Determining Section Types to Capture Key Clinical Data for Automation of Quality Measurement for Inpatients with Chronic Heart Failure
ABSTRACT Information extraction (IE) can be used to extract quality measurement data for Departme... more ABSTRACT Information extraction (IE) can be used to extract quality measurement data for Department of Veterans Affairs (VA) patients with Congestive Heart Failure (CHF) more efficiently than through manual review. Automated concept extraction will strongly benefit from targeting of relevant document sections for analysis. Based on human review, Assessment, Current history, Medications, and Echocardiogram sections contained a high prevalence of concepts for CHF quality measurement.
Journal of the American Medical Informatics Association, 2012
Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality me... more Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.
Determining Section Types to Capture Key Clinical Data for Automation of Quality Measurement for Inpatients with Chronic Heart Failure
ABSTRACT Information extraction (IE) can be used to extract quality measurement data for Departme... more ABSTRACT Information extraction (IE) can be used to extract quality measurement data for Department of Veterans Affairs (VA) patients with Congestive Heart Failure (CHF) more efficiently than through manual review. Automated concept extraction will strongly benefit from targeting of relevant document sections for analysis. Based on human review, Assessment, Current history, Medications, and Echocardiogram sections contained a high prevalence of concepts for CHF quality measurement.
Journal of the American Medical Informatics Association, 2012
Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality me... more Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.
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