Sagheb, Elham; Wi, Chung-Il; Yoon, Jungwon; Seol, Hee Yun; Shrestha, Pragya; Ryu, Euijung; Park, Miguel; Yawn, Barbara; Liu, Hongfang; Homme, Jason; Juhn, Young; Sohn, Sunghwan
The journal of allergy and clinical immunology. In practice
2021Nov ; 145 ( 48 ) :.
PMID : 34800704
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Sagheb, Elham - Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester MN.
Wi, Chung-Il - Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester MN.
Yoon, Jungwon - Department of Pediatrics, Myongji Hospital, South Korea.
Seol, Hee Yun - Pusan National University, Yangsan Hospital, South Korea.
Shrestha, Pragya - Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester MN.
Ryu, Euijung - Department of Health Sciences Research, Mayo Clinic, Rochester MN.
Park, Miguel - Division of Allergic Diseases, Mayo Clinic, Rochester MN.
Yawn, Barbara - University of Minnesota, Minneapolis MN.
Liu, Hongfang - Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester MN.
Homme, Jason - Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester MN.
Juhn, Young - Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester MN. Electronic address: Juhn.young@mayo.edu.
Sohn, Sunghwan - Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester MN. Electronic address: sohn.sunghwan@mayo.edu.
ABSTRACT
BACKGROUND: Clinicians' 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. OBJECTIVE: This 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.
METHODS: This 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.
RESULTS: Natural 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.
CONCLUSIONS: Natural 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. CI - Copyright ??2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
keyword
adherence to asthma guidelines; automated chart review; documentation variation; national asthma education and prevention program; natural language processing
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