Data quality assurance. Module 1. Framework and metrics

Data quality assurance. Module 1. Framework and metrics PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240047352
Category : Medical
Languages : en
Pages : 40

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Book Description

Data quality assurance. Module 1. Framework and metrics

Data quality assurance. Module 1. Framework and metrics PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240047352
Category : Medical
Languages : en
Pages : 40

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Book Description


Data quality assurance. Module 3. Site assessment of data quality

Data quality assurance. Module 3. Site assessment of data quality PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240049118
Category : Medical
Languages : en
Pages : 92

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Book Description
This publication is one of the three module toolkit and provide technical guidance and tools to support the work on strengthening data quality in countries. This is part of the Division of Data, Analytics and Delivery for Impact’s scope of work providing normative guidance for health information system strengthening.

Data quality assurance. Module 2. Discrete desk review of data quality

Data quality assurance. Module 2. Discrete desk review of data quality PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240047379
Category : Medical
Languages : en
Pages : 56

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Book Description
This publication is one of the three module toolkit and provide technical guidance and tools to support the work on strengthening data quality in countries. This is part of the Division of Data and Delivery for Impact's scope of work providing normative guidance for health information system strengthening.

Consolidated guidance on tuberculosis data generation and use. Module 1. Tuberculosis surveillance

Consolidated guidance on tuberculosis data generation and use. Module 1. Tuberculosis surveillance PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240075291
Category : Medical
Languages : en
Pages : 94

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Book Description
Since 1995, WHO has ensured a consistent approach to national, regional and global TB surveillance by providing standardized definitions, forms and registers for the recording and reporting of individual-level and aggregated data about people diagnosed with and treated for TB, which are used worldwide. This standardization has facilitated the regular reporting of TB data to WHO from 215 countries and areas in annual rounds of global TB data collection, with findings published in an annual WHO global TB report since 1997 and data made publicly available via the online WHO global TB database. The goal of this 2024 edition of WHO guidance on TB surveillance (following the last major update published in 2013) is to ensure the continued worldwide standardization of TB surveillance, in the context of the WHO End TB Strategy, the latest WHO guidelines on TB screening, prevention, diagnosis and treatment, and commitments made at the 2023 UN high-level meeting on TB, while also promoting the establishment or strengthening of digital, case-based TB surveillance that is integrated within the overall public health architecture. This 2024 edition provides a comprehensive and consolidated package, bringing together both updated guidance as well as (within web annexes) closely related WHO products, tools and documentation related to TB surveillance. The web annexes (and associated links to them) are listed below. The package was informed by (and includes a summary of) lessons learned about TB surveillance during more than 100 national TB epidemiological reviews conducted since 2013.

Analysis and use of health facility data: guidance for maternal, newborn, child and adolescent health programme managers

Analysis and use of health facility data: guidance for maternal, newborn, child and adolescent health programme managers PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240080333
Category : Medical
Languages : en
Pages : 64

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Book Description
This guidance describes a catalogue of indicators for maternal, newborn, child, and adolescent health (MNCAH) that can be monitored through health management information system data. It is a module of the WHO Toolkit for Routine Health Information Systems (RHIS) Data and links to relevant indicators from other programmatic modules of the WHO toolkit. The document provides guidance on possible analysis and visualization of the indicators, including considerations for interpreting and using the data for decisionmaking. An annex on data quality considerations for MNCAH managers provides suggestions for reviewing and interpreting routine health facility data through a quality lens. Accompanying this guidance are a series of presentations and exercises, including a facilitator guide, that can be used in workshops to strengthen capacity of analysis, interpretation, and use of data by MNCAH managers. The target audience for the guidance and accompanying materials are ministry of health staff working on MNCAH programmes and monitoring and evaluation activities at national and subnational levels; health workers; and partner organizations involved in supporting MNCAH programmes and monitoring. This text will be submitted to Language Services for translation whenever a publication is requested for translation and also provided to WHO Press at the same time as the translation.

Handbook of Data Quality

Handbook of Data Quality PDF Author: Shazia Sadiq
Publisher: Springer Science & Business Media
ISBN: 3642362575
Category : Computers
Languages : en
Pages : 440

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Book Description
The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.

Guidance on the analysis and use of routine health information systems. Sensory functions: eye and ear care module

Guidance on the analysis and use of routine health information systems. Sensory functions: eye and ear care module PDF Author: World Health Organization
Publisher: World Health Organization
ISBN: 9240075100
Category : Medical
Languages : en
Pages : 60

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Book Description


Measurements and Data Quality Assurance

Measurements and Data Quality Assurance PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 184

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Book Description


Data Quality

Data Quality PDF Author: Yng-Yuh Richard Wang
Publisher: Springer Science & Business Media
ISBN: 0792372158
Category : Business & Economics
Languages : en
Pages : 175

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Book Description
Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

SAS Data Analytic Development

SAS Data Analytic Development PDF Author: Troy Martin Hughes
Publisher: John Wiley & Sons
ISBN: 1119255910
Category : Computers
Languages : en
Pages : 446

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Book Description
Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer’s compendium for writing better-performing software and the manager’s guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements—the what and how—to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives—thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS® software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements.