This paper describes the Access Risk Knowledge (ARK) platform and
ontologies for socio-technical risk analysis using the Cube methodology. Linked
Data is used in ARK to integrate qualitative clinical risk management data with
quantitative operational data and analytics. This required the development of a
novel clinical safety management taxonomy to annotate qualitative risk data and
make it more amenable to automated analysis. The platform is complemented
by other two ontologies that support structured data capture for the Cube sociotechnical analysis methodology developed by organisational psychologists at Trinity College Dublin. The ARK platform development and trials have shown the benefits of a Semantic Web approach to flexibly support data integration, making qualitative data machine readable and building dynamic, high-usability web applications
applied to clinical risk management. The main results so far are a self-annotated,
standards-based taxonomy for risk and safety management expressed in the W3C’s
standard Simple Knowledge Organisation System (SKOS) and a Cube data capture,
curation and analysis platform for clinical risk management domain experts. The
paper describes the ontologies and their development process, our initial clinical
safety management use case and lessons learned from the application of ARK to
real-world use cases. This work has shown the potential for using Linked Data to
integrate operational and safety data into a unified information space supporting
more continuous, adaptive and predictive clinical risk management.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Additional Information:
In conjunction with the10th Workshop on Ontologies and Data in Life Sciences (ODLS) and part of the Bolzano Summer of Knowledge (BoSK 2020)
Enterprise Ireland under Grant Agreement No. CF 2018-2012, Science Foundation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant # 13/RC/2106
ID Code:
25394
Deposited On:
18 Jan 2021 14:23 by
Vidatum Academic
. Last Modified 10 Feb 2021 13:29