Collaborative Semantic Annotation Tooling (CoAT) to Improve Efficiency and Plug-and-Play Semantic Interoperability in the Secondary Use of Medical Data: Concept, Implementation, and First Cross-Institutional Experiences

Author:

Wiktorin Thomas12,Grigutsch Daniel1,Erdfelder Felix123,Heidel Andrew J.4,Bloos Frank5,Ammon Danny4ORCID,Löbe Matthias6ORCID,Zenker Sven123ORCID

Affiliation:

1. Staff Unit for Medical & Scientific Technology Development & Coordination (MWTek), Commercial Directorate, University Hospital Bonn, 53127 Bonn, Germany

2. Applied Medical Informatics (AMI) Lab, Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, 53127 Bonn, Germany

3. Applied Mathematical Physiology (AMP) Lab, Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, 53127 Bonn, Germany

4. Data Integration Center, Jena University Hospital, 07747 Jena, Germany

5. Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, 07747 Jena, Germany

6. Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107 Leipzig, Germany

Abstract

The cross-institutional secondary use of medical data benefits from structured semantic annotation, which ideally enables the matching and merging of semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambiguities resulting from the non-uniqueness of potential mappings to various levels of ontological hierarchies and relational and representational systems. The divergent resolution of such ambiguities can inhibit joint cross-institutional data usage based on semantic annotation since data items with related content from different sites will not be identifiable based on their respective annotations if different choices were made without further steps such as ontological inference, which is still an active area of research. We hypothesize that a collaborative approach to the semantic annotation of medical data can contribute to more resource-efficient and high-quality annotation by utilizing prior annotational choices of others to inform the annotation process, thus both speeding up the annotation itself and fostering a consensus approach to resolving annotational ambiguities by enabling annotators to discover and follow pre-existing annotational choices. Therefore, we performed a requirements analysis for such a collaborative approach, defined an annotation workflow based on the requirement analysis results, and implemented this workflow in a prototypical Collaborative Annotation Tool (CoAT). We then evaluated its usability and present first inter-institutional experiences with this novel approach to promote practically relevant interoperability driven by use of standardized ontologies. In both single-site usability evaluation and the first inter-institutional application, the CoAT showed potential to improve both annotation efficiency and quality by seamlessly integrating collaboratively generated annotation information into the annotation workflow, warranting further development and evaluation of the proposed innovative approach.

Funder

German Federal Ministry of Education and Research

Deutsche Forschungsgemeinschaft

Publisher

MDPI AG

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