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Increasing data science adoption by healthcare professionals provides an opportunity to bridge the demand for analytics usage in clinical decision-making. One approach involves teaching data science concepts through an online education platform. We designed a think-aloud experiment in which we observed seven internal medicine residents complete an online interactive lesson using the Integrated Statistics Learning Environment (ISLE). The hour-long lesson focused on the predictive modeling lifecycle. Participants’ verbalizations and structured interactions with the platform were recorded. We observed a surprising misunderstanding of the lesson’s three main data science concepts. We also noticed a willingness to spend more time on familiar concepts and a comparative disengagement when seeing new topics. Many are amenable to the idea of learning data science through an online forum. However, it is important to first consult prospective clinician users prior to designing lessons to ensure that lessons align with clinical backgrounds, expectations and just-in-time learning needs.

Think-aloud interviews have been a valuable but underused tool in statistics education research. Think-alouds, in which students narrate their reasoning in real time while solving problems, differ in important ways from other types of cognitive interviews and related education research methods. Beyond the uses already found in the statistics literature -- mostly validating the wording of statistical concept inventory questions and studying student misconceptions -- we suggest other possible use cases for think-alouds and summarize best-practice guidelines for designing think-aloud interview studies. Using examples from our own experiences studying the local student body for our introductory statistics courses, we illustrate how research goals should inform study-design decisions and what kinds of insights think-alouds can provide. We hope that our overview of think-alouds encourages more statistics educators and researchers to begin using this method., 29 pages, 2 tables, 2 figures

An ever increasing number of people are affected by chronic kidney disease (CKD). A better understanding of the progression ofCKD and its complications is needed to address what is becoming a major burden for health-care systems worldwide. Utilizing a rich data set consisting of the Electronic Health Records (EHRs) of more than 33,000 patients from a leading community nephrology practice in Western Pennsylvania, we applied group-based trajectory modeling (GBTM) in order to detect patient risk groups and uncover typical progressions of CKD and related comorbidities and complications. We have found distinct risk groups with differing trajectories and are able to classify new patients into these groups with high accuracy (up to ≈ 90%). Our results suggest that multitrajectory modeling via GBTM can shed light on the developmental course ofCKD and the interactions between related complications.