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Methods Inf Med 2022; 61(01/02): 029-037
DOI: 10.1055/a-1801-2718
Original Article

Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms

Harshad Hegde
1   Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
,
Ingrid Glurich
1   Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
,
Aloksagar Panny
1   Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
,
Jayanth G. Vedre
2   Department of Critical Care Medicine, Marshfield Clinic Health System, Marshfield, Wisconsin, United States
,
Jeffrey J. VanWormer
3   Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
,
Richard Berg
4   Office of Research Computing and Analytics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
,
Frank A. Scannapieco
5   Department of Oral Biology, School of Dental Medicine, State University of New York at Buffalo, Buffalo, New York, United States
,
Jeffrey Miecznikowski
6   Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, New York, United States
,
Amit Acharya
1   Center for Oral and Systemic Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
7   Advocate Aurora Research Institute, Advocate Aurora Health, Downers Grove, Illinois, United States
› Author Affiliations
Funding Research reported in this publication was supported by the National Institute of Dental & Craniofacial Research of the National Institutes of Health under Award Number 1R03DE027020–01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Abstract

Background The 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 (EHRs), frequently in nonstructured 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 subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification 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 Natural Language Processing 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%), health care–acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases 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.

Keywords

pneumonia - classification - natural language processing - electronic health record - health informatics


Publication History

Received: 28 August 2021

Accepted: 15 March 2022

Accepted Manuscript online:
17 March 2022

Article published online:
28 June 2022

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