Text data extraction for a prospective, research-focused data mart: implementation and validation

• Published in: BMC medical informatics and decision making Vol. 12; no. 1; p. 106
• Main Authors: Hinchcliff, Monique, Just, Eric, Podlusky, Sofia, Varga, John, Chang, Rowland W, Kibbe, Warren A
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.09.2012; BioMed Central Ltd; BMC
• Subjects: Analysis; Automatic Data Processing; Correspondence; Data Mining - methods; Data Mining - standards; Data warehouses; Development and progression; Electronic Health Records; Electronic records; File servers; Health Informatics; Humans; Information storage and retrieval; Information systems; Information Systems and Communication Service; Management of Computing and Information Systems; Medical Informatics; Medical records; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Public software; Pulmonary function tests; Respiratory Function Tests; Scleroderma (Disease); Scleroderma, Systemic; Sclerosis; Servers; Software; Software - standards; Systemic scleroderma; Translational Medical Research; United States; United States; Chicago Illinois; Information storage and retrieval; Automatic data processing; Electronic health records; Medical informatics; Information systems
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-12-106

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.