JOURNAL ARTICLE
RESEARCH SUPPORT, N.I.H., EXTRAMURAL
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Clinical Trial Assessment of Infrastructure Matrix Tool to Improve the Quality of Research Conduct in the Community.

PURPOSE: Several publications have described minimum standards and exemplary attributes for clinical trial sites to improve research quality. The National Cancer Institute (NCI) Community Cancer Centers Program (NCCCP) developed the clinical trial Best Practice Matrix tool to facilitate research program improvements through annual self-assessments and benchmarking. The tool identified nine attributes, each with three progressive levels, to score clinical trial infrastructural elements from less to more exemplary. The NCCCP sites correlated tool use with research program improvements, and the NCI pursued a formative evaluation to refine the interpretability and measurability of the tool.

METHODS: From 2011 to 2013, 21 NCCCP sites self-assessed their programs with the tool annually. During 2013 to 2014, NCI collaborators conducted a five-step formative evaluation of the matrix tool.

RESULTS: Sites reported significant increases in level-three scores across the original nine attributes combined (P<.001). Two specific attributes exhibited significant change: clinical trial portfolio diversity and management (P=.0228) and clinical trial communication (P=.0281). The formative evaluation led to revisions, including renaming the Best Practice Matrix as the Clinical Trial Assessment of Infrastructure Matrix (CT AIM), expanding infrastructural attributes from nine to 11, clarifying metrics, and developing a new scoring tool.

CONCLUSION: Broad community input, cognitive interviews, and pilot testing improved the usability and functionality of the tool. Research programs are encouraged to use the CT AIM to assess and improve site infrastructure. Experience within the NCCCP suggests that the CT AIM is useful for improving quality, benchmarking research performance, reporting progress, and communicating program needs with institutional leaders. The tool model may also be useful in disciplines beyond oncology.

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