Publication details

Interactive presentation of evaluation data in training against medical errors



Type Conference abstract
Description Medical error is recognised to be a significant cause of patient harms in clinical practice, and improved education provisions have been identified as a key tool in reducing their frequency and severity. The EC Erasmus+ funded Training Against Medical Error (TAME) project has sought to utilise the sophisticated pedagogy of interactive online virtual patient cases as the basis for developing training aimed at providing undergraduate medical students with a greater and awareness of the causes of medical error. Six virtual patient cases in Paediatrics, designed to be used in a Problem-Based Learning style group setting, have been implemented at six partner institutions (Karaganda State Medical University, Astana Medical University, Zaporozhye State Medical University, Bukovinian State Medical University, Hanoi Medical University, Hue University of Medicine and Pharmacy) in Kazakhstan, Ukraine and Vietnam. These have been extensively evaluated with a focus on capturing the experience of key stakeholders (learners, tutors, tutor trainers, case writers, course teams, project consortium and funding body) to identify and assess its impact and effectiveness. The evaluation uses mixed-methods, with quantitative methods used to gather feedback from the large learner population, and qualitative methods to gather more in-depth opinions from smaller tutor and partner groups. A key challenge for the project has been how best to present the considerable amount of evaluation data collected. The increasing availability of extensible tools such as the R Project for Statistical Computing have provided frameworks which can be used to display data in ways that is both innovative and effective, and which was not previously possible. To that end, the TAME project has developed a series of interactive web applications that allow the evaluation data to be explored and interpreted as interactive charts and data tables. The R environment and packages such as the Shiny framework has been used to make the data accessible and browseable. Data visualisations have been constructed that permit the complex data sets to be viewed and patterns to be identified, informing the conclusions and findings that can be drawn from the data. This presentation will demonstrate these data applications, and describe the approaches used to create the resources. It will consider ways in which this approach to visualising and exploring data can be extended and applied to future work.