Abstract
Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods. Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results: There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions: Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public.
| Original language | English (US) |
|---|---|
| Article number | 106 |
| Journal | BMC Medical Informatics and Decision Making |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2012 |
Funding
The authors would like to thank Abel Kho, MD and Justin Starren, MD PhD for reviewing the manuscript. The authors would also like to thank the Northwestern Medical Enterprise Data Warehouse team for providing a foundation for this work. This work was supported in part by the National Institutes of Health [Eunice Kennedy Shriver National Institute of Child Health & Human Development K12 HD055884 to MH; National Institute of Arthritis and Musculoskeletal and Skin P60 AR48098 to RWC; and National Center for Research Resources UL1RR025741 to RWC and WAK] and by an Arthritis Foundation Chapter Grant to MH. No funding agency played any role in the project study design or data interpretation.
Keywords
- Automatic data processing
- Electronic health records
- Information storage and retrieval
- Information systems
- Medical informatics
ASJC Scopus subject areas
- Health Policy
- Health Informatics
- Computer Science Applications