Challenges of building clinical data analysis solutions

Abstract

Increasingly, owners of clinical information systems are turning to clinical data warehouses (CDWs) to store and to analyze their data. The CDW allows institutions to make better use of their clinical data that has been collected through its information systems. A CDW extracts data from these systems, transforms it into a usable form, and then allows users to view and analyze years of data across a large cross section of patient charts. Although warehouses have existed in healthcare for some time, there are relatively few institutions that maintain patient charts in a CDW. This is, in part, because of the challenges often seen when attempting to warehouse this type of data. These include integrating a diverse set of care practices and a variety of definitions for common data elements like medications, observations, treatments, units of measure, and even unique patient identifiers. In addition, these systems often struggle with a high level of inconsistent and/or incomplete data that must be cleaned up on a regular basis. Unlike other data warehouse systems, CDWs are often expected to gather data around the clock and in a manner that has minimum impact to the performance of the source Clinical Information Systems. Finally, CDWs often have a diverse range of clinical and administrative users. This often leads to a need for a variety of applications and/or tools for viewing and analyzing the data.