Journal: Clinical and Translational Science

Article Title: With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

doi: 10.1111/cts.70093

Figure Lengend Snippet: Table of contents outlining the structure and headings of this tutorial.

Article Snippet: Two aggregated, de‐identified EHR data platforms that are broadly representative (over 100 million patients from the United States (US)) are becoming more widely available and used: TriNetX ( www.trinetx.com ) and Cosmos ( www.cosmos.epic.com ), which now support thousands of publications.

Techniques:

Journal: Clinical and Translational Science

Article Title: With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

doi: 10.1111/cts.70093

Figure Lengend Snippet: Process diagram depicting data generation in secondary EHR research and opportunities for bias. This schematic visually represents the flow of data from the general population to the EHR and finally to the study population created using the EHR data. This a broad conceptual visualization for secondary use of EHR data for research and some of the biases that are embedded in this pathway.

Article Snippet: Two aggregated, de‐identified EHR data platforms that are broadly representative (over 100 million patients from the United States (US)) are becoming more widely available and used: TriNetX ( www.trinetx.com ) and Cosmos ( www.cosmos.epic.com ), which now support thousands of publications.

Techniques:

Journal: Clinical and Translational Science

Article Title: With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

doi: 10.1111/cts.70093

Figure Lengend Snippet: Table of contents outlining the structure and headings of this tutorial.

Article Snippet: For example, while a large EHR data platform like TriNetX has data from many patients with diverse contexts, the absolute measures of frequency (i.e., incidence, prevalence, risk) should not be construed to be population‐based.

Techniques:

Journal: Clinical and Translational Science

Article Title: With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

doi: 10.1111/cts.70093

Figure Lengend Snippet: Process diagram depicting data generation in secondary EHR research and opportunities for bias. This schematic visually represents the flow of data from the general population to the EHR and finally to the study population created using the EHR data. This a broad conceptual visualization for secondary use of EHR data for research and some of the biases that are embedded in this pathway.

Article Snippet: For example, while a large EHR data platform like TriNetX has data from many patients with diverse contexts, the absolute measures of frequency (i.e., incidence, prevalence, risk) should not be construed to be population‐based.

Techniques:

Journal: Clinical and Translational Science

Article Title: With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

doi: 10.1111/cts.70093

Figure Lengend Snippet: Application of a graphical framework for study design visualization: Sample diagram was constructed from the design of a previous TriNetX study evaluating risk for specific encounter diagnoses or medication prescriptions for treatment of mood or anxiety disorders after EHR evidence of COVID‐19 infection. ⁷²

Article Snippet: For example, while a large EHR data platform like TriNetX has data from many patients with diverse contexts, the absolute measures of frequency (i.e., incidence, prevalence, risk) should not be construed to be population‐based.

Techniques: Construct, Infection