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Building PCOR Value and Integrity with Data Quality and Transparency Standards

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Not Important

Somewhat Unimportant

Somewhat Important

Very Important

Unknown or Unsure

Support data quality (DQ) analyses of specific data elements in a data set.

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Support DQ analyses of multiple data elements in a single-site data set.

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Support DQ analyses of multiple data elements across multi-site data sets.

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Support assessment of metadata quality (e.g., data element definitions, source systems, business rules, documentation of changes over time).

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Incorporate data life-cycle metadata such as data collection context, transformations, computations or combinations, and imputations.

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Provide data quality assessment methods that can be automated and are repeatable with minimal manual assessments.

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Provide data quality assessment methods that can be parameterized to enable comparable reports.

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Allow for user-defined "acceptance criteria" (e.g. "How much missing data is allowable").

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Support a broad range of definitions for "gold" standards, including relative benchmarks and comparisons.

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Support ETL (extraction transfer load) validations.

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Provide guidance on which DQ assessment methods are most application for intended use (for instance, based on study design).

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DQ assessment methods can be linked to the intended use of the data, e.g., subject identification, outcome measures, or other covariates.

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Is consistent with epidemiologic standards for data analysis. Supports DQ methods that can be applied to longitudinal data.

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Can link DQ processes & findings with analytic results.

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Provide guidance on presentation of DQ findings that are appropriate to audiences and intended uses.

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Next, we would like to know what you think is more important: to have the DQ methods framework base its guidance on ensuring validity of data (guide how well the data compares to other sources of information) or reliability of the data (guide how well the data compares to itself)?

ensure validity of data ensure reliability of data

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Strongly Disagree

Somewhat Disagree

Somewhat Agree

Strongly Agree

Spending my professional time participating in the creation of a DQ methods model is worthwhile.

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Spending my professional time participating in the creation of data reports that are understandable to data consumers (patients and policy makers) is worthwhile.

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I am more concerned with knowing that data results are reliable and valid than with data analysis methods.

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Next, we would like to know a bit about the professionals who complete this survey.

In your professional capacity, are you directly engaged in 1 or more aspects of generating data for research purposes OR are you directly engaged in taking data results and applying them outside of the research setting?

Generate or analyze data for research purposes

Apply data results for policy making, health systems management, patient concerns, or related purposes

Both

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Finally, what have we missed? What do you think is important to include in the DQ methods framework?

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