Bridging the Gaps in Analytical Assets Management at Healthcare Institutions

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The healthcare industry is in the process of rapid adoption of electronic health record systems. These systems may be lacking when it comes to managing reporting and analytics

Adoption of Modern EHR Systems Brings New Challenges in Data Management

A transition to a single EHR system for the entire Healthcare institution leads to centralized reporting and data analysis. While such centralization has substantial benefits, it also comes with significant complexities arising from extensive number of heterogeneous analytical assets and complex data extraction processes which require management and monitoring.

EHR vendors including top players, generally are not focused on providing comprehensive infrastructures for organizing analytical assets, managing data extraction processes and tracking requests for reports and analytical insights. Lack of such Infrastructure leads to potentially significant duplication of assets, volatile report generation processes, excessive demand queue for reports and analytical insights and other negative implications.

What is Considered an Analytical Asset?

For the purpose of this document it is important to define what analytical assets are, which processes are associated with them, and the types of entities and relationships involved in the analytical processing.

Reporting and Analysis are commonly considered main areas of data analytics. While both of these activities draw upon the same collected data, reporting and analysis are different in terms of their purpose, outputs and delivery.

Reporting: The process of organizing data into informational summaries in order to monitor how different areas of a business are performing. Reporting translates raw data into information.

Analysis: The process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance. Analysis transforms data and information into insights.

Reporting and Analysis activities result a number of artifacts. These artifacts are for example, report files that are stored on network shares, or other entities produces within corresponding analytical infrastructure – such as cubes, views, and dashboards. Other types of artifacts associated with analytical activities are those that facilitate these activities – such as stored procedures or scripts for data extraction.

The analytical activities are usually performed per requests from sites, departments, clinicians and researchers. These requests are also artifacts associated with analytical activities. The organizational unit that handles such requests is responsible for managing the demand and the lifecycle of requests starting from the point when a report was requested, to the point when the report was delivered to the requestor.

All analytical activities, the artifacts, and their lifecycles has a variety of associated metadata – any information that describes and qualifies analytical artifacts. Such metadata includes ownership, network drives where reports are stored, reporting metrics, systems where analysis is performed, relationships between artifacts and much more.

The artifacts of the analytical activity and their metadata cumulatively represent analytical assets.

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