OR.NET: a service-oriented architecture for safe and dynamic medical device interoperability.
Andreas BestingMalte SchmitzBjörn AndersenMax RockstrohStefan FrankeStefan SchlichtingFrank GolatowskiDirk TimmermannPublished in: Biomedizinische Technik. Biomedical engineering (2018)
Modern surgical departments are characterized by a high degree of automation supporting complex procedures. It recently became apparent that integrated operating rooms can improve the quality of care, simplify clinical workflows, and mitigate equipment-related incidents and human errors. Particularly using computer assistance based on data from integrated surgical devices is a promising opportunity. However, the lack of manufacturer-independent interoperability often prevents the deployment of collaborative assistive systems. The German flagship project OR.NET has therefore developed, implemented, validated, and standardized concepts for open medical device interoperability. This paper describes the universal OR.NET interoperability concept enabling a safe and dynamic manufacturer-independent interconnection of point-of-care (PoC) medical devices in the operating room and the whole clinic. It is based on a protocol specifically addressing the requirements of device-to-device communication, yet also provides solutions for connecting the clinical information technology (IT) infrastructure. We present the concept of a service-oriented medical device architecture (SOMDA) as well as an introduction to the technical specification implementing the SOMDA paradigm, currently being standardized within the IEEE 11073 service-oriented device connectivity (SDC) series. In addition, the Session concept is introduced as a key enabler for safe device interconnection in highly dynamic ensembles of networked medical devices; and finally, some security aspects of a SOMDA are discussed.
Keyphrases
- healthcare
- electronic health record
- quality improvement
- mental health
- randomized controlled trial
- patient safety
- endothelial cells
- emergency department
- machine learning
- primary care
- computed tomography
- public health
- multiple sclerosis
- pain management
- white matter
- minimally invasive
- working memory
- deep learning
- global health
- induced pluripotent stem cells
- health information
- big data
- adverse drug
- diffusion weighted imaging