Competencies for the use of medical data
Data literacy for clinical research and health care
Hannover Medical School (MHH)
Advanced information infrastructure concepts to share medical data along with advancements in data analytics such as machine learning and data mining offer new opportunities and challenges for the reuse of medical data in clinical research. Against this background, combined with the wish for a learning health care system that incorporates translation of results from research back to medical practice, the module provides medical students with the necessary data literacy for their scientific development.
Target Group: The module is aimed at medical students between their third and fifth study year and is designed as a blended learning course. Three classroom lectures (at the beginning, in the middle and at the end of the course) are complemented by five collaborative e-learning lectures. To make the above-mentioned topics more interesting and attractive, the basic material, which has been developed by experts in medicine and medical informatics, is presented in different forms, e.g. videos, scientific papers, manuals or games. Furthermore, the applied learning tasks will make the learners a part of a community of inquiry.
Learning Objectives: The structure of the module is based on the five main topics of data literacy, defining the competencies which are required for successful and secure working with data. Starting with realistic examples of the use of medical data, a conceptual framework with relevant terms and concepts will be defined and discussed. In the part data collection, different sources of medical data such as registries, wearables, omics databases, etc. are identified. The students measure their own activity data with sensors and gather more experience concerning potential problems while measuring and documenting. Requirements (e.g. FAIR data principles, guidelines for the management of research data) and principles of medical data management during a data lifecycle are introduced. Furthermore, the students receive information about the functionalities of a data warehouse and the tasks of a data integration center and they learn approaches for data modeling. The section about data evaluation delivers insight into different computerized data analyses as well as visualization and classification approaches combined with practical exercises. Data appliance needs to consider some administrative tasks, e.g. students prepare an application for ethical approval (including a clinical study protocol and a process description). In this context, aspects of data privacy and protection will also be discussed.
Embedding: The elective module “Reliable Use of Data in Medical Research” comprising a learning unit of 28 hours is integrated into the model study program HannibaL (“Hannover integrated adaptive practice-related learning concept“). HannibaL was established at the Hannover Medical School in 2005. Its main characteristics are early patient contacts for practical skills and patient communication as well as the imparting of profound knowledge with a focus on scientific work and research.