Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany.
Institute of Virology, Hannover Medical School, Hannover, Germany.
Stud Health Technol Inform. 2022 Aug 31;298:56-60. doi: 10.3233/SHTI220907.
Progress in methods for biomedical research, such as multi-omics analyses and in data-driven healthcare, such as new procedures in diagnostic imaging lead, along with the rising availability of additional data sources, to a growing demand for experts in biomedical data analysis. Addressing this need in academic education and the challenge of interdisciplinary teamwork in the biomedical domain, the authors have designed and implemented a new Master's program for biomedical data science that accepts students with different educational backgrounds, medical doctors, veterinarians and students with a Bachelor's degree in life sciences, and incorporates blended learning. This paper aims to present the didactic concept of the program, report on feedback from the students and first evaluation results, and discuss the benefits and drawbacks of this approach. Our results show that the program is well-accepted by the students, who stress the benefits of working in interprofessional teams, the option for part-time study along with their jobs with flexible learning opportunities, and of good and intensive interaction offers with their peers and teachers. Readjustments are necessary to improve tutoring support and alignment of content among distinct modules and to decrease workload peaks. While our evaluation results are still preliminary, we are convinced that our approach of mostly online offers, yet with a strong focus on teamwork, practical exercises guided by experts and communication skills, may serve to educate students to be well-prepared for their future tasks and operations in biomedical data science, in research, clinical care and industry.
生物医学研究方法的进展,如多组学分析,以及数据驱动的医疗保健,如诊断成像中的新程序,加上更多数据源的可用性增加,导致对生物医学数据分析专家的需求不断增长。为了满足学术教育的这一需求和生物医学领域跨学科团队合作的挑战,作者设计并实施了一个新的生物医学数据科学硕士课程,该课程接受具有不同教育背景的学生,包括医生、兽医和生命科学专业的本科生,并结合了混合学习。本文旨在介绍该课程的教学理念,报告学生的反馈意见和初步评估结果,并讨论这种方法的优缺点。我们的结果表明,该课程受到学生的广泛欢迎,学生们强调了在跨专业团队中工作的好处、在工作的同时兼职学习的选择、灵活的学习机会以及与同行和教师的良好和深入的互动机会。需要进行调整以改善辅导支持和不同模块之间的内容一致性,并减少工作量高峰。虽然我们的评估结果仍初步,但我们相信,我们的主要在线课程提供方式,同时强调团队合作、专家指导的实践练习和沟通技巧,可能有助于教育学生为未来在生物医学数据科学、研究、临床护理和工业中的任务和运营做好充分准备。