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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.


Number of publications per year retrieved from PubMed related to Data Warehousing and Big Data.
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f1-2092396: Number of publications per year retrieved from PubMed related to Data Warehousing and Big Data.

Mentions: The last decade has seen an explosion in the amount of clinical data stored in electronic form. Examples of institutions that have improved quality and efficiency of healthcare using health information technology (health IT) have indicated the potential value of electronic health records (EHRs) and electronic health data (1). Government incentives for healthcare adoption of electronic health records have moved EHR adoption to the late majority, with most hospitals and ambulatory providers using EHRs for patients (2,3). Coincident with these trends have been the increased use of large clinical data warehouses for both research and quality improvement. A query of publications in PubMed relating to “data warehouse” or “data warehousing” shows growth in the number of publications in the field growing substantially in the last five years after modest growth before. A similar search for “big data” shows a more dramatic rise (see Figure 1). With this increase in availability of data has come the promise of using the data for research, such as retrospective analyses (4), knowledge discovery (5), cohort identification (6), and phenotype analysis (7,8). Many large research projects have begun efforts to maximize the amount of data available for such studies by linking data across institutions, in population databases (9–11), research networks (12), and research registries (13).


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)

Number of publications per year retrieved from PubMed related to Data Warehousing and Big Data.
© Copyright Policy
Related In: Results  -  Collection

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

f1-2092396: Number of publications per year retrieved from PubMed related to Data Warehousing and Big Data.
Mentions: The last decade has seen an explosion in the amount of clinical data stored in electronic form. Examples of institutions that have improved quality and efficiency of healthcare using health information technology (health IT) have indicated the potential value of electronic health records (EHRs) and electronic health data (1). Government incentives for healthcare adoption of electronic health records have moved EHR adoption to the late majority, with most hospitals and ambulatory providers using EHRs for patients (2,3). Coincident with these trends have been the increased use of large clinical data warehouses for both research and quality improvement. A query of publications in PubMed relating to “data warehouse” or “data warehousing” shows growth in the number of publications in the field growing substantially in the last five years after modest growth before. A similar search for “big data” shows a more dramatic rise (see Figure 1). With this increase in availability of data has come the promise of using the data for research, such as retrospective analyses (4), knowledge discovery (5), cohort identification (6), and phenotype analysis (7,8). Many large research projects have begun efforts to maximize the amount of data available for such studies by linking data across institutions, in population databases (9–11), research networks (12), and research registries (13).

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.