View source for The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data

===Introduction===
[[Data warehouse|Data warehouses]] have emerged as the place where information resides from the patient electronic medical record, lab results, radiology results, registries, other databases, and fragmented clinical information.  Most academic centers and healthcare providers are challenged with the collection and organization of data.  Data warehouses have emerged as the keeper of information including modeling of data elements and clinical models, included in these models are the [[CDS|clinical decision support systems]].  Mayo Clinic’s Enterprise Data Trust is a support to the decision-making process.  Various resources allow organization of patient data, education, research and administrative data.<ref name = ‘Enterprise Data’>Chute, C.G., Beck, S. A., Fisk, T.B., Mohr, D. N. (2010) The enterprise data trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.  Journal of the American Medical Informatics Association. 17(2), 131-135.  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3000789/</ref>

===Semantically Integrated Warehousing===
Mayo Enterprise Data Trust (EDT) collects data from internal and external sources and is the truth for data aggregated into the system.  It is derived from all facets of Mayo, including practice, research, education and administration.  Mayo discovered four principles to be key in the approach to data.
*Subject oriented-about a subject area
*Integrated-variety of sources to create a whole
*Time-variant-particular time period
*Non-volatile-data is stable and never removed
===Enterprise Information Management===

====Enterprise Data Governance====
This group oversees the modeling, standardization, and quality of the data and metadata.  It manages the applications, vocabulary, applications and oversees the standardizations and quality of the data capture.  

====Enterprise Data Modeling====
The enterprise data modeling captures the current and future stat of the data assets.
*Subjects-defines the activities for example for the Individual
*Concepts-collection of data is contained in more than one area
*Business information models-organization of the data that supports the Concepts.

====Enterprise Vocabulary System====
Mayo has worked for a common vocabulary and terminology service, for example [[HL7]].  Several partnerships exist for terminology management. 

====Enterprise Managed Metadata Environment====
This system is a means of gaining knowledge for the ever growing pool of data.  This allows the retrieval of data to be used for more informed and in-depth decisions in a timely manner.

====Enterprise Data Trust====
The enterprise data trust accumulates a diverse collection of data and allows for a historical collection of data.  Although the [[EHR]] (electronic health record) captures real-time date, the enterprise data trust is meant to inform about outcomes in research, evidence base practice, quality improvement and other analytical activities.  

===Conclusions===
Research is complex and information is complex.  Mayo has successfully integrated the clinical complex data capture through the Data Governance process.  They have discovered that the information transparency has transformed the capacity to for quality improvement, research productivity, and best-practice monitoring.

====Area of interest and comments====
A well-structured information system allows for quality improvement.  Mayo is realizing success in the research area and analysis of the referrals, quality dashboards, and infection and infection-related case data.  With a query based data warehouse the possibilities are endless to the a fully integrated holistic approach to patient outcomes. 

== Reference ==
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[[Category:CIS]]
[[Category:CDS]]
[[Category:Databases, Big Data, and Data Quality]]