Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs
| Autor: | Jennifer H. Garvin, Paul A. Heidenreich, Mary K. Goldstein, Megha Kalsy, Michael E. Matheny, Bruce E. Bray, Ruth M. Reeves, Dan Bolton, Natalie Kelly, Glenn T. Gobbel, Stéphane M. Meystre, Julia Heavirland, Youngjun Kim, Andrew Redd |
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| Rok vydání: | 2017 |
| Předmět: |
quality measures
heart failure Health Informatics 030204 cardiovascular system & hematology computer.software_genre 03 medical and health sciences 0302 clinical medicine Health Information Management Health care medicine informatics 030212 general & internal medicine Veterans Affairs Original Paper Ejection fraction Inpatient care business.industry left ventricular ejection fraction (EF) medicine.disease Test (assessment) Heart failure Informatics Cohort natural language processing (NLP) Artificial intelligence business computer Natural language processing |
| Zdroj: | JMIR Medical Informatics |
| ISSN: | 2291-9694 |
| Popis: | Background: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. Objective: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. Methods: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) |
| Databáze: | OpenAIRE |
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