Toward Engineering-Oriented Health Informatics Education

摘要

The need for informatics-trained professionals in health organizations has been ever-increasing. In addition, there is also a significant need to orchestrate data collection through informatics infrastructure, manage computing resources, store data, and operate network-enabled medical devices. In many medical fields, the overall need for supporting advanced medical devices with complex technological requirements is also exponentially increasing. Existing undergraduate major and minor programs in health informatics do not adequately equip students with skills to address these challenges, mostly due to limited STEM-focused courses. There is an increasing gap between overall skill-set of graduating health informatics professionals, and the job requirements. It takes a long time to train these individuals on the field, and equip them with necessary informatics skills to address these challenges. This skills gap has traditionally been addressed by employing graduates with computer science and engineering degrees. However, professionals with health informatics degrees, and computer science and engineering degrees each approach problems differently from their particular perspective(s), and resulting multidisciplinary teams can only provide short-term solutions. Thus, resulting data architectures and support infrastructures are both inefficient and incomplete in most cases. This article provides a basic analysis of twelve existing health informatics undergraduate major programs, and proposes a more STEM-focused, engineering-oriented degree options to complement these programs to help narrow the skills gap. In particular, we argue that at least some of the health informatics professionals on-the-field should be provided opportunities—during their undergraduate education—to (i) have hands-on coding skills at more advanced levels, (ii) be aware of how to orchestrate data and computational infrastructures, and (iii) know about contemporary tools and methods to analyze large datasets efficiently.