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. 2021 Jan 15;28(1):126-131.
doi: 10.1093/jamia/ocaa213.

Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome

Sarah DeLozier  1 , Peter Speltz  1 , Jason Brito  1 , Leigh Anne Tang  1 , Janey Wang  1 , Joshua C Smith  1 , Dario Giuse  1 , Elizabeth Phillips  2 , Kristina Williams  2 , Teresa Strickland  2 , Giovanni Davogustto  2 , Dan Roden  1   2 , Joshua C Denny  1   2
Affiliations

Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome

Sarah DeLozier et al. J Am Med Inform Assoc. .

Abstract

Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation. Between 2016 and 2018, the automated recruitment system resulted in capture of 138 true cases of drug-induced rare events, improving recall from 43% to 93%. Our focused electronic alert system maintained 2-year enrollment, including across an EHR migration from a bespoke system to Epic. Real-time monitoring of EHR notes may accelerate research for certain conditions less amenable to conventional study recruitment paradigms.

Keywords: data mining; electronic health records; natural language processing; patient selection; precision medicine; rare diseases.

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Figures

Figure 1.
Figure 1.
Alert system workflow. Email accrual is representative of notes created by members in the educational tool Learning Portfolio. A positive text match automated both a REDCap (Research Electronic Data Capture) entry via a REDCap application programming interface call and a secure email to study personnel with a customized view of pertinent information from Learning Portfolio. Via an emailed link, the study researchers, each with their own institutional review board approval, could review the case in Learning Portfolio and/or the electronic health record (EHR). Study investigators could then decide whether or not to approach the patient for enrollment and/or the primary care team with clinical decision support, including suggestion of a formal consult. Active study recruitment roles indicated by square boxes. API: application programming interface.
Figure 2.
Figure 2.
Total monthly enrollment by recruitment method. Cumulative email alerts shown for nonunique medical record numbers (n = 1490). Conventional recruitment methods included canvassing of key medical units, engaging departments of subspecialists, and a custom-built electronic dashboard of all electrocardiograms with prolonged corrected QT intervals. No email alerts triggered for 5 months of the study due to a combination of electronic health record (EHR) transition and fewer unenrolled patients with a history of disease.
Figure 3.
Figure 3.
Patients with adverse drug events enrolled by email text match. Total email alerts shown for unique medical record numbers (n = 1173). Contextual mentions classified by REDCap users and confirmed by manual chart review. Major exclusion criteria consisted of significant comorbidities (eg, history of bone marrow transplant) and age less than 18 years. Both studies required documentation (eg, rhythm strip) and event onset ≤4 weeks of initiation of drug. Had event indicated patients with a confirmed history of the adverse drug events drug-induced torsades de pointes or Stevens-Johnson syndrome and toxic epidermal necrolysis. Other rash indicates text mentions included as part of a differential diagnosis (n = 171) or negated (n = 253). CLQT: congenital long QT syndrome.
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