TY - JOUR
T1 - Artificial Intelligence Assesses Clinicians’ Adherence to Asthma Guidelines Using Electronic Health Records
AU - Sagheb, Elham
AU - Wi, Chung Il
AU - Yoon, Jungwon
AU - Seol, Hee Yun
AU - Shrestha, Pragya
AU - Ryu, Euijung
AU - Park, Miguel
AU - Yawn, Barbara
AU - Liu, Hongfang
AU - Homme, Jason
AU - Juhn, Young
AU - Sohn, Sunghwan
N1 - Funding Information:
This study was supported by National Institutes of Health–funded R21 Grant R21 AI142702 and R01 Grant R01 HL126667.
Funding Information:
We thank Mrs Kelly Okeson for her administrative assistance. E. Sagheb conceptualized and designed the study, developed the algorithms, collected and interpreted the data, drafted the initial manuscript, and reviewed and revised the manuscript. S. Sohn conceptualized and designed the study; supervised data collection, algorithm development, and analysis; drafted the initial manuscript; interpreted the data; and reviewed and finalized the manuscript. Y. Juhn conceptualized and designed the study; supervised data collection, algorithm development, and analysis; interpreted the data; and reviewed and finalized the manuscript. C-I Wi, J. Yoon, Y. Seol, and P. Shrestha conceptualized and designed the study, collected and interpreted the data, and reviewed and revised the manuscript. J. Homme, B. Yawn, M. Park, H. Liu, and E. Ryu analyzed and interpreted the data and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. This study was supported by National Institutes of Health?funded R21 Grant R21 AI142702 and R01 Grant R01 HL126667. Y. Juhn is Principal Investigator of the Respiratory Syncytial Virus incidence study supported by GlaxoSmithKline, United Kingdom. The rest of the authors declare that they have no relevant conflicts of interest.
Publisher Copyright:
© 2021 American Academy of Allergy, Asthma & Immunology
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Adherence to asthma guidelines
KW - Automated chart review
KW - Documentation variation
KW - National asthma education and prev4ention program
KW - Natural language processing
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U2 - 10.1016/j.jaip.2021.11.004
DO - 10.1016/j.jaip.2021.11.004
M3 - Article
C2 - 34800704
AN - SCOPUS:85121988684
SN - 2213-2198
VL - 10
SP - 1047-1056.e1
JO - Journal of Allergy and Clinical Immunology: In Practice
JF - Journal of Allergy and Clinical Immunology: In Practice
IS - 4
ER -