Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records

Elham Sagheb; Chung-Il Wi; Jungwon Yoon; Hee Yun Seol; Pragya Shrestha; Euijung Ryu; Miguel Park; Barbara Yawn; Hongfang Liu; Jason Homme; Young Juhn; Sunghwan Sohn

doi:10.1016/j.jaip.2021.11.004

Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records

J Allergy Clin Immunol Pract. 2022 Apr;10(4):1047-1056.e1. doi: 10.1016/j.jaip.2021.11.004. Epub 2021 Nov 17.

Authors

Elham Sagheb¹, Chung-Il Wi², Jungwon Yoon³, Hee Yun Seol⁴, Pragya Shrestha², Euijung Ryu⁵, Miguel Park⁶, Barbara Yawn⁷, Hongfang Liu¹, Jason Homme², Young Juhn⁸, Sunghwan Sohn⁹

Affiliations

¹ Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn.
² Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn.
³ Department of Pediatrics, Myongji Hospital, Goyang, South Korea.
⁴ Pusan National University, Yangsan Hospital, Yangsan, South Korea.
⁵ Department of Health Sciences Research, Mayo Clinic, Rochester, Minn.
⁶ Division of Allergic Diseases, Mayo Clinic, Rochester, Minn.
⁷ Department of Family and Community Health, University of Minnesota, Minneapolis, Minn.
⁸ Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn. Electronic address: Juhn.young@mayo.edu.
⁹ Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn. 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.

Keywords: Adherence to asthma guidelines; Automated chart review; Documentation variation; National asthma education and prev4ention program; Natural language processing.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Artificial Intelligence
Asthma* / diagnosis
Asthma* / drug therapy
Asthma* / epidemiology
Child
Cross-Sectional Studies
Electronic Health Records*
Humans
Retrospective Studies

Abstract

Publication types

MeSH terms

Grants and funding