Wide cleft between theory and practice: medical students' perception of their education in patient and medication safety.

In medicine today, future doctors are expected to ensure patient safety. Yet medical students often feel uncertain if they can meet these high expectations. This study aims to quantify the perceptions of medical students regarding the actual quality of their education in the fields of patient safety and, in particular, medication safety. A questionnaire was designed and distributed to about 100 upper-level medical students. The students had to respond to 12 questions regarding the following categories: 1) familiarity with patient safety and/or medication safety; 2) personal experience in high-risk clinical situations; and 3) perceived relevance of knowledge in the area of patient and medication Safety for clinical practice. Of the respondents 42.1% and 36.8% had delved into the topic patient safety and medication safety, respectively. In clinical practice 88.2% of respondents had experienced a high-risk situation for patients. Regarding patient safety and medication safety, respectively, 82.9% and 85.3% of the respondents found these topics to be particularly relevant to their clinical practice. This study has shown that there is a measurable discrepancy between the students' perceived quality of their medical education and their feelings that they are well prepared to cope with severe clinical challenges.

sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis.

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR. sendigR reference manual: https://phuse-org.github.io/sendigR/. sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.