Identifying pneumonia sub-types from electronic health records using rule-based algorithms

Authors

Harshad Hegde, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Ingrid Glurich, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Aloksagar Panny, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Jayanth G. Vedre, Critical Care Medicine Department, Marshfield Clinic Health System, Marshfield, United States.
Jeffrey J. VanWormer, Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, United States.
Richard Berg, Office of Research Computing and Analytics, Marshfield Clinic Research Institute, Marshfield, United States.Follow
Frank A. Scannapieco, Department of Oral Biology, School of Dental Medicine, State University of New York at Buffalo, Buffalo, United States.
Jeffrey Miecznikowski, Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, United States.
Amit Acharya, Advocate Aurora HealthFollow

Recommended Citation

Hegde H, Glurich I, Panny A, et al. Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms. Methods Inf Med. 2022;61(1-02):29-37. doi:10.1055/a-1801-2718

Affiliations

Advocate Aurora Research Institute

Abstract

Background: International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations, where pneumonia is standardly subtyped by settings, exposures and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHR), frequently in non-structured formats including radiological interpretation or clinical notes that complicate electronic classification.

Objective: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR.

Methods: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for 'rule of two' pneumonia-related codes or one ICD code and radiologically-confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support sub-classification based on features including symptomatic patient point of entry into the healthcare system timing of pneumonia emergence and identification of clinical, laboratory or medication orders that informed definition of the pneumonia sub-classification algorithm.

Results: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following NLP classification of pneumonia status as 'negative' or 'unknown'. Subtyping of 83,387 episodes identified: community acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), healthcare-acquired (5%), ventilator-associated (0.4%) cases, and 9.4% were not classifiable by the algorithm.

Conclusion: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.

Document Type

Article

PubMed ID

35299265