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Currently Skimming:

2 Improving Data Quality and Integrity
Pages 21-42

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From page 21...
... INTRODUCTION TO QUALITY ASSURANCE PROGRAMS A process or product that conforms to requirements or to a quality standard is recognized as a quality process or product (Crosby, 1979)
From page 22...
... REASONS FOR IMPLEMENTING QUALITY ASSURANCE PROGRAMS Implementation of a laboratory quality assurance program is a recommended best practice because a standard for work is established to assure that the best quality data are generated. Determining whether the work conforms to the standard is part of quality assurance.
From page 23...
... . Quality assurance programs facilitate effective research and data management in laboratory environments because they address organizational, managerial, and technical activities; support data completeness and accuracy; promote process standardization when possible; and assure work transparency and traceability.
From page 24...
... . EVIDENCE THAT QUALITY ASSURANCE PROGRAMS SUPPORT DATA QUALITY Evidence that quality assurance programs are appropriate and effective strategies for maintaining and demonstrating data quality comes from meta-research and reports by organizations and scientists who have integrated quality assurance best practices or programs into their laboratories.
From page 25...
... APPROACHES FOR DESIGNING LABORATORY DATA QUALITY PROGRAMS The design and implementation of any process to assure research data quality requires substantial input from scientists to ensure that the process is science centered (incorporates general and field-specific scientific best practices) , realistic for the research environment, and fit for purpose (Leonelli, 2017)
From page 26...
... Institution-defined QMS requirements that are implemented throughout the institu tion, and 4. Externally-defined QMS requirements that are implemented at the institution or indi vidual laboratory level to demonstrate compliance with an external quality standard.
From page 27...
... of tools, including written and unwritten policies and procedures, personnel training and mentoring, proficiency testing, the use of electronic laboratory notebooks, automated quality control, and the use of software programs to maintain sample and data records. Examples of this approach are given in Box 2.1.
From page 28...
... The institutional expectations are directed toward generalized laboratory best practices rather than compliance with a centrally controlled quality standard. For example, the guidelines for nonroutine work (i.e., research)
From page 29...
... They will likely need extra time and support to change their routine approach. Approach 3: Institution-Defined QMS Requirements An institution-defined QMS is a centrally controlled system designed to assure that the quality objectives of the institution are met.
From page 30...
... The goals of the Ferring Research Institute, the research arm of Ferring Pharmaceuticals, were to "imple ment quality assurance mechanisms that assure the quality and integrity of critical (impactful) data while avoiding the potential threat to scientific ingenuity from excessive regulations" and to "eliminate preventable errors, oversights, or misinterpretations in data analysis and final reporting." The hope was that scientists would adopt quality practices across all research activities as they became more experienced applying data quality best practices.
From page 31...
... Finally, it reduced the number and severity of problems found in audits. Example 2 Completing the data life cycle: Using information management in macrosystems ecology research (from Rüegg et al., 2014)
From page 32...
... Other examples are given in Box 2.3. Box 2.3 Examples of Institution-Defined QMS Requirements Example 1 Development of a research QMS for the French National Institute for Agronomic Research (from Molinéro-Demilly et al., 2018)
From page 33...
... • The use of a centralized QMS should improve quality, reliability, work transparency, and consistency across the institution. • An internally defined quality standard can be customized to address the specific needs of an organization.
From page 34...
... Approach 4: Externally-Defined QMS Requirements With this approach, an institution adopts one or more externally-defined quality standards and develops its QMS to ensure that the requirements of those standards will be met. A laboratory or laboratory system may choose to comply with an external standard because it seeks accreditation by an external agency to carry out a particular line of work, enhance c ­ redibility, or meet the specific requirements of clients, collaborators, or regulatory agencies.
From page 35...
... It is used by the National Environmental Laboratory Accreditation Program to "foster the generation of data of known and documented quality" through the accreditation of environmental laboratories according to consensus standards "representing the best professional practices in the industry."4 It is possible for laboratories to comply with multiple standards within the same environment. For example, some TNI-accredited laboratories may also have to comply with the additional quality standards established by their own organization.
From page 36...
... The primary disadvantages to the institution are the following: • Demonstrating compliance with an external quality standard requires substantial resources to address the complex array of management, performance, and monitoring requirements. • An organization may need to maintain more than one quality standard if the scope of the external QMS accreditation does not cover the entire quality system.
From page 37...
... Approaches 1–4 discussed above reflect the increasing complexity, cost, requirements, and oversight needed to assure confidence in the data. Approaches 2–4 are quality assurance programs and recommended best practices, with centralized expectations that a sufficient management system will be in place for all laboratories, and that the effectiveness of the program can be demonstrated throughout all laboratory processes.
From page 38...
... . MAINTAINING DATA QUALITY PROGRAMS Implementing quality assurance programs as an organizational strategy to maintain and demonstrate data quality requires a strong and sustained commitment from leadership, management, and laboratory scientists.
From page 39...
... For example, quality assurance activities can be supported through the use of tools that make it easier and faster to meet quality requirements. Examples include the use of electronic notebooks, centralized data collaboration and sharing sites, quality management software (record repository and working environment for quality assurance activities)
From page 40...
... procedure for non-routine research records 3.  Method validation records 4.  Quality control records 5.  Research monitoring records (other quality checkpoints throughout the research life cycle) Standard Operating Procedures: Ensure that procedures are performed consistently, Yes No changed as needed, and maintained as historical records 1.  Routine work instructions for laboratory and data management, established technical procedures, and equipment use are documented and controlled 2.  Document control procedures are in place (creation, revision, and archive)
From page 41...
... Approaches to laboratory management vary according to the scope and objectives of the laboratory and organization overseeing it. Best practices relevant to the USGS include • Institution-defined best practices that are implemented at the individual laboratory level, • Institution-defined QMS requirements that are implemented throughout the institution, and • Externally-defined QMS requirements that are implemented to demonstrate compliance with an external quality standard.


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