ArticleJournal of the American Medical Informatics Association : JAMIA2018
A conceptual framework for evaluating data suitability for observational studies.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Data-driven FMEA approach for hazard identification and risk evaluation in digital health.Scientific reports · 2025Article
- Beyond the numbers: the importance of contextual data when reusing blood pressure data from electronic health records.Frontiers in digital health · 2025Article
- A Standard Framework for Evaluating Large Health Care Data and Related Resources.MMWR supplements · 2024Article
- Good practices for clinical data warehouse implementation: A case study in France.PLOS digital health · 2023Review
- Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.Applied ontology · 2022Article
- Assessing the Quality of Electronic Data for 'Fit-for-Purpose' by Utilizing Data Profiling Techniques Prior to Conducting a Survival Analysis for Adults with Acute Lymphoblastic Leukemia.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Clinical data quality: a data life cycle perspective.Biostatistics & epidemiology · 2020Article
- Between Many Rocks and Hard Places.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2019Review
- TASKA: A modular task management system to support health research studies.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo contribute a conceptual framework for evaluating data suitability to satisfy the research needs of observational studies. MATERIALS AND
methodsSuitability 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.
resultsData 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.