A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records.
Background: Natural language processing allows efficient extraction of clinical variables and outcomes from electronic health records (EHRs). However, measuring pragmatic clinical trial outcomes may demand accuracy that exceeds natural language processing performance. Combining natural language proc...
| Publicado en: | Clinical Trials Vol. 23; no. 2; pp. 145 - 155 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications, Ltd.
Apr2026
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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=192767427&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192767427 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17407745 AB6 jtl: Clinical Trials issn: 17407745 maglogo: N pubinfo: dt: Apr2026 vid: 23 iid: 2 pid: 33180 pub: Sage Publications, Ltd. place: <Blank> artinfo: ui: 192767427 191558123 192767427 192767427 10.1177/17407745251405386 192767427 ppf: 145 ppct: 10 formats: tig: atl: A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records. aug: au: Lee, Robert Y Li, Kevin S Sibley, James Cohen, Trevor Lober, William B O'Brien, Janaki LeDuc, Nicole Mallon Andrews, Kasey Ungar, Anna Walsh, Jessica Nielsen, Elizabeth L Dotolo, Danae G Kross, Erin K affil: Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington School of Medicine, Seattle, WA, USA sug: subj: Natural Language Processing Electronic Health Records Clinical Trials Outcome Assessment Funding Source Human Inpatients Goals and Objectives Descriptive Statistics Critical Illness Communication Sensitivity and Specificity Confidence Intervals ab: Background: Natural language processing allows efficient extraction of clinical variables and outcomes from electronic health records (EHRs). However, measuring pragmatic clinical trial outcomes may demand accuracy that exceeds natural language processing performance. Combining natural language processing with human adjudication can address this gap, yet few software solutions support such workflows. We developed a modular, scalable system for natural language processing-screened human abstraction to measure the primary outcomes of two clinical trials. Methods: In two clinical trials of hospitalized patients with serious illness, a deep-learning natural language processing model screened electronic health record passages for documented goals-of-care discussions. Screen-positive passages were referred for human adjudication using a REDCap-based system to measure the trial outcomes. Dynamic pooling of passages using structured query language within the REDCap database reduced unnecessary abstraction while ensuring data completeness. Results: In the first trial (N = 2512), natural language processing identified 22,187 screen-positive passages (0.8%) from 2.6 million electronic health record passages. Human reviewers adjudicated 7494 passages over 34.3 abstractor-hours to measure the cumulative incidence and time to first documented goals-of-care discussion for all patients with 92.6% patient-level sensitivity. In the second trial (N = 617), natural language processing identified 8952 screen-positive passages (1.6%) from 559,596 passages at a threshold with near-100% sensitivity. Human reviewers adjudicated 3509 passages over 27.9 abstractor-hours to measure the same outcome for all patients. Discussion: We present the design and source code for a scalable and efficient pipeline for measuring complex electronic health record-derived outcomes using natural language processing-screened human abstraction. This implementation is adaptable to diverse research needs, and its modular pipeline represents a practical middle ground between custom software and commercial platforms. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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