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Active Learning of the HL7 Medical Standard
- Source :
- Journal of Digital Imaging
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Health Level Seven (HL7®) is a standard for exchanging information between medical information systems. It is widely deployed and covers the exchange of information in several functional domains. It is very important and crucial to achieve interoperability in healthcare. HL7 competences are needed by all professionals touching information technology in healthcare. However, learning the standard has always been long and difficult due to its large breadth as well as to large and complex documentation. In this paper, we describe an innovative active learning approach based on solving problems from real clinical scenarios to learn the HL7 standard, quickly. We present the clinical scenarios used to achieve learning. For each scenario, we describe and discuss the learning objectives, clinical problem, clinical data, scaffolding introduction to the standard, software used, and the work required from the students. We present and discuss the results obtained by implementing the proposed approach during several semesters as part of a graduate course. Our proposed method has proven that HL7 can be learned quickly. We were successful in enabling students of different backgrounds to gain confidence and get familiar with a complex healthcare standard without the need for any software development skill.
- Subjects :
- Active learning
Computer science
Interoperability
Health informatics
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Software
Documentation
HL7 standard
Problem based
Health care
Learning
Electronic Health Records
Humans
Radiology, Nuclear Medicine and imaging
Health Level Seven
Radiological and Ultrasound Technology
business.industry
4. Education
Healthcare
Software development
Information technology
Data science
Computer Science Applications
Systems Integration
Medical informatics
business
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 1618727X and 08971889
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Journal of Digital Imaging
- Accession number :
- edsair.doi.dedup.....0206a47f2becbbcb31375c925906765f