ArticleThe journal of allergy and clinical immunology. In practice2022
Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.
Article in The journal of allergy and clinical immunology. In practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Artificial intelligence in atopic dermatitis: Current applications and future perspectives.Chinese medical journal · 2026Review
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
- Application of a natural language processing algorithm to early asthma ascertainment for adults in the era of electronic health records.The journal of allergy and clinical immunology. Global · 2026Article
- Evaluation of large Language models on pediatric asthma: a comparative study of Claude3-Opus, Gemini 2.0, ChatGPT-4o, and DeepSeek-a cross-sectional questionnaire study.BMC medical informatics and decision making · 2026Article
- Management of Patients With Comorbid Asthma and Obesity: A Large Language Model Evaluation of Clinical Documentation.The journal of allergy and clinical immunology. In practice · 2026Article
- Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Inhaled corticosteroids for COVID-19: a real-world data analysis on guideline adherence.BJGP open · 2025Article
- Article
- Leveraging Electronic Health Records for Guideline-Based Asthma Documentation.The journal of allergy and clinical immunology. In practice · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundClinicians' asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians' adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data.
objectiveThis study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines.
methodsThis is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text.
resultsNatural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions.
conclusionsNatural language processing technologies may enable the automated assessment of clinicians' documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms.
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