CC BY-NC-ND 4.0 · Appl Clin Inform 2018; 09(03): 528-540
DOI: 10.1055/s-0038-1666994
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Identifying Asthma Exacerbation-Related Emergency Department Visit Using Electronic Medical Record and Claims Data

Agnes S. Sundaresan
1  Department of Epidemiology and Health Services Research, Geisinger Health System, Danville, Pennsylvania, United States
2  Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States
Gargi Schneider
3  MedPeds Program, Geisinger Medical Center, Danville, Pennsylvania, United States
Joy Reynolds
4  Lewis Katz School of Medicine at Temple University, Temple University, Philadelphia, Pennsylvania, United States
H. Lester Kirchner
5  Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States
6  Department of Clinical Sciences, Geisinger Commonwealth School of Medicine, Geisinger Health System, Scranton, Pennsylvania, United States
7  Department of Pediatrics, Global and Immigrant Health Section, Baylor College of Medicine, Houston, Texas, United States
› Author Affiliations
Further Information

Publication History

07 December 2017

28 May 2018

Publication Date:
18 July 2018 (online)


Background Asthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.

Objective We propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.

Methods We performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.

Results There were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91–0.94) and 0.94 (0.93–0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.

Conclusion We were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in the project. The study was reviewed by the Geisinger Institutional Review Board.

Supplementary Material