Congestive heart failure information extraction framework for automated treatment performance measures assessment

• Published in: Journal of the American Medical Informatics Association : JAMIA 2017-04, Vol.24 (e1), p.e40-e46
• Main Authors: Meystre, Stéphane M, Kim, Youngjun, Gobbel, Glenn T, Matheny, Michael E, Redd, Andrew, Bray, Bruce E, Garvin, Jennifer H
• Format: Article
• Language: English
• Subjects: Cardiotonic Agents - therapeutic use; Editor's Choice; Electronic Health Records; Heart Failure - drug therapy; Heart Failure - physiopathology; Hospitals, Veterans; Humans; Information Storage and Retrieval - methods; Machine Learning; Natural Language Processing; Research and Applications; Ventricular Function, Left
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocw097

Objective: This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system – CHIEF – developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF. Design: CHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications. Measurements: The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F1-measure. Results: In general, CHIEF extracted CHF medications with high recall (>0.990) and good precision (0.960–0.978). Mentions of Left Ventricular Ejection Fraction were also extracted with high recall (0.978–0.986) and precision (0.986–0.994), and quantitative values of Left Ventricular Ejection Fraction were found with 0.910–0.945 recall and with high precision (0.939–0.976). Reasons for not prescribing CHF medications were more difficult to extract, only reaching fair accuracy with about 0.310–0.400 recall and 0.250–0.320 precision. Conclusion: This study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.