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Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology

View Article: PubMed Central - PubMed

ABSTRACT

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.

No MeSH data available.


Architecture of the clinical data warehouse project. (A) Key layers of the data warehouse layout. (B) Components in the implementation. The data lake component as well as further reporting and mining tools have not yet been implemented and are therefore rendered in gray.
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f1-10.1177_1176935117694349: Architecture of the clinical data warehouse project. (A) Key layers of the data warehouse layout. (B) Components in the implementation. The data lake component as well as further reporting and mining tools have not yet been implemented and are therefore rendered in gray.

Mentions: Figure 1 shows a high-level diagram which depicts the general work flow of the clinical data warehouse solution including: operational databases and external data sources, Extraction Transformation Loading (ETL) interfaces, data warehouse, data mart, and data analysis as shown in Figure 1A. Although the traditional approach for building such a system would begin with extracting data from the operational databases and external sources and then implementing an ETL to populate the warehouse, our team recognized the large number of failed attempts at other institutions to construct a functional system in this manner. Although the specific details may vary, the primary design flaw of many of those projects was that the primary focus was on the extraction of information from the data sources, before clearly identifying the use-case scenarios and clarifying the required data mapping. Being aware of the potential pitfalls of this approach, our team decided to implement a “backward-in” strategy for our project.


Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology
Architecture of the clinical data warehouse project. (A) Key layers of the data warehouse layout. (B) Components in the implementation. The data lake component as well as further reporting and mining tools have not yet been implemented and are therefore rendered in gray.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5392017&req=5

f1-10.1177_1176935117694349: Architecture of the clinical data warehouse project. (A) Key layers of the data warehouse layout. (B) Components in the implementation. The data lake component as well as further reporting and mining tools have not yet been implemented and are therefore rendered in gray.
Mentions: Figure 1 shows a high-level diagram which depicts the general work flow of the clinical data warehouse solution including: operational databases and external data sources, Extraction Transformation Loading (ETL) interfaces, data warehouse, data mart, and data analysis as shown in Figure 1A. Although the traditional approach for building such a system would begin with extracting data from the operational databases and external sources and then implementing an ETL to populate the warehouse, our team recognized the large number of failed attempts at other institutions to construct a functional system in this manner. Although the specific details may vary, the primary design flaw of many of those projects was that the primary focus was on the extraction of information from the data sources, before clearly identifying the use-case scenarios and clarifying the required data mapping. Being aware of the potential pitfalls of this approach, our team decided to implement a “backward-in” strategy for our project.

View Article: PubMed Central - PubMed

ABSTRACT

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.

No MeSH data available.