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Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records.
Michael Weiner1, Paul R Dexter2, Kim Heithoff3
1Regenstrief Institute, Inc., Indianapolis, IN; Indiana University, Indianapolis, IN; Center for Health Information and Communication, US Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service CIN 13-416, Richard L. Roudebush VA Medical Center, Indianapolis, IN.
Chest
|December 21, 2020
Summary
Natural language processing (NLP) significantly improves chronic cough (CC) identification in electronic health records (EHRs). This method found nearly seven times more CC patients than traditional coding, aiding in understanding disease burden.
Area of Science:
- Medical Informatics
- Clinical Research
- Natural Language Processing
Background:
- Chronic cough (CC) affects 10% of adults, leading to significant healthcare costs and reduced quality of life.
- Identifying CC in electronic health records (EHRs) is challenging due to incomplete diagnostic coding.
Purpose of the Study:
- To determine if Natural Language Processing (NLP) can accurately identify cough in EHRs.
- To characterize adult patients and encounters associated with chronic cough.
Main Methods:
- Utilized NLP to analyze text notes in a Midwestern EHR system (2005-2015).
- Defined CC by three encounters spanning 56-120 days.
- Iteratively refined NLP models, achieving a positive predictive value >90% for cough encounters.
Main Results:
- NLP identified 23,371 patients with CC (61% women).
- NLP alone identified 74% of CC patients, significantly outperforming diagnostic codes or medications (15%).
- The positive predictive value of NLP was 97%; 3.0% of patients were referred, primarily to pulmonary medicine.
Conclusions:
- NLP effectively identifies a large cohort of patients with chronic cough.
- NLP enhances CC detection nearly sevenfold compared to traditional methods, improving the understanding of CC burden.
- Most identified CC cases were managed in primary care, highlighting the need to characterize treatment and unmet needs.