Introduction:
The updated common rule, for human subjects research, requires that consents “begin with a ‘concise and focused’ presentation of the key information that will most likely help someone make a decision about whether to participate in a study” (Menikoff, Kaneshiro, Pritchard. The New England Journal of Medicine. 2017; 376(7): 613–615.). We utilized a community-engaged technology development approach to inform feature options within the REDCap software platform centered around collection and storage of electronic consent (eConsent) to address issues of transparency, clinical trial efficiency, and regulatory compliance for informed consent (Harris, et al. Journal of Biomedical Informatics 2009; 42(2): 377–381.). eConsent may also improve recruitment and retention in clinical research studies by addressing: (1) barriers for accessing rural populations by facilitating remote consent and (2) cultural and literacy barriers by including optional explanatory material (e.g., defining terms by hovering over them with the cursor) or the choice of displaying different videos/images based on participant’s race, ethnicity, or educational level (Phillippi, et al. Journal of Obstetric, Gynecologic, & Neonatal Nursing. 2018; 47(4): 529–534.).
Methods:
We developed and pilot tested our eConsent framework to provide a personalized consent experience whereby users are guided through a consent document that utilizes avatars, contextual glossary information supplements, and videos, to facilitate communication of information.
Results:
The eConsent framework includes a portfolio of eight features, reviewed by community stakeholders, and tested at two academic medical centers.
Conclusions:
Early adoption and utilization of this eConsent framework have demonstrated acceptability. Next steps will emphasize testing efficacy of features to improve participant engagement with the consent process.
Objective
Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation for gout research in the ED is the timely ability to identify acute gout patients. Our goal was to refine a multicriteria, electronic medical record alert for gout flares and to determine its diagnostic characteristics in the ED.
Methods
The gout flare alert used electronic medical record data from ED nursing notes and was triggered by the term ‘gout’ preceding past medical history in the chief complaint, the term ‘gout’ and a musculoskeletal problem in the chief complaint, or the term ‘gout’ in the problem list and a musculoskeletal chief complaint. We validated its diagnostic properties to assess presence/absence of gout through manual medical record review using adjudicated expert consensus as the gold standard.
Results
In January 2020, we analyzed 202 patient records from 2 university‐based EDs; from these records, 57 patients were identified by our gout flare alert, and 145 were identified by other means as potentially having an acute gout flare. The gout flare alert's positive predictive value was 47% (95% confidence interval [95% CI] 34–60%), negative predictive value was 94% (95% CI 90–98%), sensitivity was 75% (95% CI 61–89%), and specificity was 82% (95% CI 76–88%). The diagnostic properties were similar at both institutions.
Conclusion
Our multicomponent gout flare alert had reasonable sensitivity and specificity, albeit a modest positive predictive value. An electronic gout flare alert may help enable the conduct of gout research in the ED setting.
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