Background A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Theranostic assays are based on single-gene testing, but the ability of next-generation sequencing (NGS) to interrogate numerous genetic alterations will progressively replace single-gene assays. Although NGS was evaluated to screen for theranostic mutations, its usefulness in clinical practice on large series of samples remains to be demonstrated. NGS performance was assessed following guidelines. TaqMan probes and NGS were compared for their ability to detect EGFR and KRAS mutations, and NGS mutation profiles were analyzed on a large series of non-small-cell lung cancers (n = 1343). The R correlation between expected and measured allelic ratio, using commercial samples, was >0.96. Mutation detection threshold was 2% for 10 ng of DNA input. κ Scores for TaqMan versus NGS were 0.99 (95% CI, 0.97-1.00) for EGFR and 0.98 (95% CI, 0.97-1.00) for KRAS after exclusion of rare EGFR (n = 40) and KRAS (n = 60) mutations. NGS identified 693 and 292 mutations in validated and potential oncogenic drivers, respectively. Significant associations were found between EGFR and PI3KCA or CTNNB1 and between KRAS and STK11. Potential oncogenic driver mutations or gene amplifications were more frequent in validated oncogenic driver nonmutated samples. This work is a proof of concept that targeted NGS is accessible in routine screening, including large screening, at reasonable cost. Clinical data should be collected and implemented in specific databases to make molecular data meaningful for direct patients' benefit.

Background The increasing complexity of data streams and computational processes in modern clinical health information systems makes reproducibility challenging. Clinical natural language processing (NLP) pipelines are routinely leveraged for the secondary use of data. Workflow management systems (WMS) have been widely used in bioinformatics to handle the reproducibility bottleneck. Objective To evaluate if WMS and other bioinformatics practices could impact the reproducibility of clinical NLP frameworks. Materials and Methods Based on the literature across multiple research fields (NLP, bioinformatics and clinical informatics) we selected articles which (1) review reproducibility practices and (2) highlight a set of rules or guidelines to ensure tool or pipeline reproducibility. We aggregate insight from the literature to define reproducibility recommendations. Finally, we assess the compliance of 7 NLP frameworks to the recommendations. Results We identified 40 reproducibility features from 8 selected articles. Frameworks based on WMS match more than 50% of features (26 features for LAPPS Grid, 22 features for OpenMinted) compared to 18 features for current clinical NLP framework (cTakes, CLAMP) and 17 features for GATE, ScispaCy, and Textflows. Discussion 34 recommendations are endorsed by at least 2 articles from our selection. Overall, 15 features were adopted by every NLP Framework. Nevertheless, frameworks based on WMS had a better compliance with the features. Conclusion NLP frameworks could benefit from lessons learned from the bioinformatics field (eg, public repositories of curated tools and workflows or use of containers for shareability) to enhance the reproducibility in a clinical setting.