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Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety.
We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance).
The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error.
An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.
Transcriptional regulation of the T box family of
aminoacyl–tRNA synthetase and amino acid biosynthesis
genes in Gram-positive bacteria is mediated by a conserved
transcription antitermination system, in which readthrough
of a termination site in the leader region of the mRNA
is directed by a specific interaction with the cognate
uncharged tRNA. The specificity of this interaction is
determined in part by pairing of the anticodon of the tRNA
with a “specifier sequence” in the leader,
a codon representing the appropriate amino acid, as well
as by pairing of the acceptor end of the tRNA with an unpaired
region of the antiterminator. Previous studies have indicated
that although these interactions are necessary for antitermination,
they are unlikely to be sufficient. In the current study,
the effect of multiple mutations in tRNATyr
on readthrough of the tyrS leader region terminator,
independent of other tRNA functions, was assessed using
a system for in vivo expression of pools of tRNA variants;
this system may be generally useful for in vivo expression
of RNAs with defined end points. Although alterations in
helical regions of tRNATyr that did not perturb
base pairing were generally permitted, substitutions affecting
conserved features of tRNAs were not. The long variable
arm of tRNATyr could be replaced by either a
short variable arm or a long insertion of a stable stem-loop
structure. These results indicate that the tRNA–leader
RNA interaction is highly constrained, and is likely to
involve recognition of the overall tertiary structure of
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