade. Individual participation in clinical research broadened, with an increase in diverse populations, diseases, and geographic settings. The successful execution of these studies, however, has been compromised by an international shortage of clinical research professionals, coupled with an appreciation of the growing number of core competencies necessary for performance. Developed over a decade ago, the Joint Task Force for Clinical Trial Competency (JTF) Framework outlines the knowledge, skills and attitudes that are essential for the safe and effective conduct of a clinical study. This framework has been used to develop professional pathways, trainings, and certification programs and has been extended internationally through translation. |
| excel for clinical data management: Clinical Data Quality Checks for CDISC Compliance Using SAS Sunil Gupta, 2019-09-23 Clinical Data Quality Checks for CDISC Compliance using SAS is the first book focused on identifying and correcting data quality and CDISC compliance issues with real-world innovative SAS programming techniques such as Proc SQL, metadata and macro programming. Learn to master Proc SQL’s subqueries and summary functions for multi-tasking process. Drawing on his more than 25 years’ experience in the pharmaceutical industry, the author provides a unique approach that empowers SAS programmers to take control of data quality and CDISC compliance. This book helps you create a system of SDTM and ADaM checks that can be tracked for continuous improvement. How often have you encountered issues such as missing required variables, duplicate records, invalid derived variables and invalid sequence of two dates? With the SAS programming techniques introduced in this book, you can start to monitor these and more complex data and CDISC compliance issues. With increased standardization in SDTM and ADaM specifications and data values, codelist dictionaries can be created for better organization, planning and maintenance. This book includes a SAS program to create excel files containing unique values from all SDTM and ADaM variables as columns. In addition, another SAS program compares SDTM and ADaM codelist dictionaries with codelists from define.xml specifications. Having tools to automate this process greatly saves time from doing it manually. Features SDTMs and ADaMs Vitals SDTMs and ADaMs Data CDISC Specifications Compliance CDISC Data Compliance Protocol Compliance Codelist Dictionary Compliance |
| excel for clinical data management: German Medical Data Sciences 2023 — Science. Close to People. R. Röhrig, N. Grabe, M. Haag, 2023-10-19 The Covid-19 pandemic affected the daily lives of all of us on many levels. Epidemiology suddenly became a personal matter and general interest in many aspects of medical data science became much more widespread. And physical distance became the new normal. This book presents the full paper part of the proceedings of GMDS 2023, the 68th annual meeting of the German Association for Medical Informatics, Biometry and Epidemiology, held from 17 to 21 September 2023 in Heilbronn, Germany. The theme of the conference was, Science. Close to People, a particularly appropriate theme for the first of these annual conferences to be held face-to-face since 2019. A total of 227 scientific contributions were submitted to GMDS 2023, including 41 full papers for this volume in Studies in HTI. Of these, 30 papers are included here, following a rigorous two-stage review process, which represents an acceptance rate of 73%. The 30 papers in this book are grouped under 8 headings: FAIRification; research software engineering for research infrastructure & study data management; human factors; data quality; clinical decision support & artificial intelligence; evaluation of healthcare IT; biosignals; and interoperability. Providing a broad overview of current developments in the disciplines of medical informatics, biometry and epidemiology, the book will be of interest to all those working in these fields. |
| excel for clinical data management: Global Perspectives in Ocular Oncology Bhavna V. Chawla, Mary E. Aronow, 2023-01-03 Eye cancers vary in presentation depending upon geographic location and access to healthcare. Global Perspectives in Ocular Oncology offers an international platform for leading ocular oncologists and multidisciplinary specialists to highlight worldwide strengths and solutions to the challenges in treating eye cancer. The goal of the book is to provide a universal view on the management of adult and pediatric tumors affecting the eye and ocular adnexa. A range of topics pertinent to the global community have been included. Organized into seven distinct sections, this book covers international collaborations and initiatives, technology and innovations, and novel treatment strategies. In addition, it provides a glimpse into the future of the specialty. The emphasis on sharing perspectives as well as the global and multidisciplinary framework of the book are unique to the market. This work will appeal to a variety of audiences including ocular oncologists and ophthalmic subspecialists, oncologists and other specialists, optometrists, geneticists, allied medical professionals, and trainees entering these disciplines. |
| excel for clinical data management: MASTERING HEALTHCARE EXCELLENCE Jarvis T Gray, 2024-09-02 Mastering Healthcare Excellence offers a comprehensive guide for healthcare leaders aiming to achieve unparalleled results. Through practical insights and strategic frameworks, this book empowers executives to align their teams, priori- ties, and processes effectively. Discover profen strategies to navigate complex challenges, drive innovation, and lead with excellence in today's dynamic healthcare landscape. |
| excel for clinical data management: Managing Big Data Integration in the Public Sector Aggarwal, Anil, 2015-11-12 The era of rapidly progressing technology we live in generates vast amounts of data; however, the challenge exists in understanding how to aggressively monitor and make sense of this data. Without a better understanding of how to collect and manage such large data sets, it becomes increasingly difficult to successfully utilize them. Managing Big Data Integration in the Public Sector is a pivotal reference source for the latest scholarly research on the application of big data analytics in government contexts and identifies various strategies in which big data platforms can generate improvements within that sector. Highlighting issues surrounding data management, current models, and real-world applications, this book is ideally designed for professionals, government agencies, researchers, and non-profit organizations interested in the benefits of big data analytics applied in the public sphere. |
| excel for clinical data management: Statistical Methods for Clinical Trials Mark X. Norleans, 2000-11-08 Summarizes graphical analysis, analysis of variance, meta-analysis, and design of comparable treatment groups. Streamlines the analytical techniques for continuous, categorical, longitudinal, and survival data-focusing on generalized linear models, GEEs, and mixed linear models, -ahd highlihgts p-value, and more. |
| excel for clinical data management: Encyclopedia of Biopharmaceutical Statistics - Four Volume Set Shein-Chung Chow, 2018-09-03 Since the publication of the first edition in 2000, there has been an explosive growth of literature in biopharmaceutical research and development of new medicines. This encyclopedia (1) provides a comprehensive and unified presentation of designs and analyses used at different stages of the drug development process, (2) gives a well-balanced summary of current regulatory requirements, and (3) describes recently developed statistical methods in the pharmaceutical sciences. Features of the Fourth Edition: 1. 78 new and revised entries have been added for a total of 308 chapters and a fourth volume has been added to encompass the increased number of chapters. 2. Revised and updated entries reflect changes and recent developments in regulatory requirements for the drug review/approval process and statistical designs and methodologies. 3. Additional topics include multiple-stage adaptive trial design in clinical research, translational medicine, design and analysis of biosimilar drug development, big data analytics, and real world evidence for clinical research and development. 4. A table of contents organized by stages of biopharmaceutical development provides easy access to relevant topics. About the Editor: Shein-Chung Chow, Ph.D. is currently an Associate Director, Office of Biostatistics, U.S. Food and Drug Administration (FDA). Dr. Chow is an Adjunct Professor at Duke University School of Medicine, as well as Adjunct Professor at Duke-NUS, Singapore and North Carolina State University. Dr. Chow is the Editor-in-Chief of the Journal of Biopharmaceutical Statistics and the Chapman & Hall/CRC Biostatistics Book Series and the author of 28 books and over 300 methodology papers. He was elected Fellow of the American Statistical Association in 1995. |
| excel for clinical data management: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| excel for clinical data management: ICT for Health Science Research A. Shabo (Shvo), I. Madsen, H.-U. Prokosch, 2019-04-17 Information and Communications Technology (ICT) is used in healthcare and health science research in application domains such as clinical trials and the development of drug and medical devices, as well as in translational medicine, with the aim of improving prevention, diagnosis, and interventions in health and care. This book presents accepted papers from the 2019 European Federation of Medical Informatics conference (EFMI STC 2019), held in Hanover, Germany, from 7 – 10 April 2019. More than 90 submissions were received, from which, after review, the Scientific Program Committee (SPC) accepted 50 full papers to be included in this volume of proceedings. In addition, 16 poster presentations were accepted. This year, ICT for Health Science Research was selected as the focus topic, and the conference also honors Prof. Peter Leo Reichertz (1930 – 1987), one of the founding fathers of ICT healthcare and an originator of the term Medical Informatics. The conference focuses on recent research & development supporting information systems in biomedical, translational and clinical research, as well as semantic interoperability across such systems for the purpose of data sharing and the analytics of cross-system integrated data. Papers are divided into 12 categories covering topics including digitization; data privacy; interoperability; data-driven decision support; mobile data capture; and ICT for clinical trials. The book will be of interest to all healthcare researchers and practitioners whose work involves the use of ICT. |
| excel for clinical data management: Statistics & Data Analytics for Health Data Management Nadinia A. Davis, Betsy J. Shiland, 2015-12-04 Introducing Statistics & Data Analytics for Health Data Management by Nadinia Davis and Betsy Shiland, an engaging new text that emphasizes the easy-to-learn, practical use of statistics and manipulation of data in the health care setting. With its unique hands-on approach and friendly writing style, this vivid text uses real-world examples to show you how to identify the problem, find the right data, generate the statistics, and present the information to other users. Brief Case scenarios ask you to apply information to situations Health Information Management professionals encounter every day, and review questions are tied to learning objectives and Bloom's taxonomy to reinforce core content. From planning budgets to explaining accounting methodologies, Statistics & Data Analytics addresses the key HIM Associate Degree-Entry Level competencies required by CAHIIM and covered in the RHIT exam. - Meets key HIM Associate Degree-Entry Level competencies, as required by CAHIIM and covered on the RHIT registry exam, so you get the most accurate and timely content, plus in-depth knowledge of statistics as used on the job. - Friendly, engaging writing style offers a student-centered approach to the often daunting subject of statistics. - Four-color design with ample visuals makes this the only textbook of its kind to approach bland statistical concepts and unfamiliar health care settings with vivid illustrations and photos. - Math review chapter brings you up-to-speed on the math skills you need to complete the text. - Brief Case scenarios strengthen the text's hands-on, practical approach by taking the information presented and asking you to apply it to situations HIM professionals encounter every day. - Takeaway boxes highlight key points and important concepts. - Math Review boxes remind you of basic arithmetic, often while providing additional practice. - Stat Tip boxes explain trickier calculations, often with Excel formulas, and warn of pitfalls in tabulation. - Review questions are tied to learning objectives and Bloom's taxonomy to reinforce core content and let you check your understanding of all aspects of a topic. - Integrated exercises give you time to pause, reflect, and retain what you have learned. - Answers to integrated exercises, Brief Case scenarios, and review questions in the back of the book offer an opportunity for self-study. - Appendix of commonly used formulas provides easy reference to every formula used in the textbook. - A comprehensive glossary gives you one central location to look up the meaning of new terminology. - Instructor resources include TEACH lesson plans, PowerPoint slides, classroom handouts, and a 500-question Test Bank in ExamView that help prepare instructors for classroom lectures. |
| excel for clinical data management: Software Innovations in Clinical Drug Development and Safety Chakraborty, Partha, 2015-10-02 In light of the rising cost of healthcare and the overall challenges associated with delivering quality care to patients across regions, scientists and pharmacists are exploring new initiatives in drug discovery and design. One such initiative is the adoption of information technology and software applications to improve healthcare and pharmaceutical processes. Software Innovations in Clinical Drug Development and Safety is a comprehensive resource analyzing the integration of software engineering for the purpose of drug discovery, clinical trials, genomics, and drug safety testing. Taking a multi-faceted approach to the application of computational methods to pharmaceutical science, this publication is ideal for healthcare professionals, pharmacists, computer scientists, researchers, and students seeking the latest information on the architecture and design of software in clinical settings, the impact of clinical technologies on business models, and the safety and privacy of patients and patient data. This timely resource features a well-rounded discussion on topics pertaining to the integration of computational methods in pharmaceutical science and practice including, the impact of software integration on business models, patient safety concerns, software architecture and design, and data security. |
| excel for clinical data management: School of Nursing University of California, San Francisco. School of Nursing, 2000 |
| excel for clinical data management: Nursing Informatics for the Advanced Practice Nurse, Second Edition Susan McBride, PhD, RN-BC, CPHIMS, FAAN, Mari Tietze, PhD, RN, FHIMSS, FAAN, 2018-09-28 A “must have” text for all healthcare professionals practicing in the digital age of healthcare. Nursing Informatics for the Advanced Practice Nurse, Second Edition, delivers a practical array of tools and information to show how advanced practice nurses can maximize patient safety, quality of care, and cost savings through the use of technology. Since the first edition of this text, health information technology has only expanded. With increased capability and complexity, the current technology landscape presents new challenges and opportunities for interprofessional teams. Nurses, who are already trained to use the analytic process to assess, analyze, and intervene, are in a unique position to use this same process to lead teams in addressing healthcare delivery challenges with data. The only informatics text written specifically for advanced practice nurses, Nursing Informatics for the Advanced Practice Nurse, Second Edition, takes an expansive, open, and innovative approach to thinking about technology. Every chapter is highly practical, filled with case studies and exercises that demonstrate how the content presented relates to the contemporary healthcare environment. Where applicable, concepts are aligned with the six domains within the Quality and Safety Education in Nursing (QSEN) approach and are tied to national goals and initiatives. Featuring chapters written by physicians, epidemiologists, engineers, dieticians, and health services researchers, the format of this text reflects its core principle that it takes a team to fully realize the benefit of technology for patients and healthcare consumers. What’s New Several chapters present new material to support teams’ optimization of electronic health records Updated national standards and initiatives Increased focus and new information on usability, interoperability and workflow redesign throughout, based on latest evidence Explores challenges and solutions of electronic clinical quality measures (eCQMs), a major initiative in healthcare informatics; Medicare and Medicaid Services use eCQMs to judge quality of care, and how dynamics change rapidly in today’s environment Key Features Presents national standards and healthcare initiatives Provides in-depth case studies for better understanding of informatics in practice Addresses the DNP Essentials, including II: Organization and system leadership for quality improvement and systems thinking, IV: Core Competency for Informatics, and Interprofessional Collaboration for Improving Patient and Population health outcomes Includes end-of-chapter exercises and questions for students Instructor’s Guide and PowerPoint slides for instructors Aligned with QSEN graduate-level competencies |
| excel for clinical data management: Plunkett's Health Care Industry Almanac 1999-00 Jack W. Plunkett, 1999 Gives complete access to data on national health care statistics, Medicare and Medicaid, research and technology, HMOs and hospital utilization, careers and job opportunities, and forecasts and trends. Also contains one page profiles on each of the leading Health Care 500 companies (pharmaceuticals, biotechnology, hospitals, insurance/HMOs, care providers, diagnostics, and instruments) that provide ranks and ratings, types of business, contact names, E-mail, phone, fax and website, salaries/benefits, competitive advantage, and growth plans/special features. Includes a CD-ROM version. |
| excel for clinical data management: Clinical Research Computing Prakash Nadkarni, 2016-04-29 Clinical Research Computing: A Practitioner's Handbook deals with the nuts-and-bolts of providing informatics and computing support for clinical research. The subjects that the practitioner must be aware of are not only technological and scientific, but also organizational and managerial. Therefore, the author offers case studies based on real life experiences in order to prepare the readers for the challenges they may face during their experiences either supporting clinical research or supporting electronic record systems. Clinical research computing is the application of computational methods to the broad field of clinical research. With the advent of modern digital computing, and the powerful data collection, storage, and analysis that is possible with it, it becomes more relevant to understand the technical details in order to fully seize its opportunities. - Offers case studies, based on real-life examples where possible, to engage the readers with more complex examples - Provides studies backed by technical details, e.g., schema diagrams, code snippets or algorithms illustrating particular techniques, to give the readers confidence to employ the techniques described in their own settings - Offers didactic content organization and an increasing complexity through the chapters |
What does the "@" symbol mean in Excel formula (outsid…
Oct 24, 2021 · Excel has recently introduced a huge feature called Dynamic arrays. And along with that, Excel also started to make a " …
excel - How to show current user name in a cell? - Stack O…
if you don't want to create a UDF in VBA or you can't, this could be an alternative. =Cell("Filename",A1) this will give you the full file name, and …
How to represent a DateTime in Excel - Stack Overflow
The underlying data type of a datetime in Excel is a 64-bit floating point number where the length of a day equals 1 and 1st Jan 1900 00:00 …
excel - Check whether a cell contains a substring - Stack O…
Sep 4, 2013 · Is there an in-built function to check if a cell contains a given character/substring? It would mean you can apply textual …
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The $ tells excel not to adjust that address while pasting the formula into new cells. Since you are dragging across rows, you really only need to …
What does the "@" symbol mean in Excel formula (outsid…
Oct 24, 2021 · Excel has recently introduced a huge feature called Dynamic arrays. And along with that, Excel also started to make a " …
excel - How to show current user name in a cell? - Stack O…
if you don't want to create a UDF in VBA or you can't, this could be an alternative. =Cell("Filename",A1) this will give you the full file name, and …
How to represent a DateTime in Excel - Stack Overflow
The underlying data type of a datetime in Excel is a 64-bit floating point number where the length of a day equals 1 and 1st Jan 1900 00:00 …
excel - Check whether a cell contains a substring - Stack O…
Sep 4, 2013 · Is there an in-built function to check if a cell contains a given character/substring? It would mean you can apply textual …
How to keep one variable constant with other one chan…
The $ tells excel not to adjust that address while pasting the formula into new cells. Since you are dragging across rows, you really only need to …
