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Research Data Explorer: Lessons Learned in Design and Development of Context-based Cohort Definition and Selection.

Wilcox A, Vawdrey D, Weng C, Velez M, Bakken S - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: The primary innovation of RedX was the electronic health record view of patient data, to provide better contextual understanding for non-technical users in building complex data queries.The design of RedX around this need identified multiple functions that would use individual patient views to better understand population-based data, and vice-versa.During development, the more necessary and valuable components of RedX were refined, leading to a functional self-service query and cohort identification tool.

View Article: PubMed Central - PubMed

Affiliation: Intermountain Healthcare, Salt Lake City, UT.

ABSTRACT
Research Data eXplorer (RedX) was designed to support self-service research data queries and cohort identification from clinical research databases. The primary innovation of RedX was the electronic health record view of patient data, to provide better contextual understanding for non-technical users in building complex data queries. The design of RedX around this need identified multiple functions that would use individual patient views to better understand population-based data, and vice-versa. During development, the more necessary and valuable components of RedX were refined, leading to a functional self-service query and cohort identification tool. However, with the improved capabilities and extensibility of other applications for data querying and navigation, our long-term implementation and dissemination plans have moved towards consolidation and alignment of RedX functions as enhancements in these other initiatives.

No MeSH data available.


RedX data distribution view for Troponin test. Data elements could be either continuous, discrete or both. The list of common discrete elements shows how a user would need to include both discrete and continuous data for some defined queries.
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f4-2092396: RedX data distribution view for Troponin test. Data elements could be either continuous, discrete or both. The list of common discrete elements shows how a user would need to include both discrete and continuous data for some defined queries.

Mentions: We developed RedX according to the design, and successfully completed five major functions as specified in the design. First, we created the EHR view of de-identified research data that allowed users to browse the data in the context of an individual patient. We also created a “query by example” button that allowed query development based on data elements within the patient record (Figure 2). Third, we created patient cohort lists, where the results of a query could be viewed as a list of patients in that cohort, allowing a user to navigate directly to that patient record (Figure 3). Fourth, we created a query combination tool, allowing for Boolean combinations of simpler element values. Finally, we created data distribution views, specifically for frequency vs. value, for both continuous and discrete data (Figure 4).


Research Data Explorer: Lessons Learned in Design and Development of Context-based Cohort Definition and Selection.

Wilcox A, Vawdrey D, Weng C, Velez M, Bakken S - AMIA Jt Summits Transl Sci Proc (2015)

RedX data distribution view for Troponin test. Data elements could be either continuous, discrete or both. The list of common discrete elements shows how a user would need to include both discrete and continuous data for some defined queries.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525259&req=5

f4-2092396: RedX data distribution view for Troponin test. Data elements could be either continuous, discrete or both. The list of common discrete elements shows how a user would need to include both discrete and continuous data for some defined queries.
Mentions: We developed RedX according to the design, and successfully completed five major functions as specified in the design. First, we created the EHR view of de-identified research data that allowed users to browse the data in the context of an individual patient. We also created a “query by example” button that allowed query development based on data elements within the patient record (Figure 2). Third, we created patient cohort lists, where the results of a query could be viewed as a list of patients in that cohort, allowing a user to navigate directly to that patient record (Figure 3). Fourth, we created a query combination tool, allowing for Boolean combinations of simpler element values. Finally, we created data distribution views, specifically for frequency vs. value, for both continuous and discrete data (Figure 4).

Bottom Line: The primary innovation of RedX was the electronic health record view of patient data, to provide better contextual understanding for non-technical users in building complex data queries.The design of RedX around this need identified multiple functions that would use individual patient views to better understand population-based data, and vice-versa.During development, the more necessary and valuable components of RedX were refined, leading to a functional self-service query and cohort identification tool.

View Article: PubMed Central - PubMed

Affiliation: Intermountain Healthcare, Salt Lake City, UT.

ABSTRACT
Research Data eXplorer (RedX) was designed to support self-service research data queries and cohort identification from clinical research databases. The primary innovation of RedX was the electronic health record view of patient data, to provide better contextual understanding for non-technical users in building complex data queries. The design of RedX around this need identified multiple functions that would use individual patient views to better understand population-based data, and vice-versa. During development, the more necessary and valuable components of RedX were refined, leading to a functional self-service query and cohort identification tool. However, with the improved capabilities and extensibility of other applications for data querying and navigation, our long-term implementation and dissemination plans have moved towards consolidation and alignment of RedX functions as enhancements in these other initiatives.

No MeSH data available.