RESUMO
Clinical phenotyping is an emerging research information systems capability. Research uses of electronic health record (EHR) data may require the ability to identify clinical co-morbidities and complications. Such phenotypes may not be represented directly as discrete data elements, but rather as frequency, sequential and temporal patterns in billing and clinical data. These patterns' complexity suggests the need for a robust yet flexible extract, transform and load (ETL) process that can compute them. This capability should be accessible to investigators with limited ability to engage an IT department in data management. We have developed such a system, Eureka! Clinical Analytics. It extracts data from an Excel spreadsheet, computes a broad set of phenotypes of common interest, and loads both raw and computed data into an i2b2 project. A web-based user interface allows executing and monitoring ETL processes. Eureka! is deployed at our institution and is available for deployment in the cloud.
RESUMO
Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW's web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.