ArticleJMIR medical informatics2018
Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.
Article in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Healthcare System Resilience and Adaptation: A 6-Year Analysis of Heart Failure Care in the Veterans Affairs System.Circulation. Heart failure · 2026Article
- Using Natural Language Processing to Improve Fall Documentation in VA Nursing Home Residents.Journal of the American Medical Directors Association · 2026Article
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- Healthcare System Resilience and Adaptation: A Six-Year Analysis of Heart Failure Care in the Veterans Affairs System.medRxiv : the preprint server for health sciences · 2025Article
- Clinical and research applications of natural language processing for heart failure.Heart failure reviews · 2025Review
- Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.ESC heart failure · 2024Article
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports.Healthcare (Basel, Switzerland) · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases.Journal of personalized medicine · 2022Article
- Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.JMIR medical informatics · 2021Article
- Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions.Current atherosclerosis reports · 2021Review
- Delirium and Functional Recovery in Patients Discharged to Skilled Nursing Facilities After Hospitalization for Heart Failure.JAMA network open · 2021Article
- An extensive review of tools for manual annotation of documents.Briefings in bioinformatics · 2021Review
- A registry-based algorithm to predict ejection fraction in patients with heart failure.ESC heart failure · 2020Article
- Systematizing Heart Failure Population Health.Heart failure clinics · 2020Review
- The use of the PARIHS framework in implementation research and practice-a citation analysis of the literature.Implementation science : IS · 2020Review
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Authors and funding
14 authors.
Funding
Abstract
backgroundWe 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.
objectiveTo accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF.
methodsWe automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF.
resultsThe CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF.
conclusionsThe CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.
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