Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records.
Background: Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve det...
| Publicado en: | CHEST Vol. 159; no. 6; pp. 2346 - 2356 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
American College of Chest Physicians
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150469633&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150469633 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00123692 1G4 jtl: CHEST issn: 00123692 maglogo: N pubinfo: dt: Jun2021 vid: 159 iid: 6 pid: 2556 pub: American College of Chest Physicians place: Glenview, Illinois artinfo: ui: 150469633 150469633 NLM33345951 150469633 10.1016/j.chest.2020.12.011 NLM33345951 150469633 ppf: 2346 ppct: 10 formats: tig: atl: Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records. aug: au: Weiner, Michael Dexter, Paul R. Heithoff, Kim Roberts, Anna R. Liu, Ziyue Griffith, Ashley Hui, Siu Schelfhout, Jonathan Dicpinigaitis, Peter Doshi, Ishita Weaver, Jessica P. affil: Regenstrief Institute, Inc., Indianapolis, IN sug: subj: Cough Diagnosis Internal Medicine Statistics and Numerical Data United States Middle Age Chronic Disease Human Young Adult Adult Male Adolescence Aged Aged, 80 and Over Female Comparative Studies Multicenter Studies Evaluation Research Validation Studies Middle Aged: 45-64 years Adult: 19-44 years Adolescent: 13-18 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection.Research Question: Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC?Study Design and Methods: A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals.Results: Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals).Limitations: Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified.Interpretation: NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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