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5 Recommendations and Conclusions
Pages 61-66

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From page 61...
... Before QMS development began in 2016, most USGS laboratory principal investigators defined their own quality assurance procedures and best practices (step 1 in Figure 5.1) , similar to the practices of academic laboratories (see Chapter 3)
From page 62...
... This innovative approach would also provide a leadership opportunity for the USGS, since relatively few large government organizations or academic institutions have attempted such broad-scale implementation of a quality assurance program in basic research settings.
From page 63...
... For example, some laboratories may choose to have a single Standard Operating Procedure (SOP) that addresses all the elements of laboratory management and multiple SOPs that describe technical activities.
From page 64...
... However, current USGS resource commitments, quality assurance staffing, and training are insufficient to implement a centrally controlled QMS for all USGS laboratories, especially on a fast timeframe. The USGS plans to cover the shortfall by splitting costs with its laboratories: the USGS will provide central training and quality assurance experts, and the laboratories will devote an estimated 20 percent of their resources for about 2 years to implement the QMS and about 10 percent annually thereafter to maintain the system.
From page 65...
... The USGS will need to take the time to gain substantive input and feedback from laboratory principal investigators, laboratory managers, quality assurance experts, and other organizations using these approaches to develop the systems, as well as to use lessons learned to make course corrections. Slowing implementation of the QMS would also make resources go further.
From page 66...
... Because institution-defined best practices are less expensive to implement than a QMS, more resources could be directed to laboratories that carry out substantial production activities requiring a QMS. For both approaches, ample time is needed to • Communicate with staff, including explaining the quality goals of the organization and gaining staff input and feedback on system design and implementation; • Provide staff training, including meetings with quality assurance experts; • Establish mechanisms to recognize, support, and reward the substantial time and resources invested by laboratory scientists and quality assurance experts to meet USGS data quality goals; • Develop QMS champions who would help lead the necessary culture change; and • Learn from implementation experiences and continually improve the system.


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