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. 2018 Mar 1;25(3):248-258.
doi: 10.1093/jamia/ocx095.

A conceptual framework for evaluating data suitability for observational studies

Ning Shang  1 , Chunhua Weng  1 , George Hripcsak  1
Affiliations

A conceptual framework for evaluating data suitability for observational studies

Ning Shang et al. J Am Med Inform Assoc. .

Abstract

Objective: To contribute a conceptual framework for evaluating data suitability to satisfy the research needs of observational studies.

Materials and methods: Suitability considerations were derived from a systematic literature review on researchers' common data needs in observational studies and a scoping review on frequent clinical database design considerations, and were harmonized to construct a suitability conceptual framework using a bottom-up approach. The relationships among the suitability categories are explored from the perspective of 4 facets of data: intrinsic, contextual, representational, and accessible. A web-based national survey of domain experts was conducted to validate the framework.

Results: Data suitability for observational studies hinges on the following key categories: Explicitness of Policy and Data Governance, Relevance, Availability of Descriptive Metadata and Provenance Documentation, Usability, and Quality. We describe 16 measures and 33 sub-measures. The survey uncovered the relevance of all categories, with a 5-point Likert importance score of 3.9 ± 1.0 for Explicitness of Policy and Data Governance, 4.1 ± 1.0 for Relevance, 3.9 ± 0.9 for Availability of Descriptive Metadata and Provenance Documentation, 4.2 ± 1.0 for Usability, and 4.0 ± 0.9 for Quality.

Conclusions: The suitability framework evaluates a clinical data source's fitness for research use. Its construction reflects both researchers' points of view and data custodians' design features. The feedback from domain experts rated Usability, Relevance, and Quality categories as the most important considerations.

Keywords: data suitability; observational studies; survey.

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Figures

Figure 1.
Figure 1.
The workflow for developing the suitability conceptual framework. In constructing the framework, we conducted a literature review, and its findings were summarized and discussed within our team. A web-based survey from domain experts was used to validate the framework.
Figure 2.
Figure 2.
Paper selection process in literature review.
Figure 3.
Figure 3.
The suitability conceptual framework (categories and measures) for evaluating the suitability of electronic clinical data for observational studies.
Figure 4.
Figure 4.
Respondents’ experience of data use.
Figure 5.
Figure 5.
Multiple roles of data users/providers/custodians among survey respondents.
Figure 6.
Figure 6.
Web-based survey results for evaluating the importance of the suitability framework sub-measures.
See this image and copyright information in PMC

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