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.
Managing clinical risk is an integral part of any organisation providing clinical services.
The Cube is an established methodology for analysing socio-technical systems which
offers a framework for managing such risks [
Organisations already collect and study (human-oriented) risk information in the form of risk registers, annual safety reports and incident reports. They are also increasingly overloaded with digital operational data that could be used to inform risk management as well as operational management. Typically these two management cycles operate independently, upon siloed data, and focus on qualitative analysis (risk) or quantitative analysis (operations in digitally transformed organisations like modern clinical practice) respectively. These differences mean that it is hard to find machine-readable rather than human-focused risk data and even harder to fuse it with structured operational data or predictive analytics from data science projects, despite the utility that these sources may have for implementing a more dynamic and effective risk management system. These disconnected activities also make it difficult to efficiently analyze and build a machine readable evidence base that is suitable for modern automated or semi-automated analytical approaches. Even though the Cube methodology supports such activities, its deployment has been hampered by the lack of an integrated platform facilitating its use.
This paper describes the Access Risk Knowledge (ARK) platform for Cube-based risk analysis and evidence base collection. ARK uses Semantic Web technologies to model, integrate, and classify risk and socio-technical system analysis information from both qualitative and quantitative data sources into a unified risk graph. This graph is based on three new ontologies: a risk terminology expressed as a SKOS model, a Cube analysis vocabulary, and an ARK project vocabulary. The ARK platform and ontologies have been built to study this research question: ”to what extent can a Linked Data approach support semantic interlinking and interoperability between quantitative and qualitative risk data, extraction and representation of risk domain knowledge, and enhance dynamic and integrated analysis of risk by supporting the Cube methodology?”.
ARK is built as a native web application in Node.js with a React user interface driven from an Apache Jena triplestore based on our developed ontologies. The ontologies are based on two years of iterative development between mixed teams of organisational psychologists and knowledge engineers. By using these technologies, the ARK platform provides a way to standardize social-technical analysis outputs into a self-describing semantic graph that can include Cube structured questionnaire-based analysis, multidimensional synthesis, or risk mitigation project-oriented views of the organisation under study. It also allows for semantic interlinking and interoperability of diverse data sources such as operational data or analytics. It facilitates making qualitative data machine readable by richly representing it in the Cube ontology along with metadata, and it enables human-oriented textual responses to be annotated with terms from the risk terminology. This paper describes the following contributions:
The Cube Ontology, ARK projects vocabulary, and ARK risk terminology. They enable formal and structured socio-technical analysis of systems and SKOS representation of the background knowledge on risk management (including provenance of term definitions).
The ARK application. This provides a user interface for curation of structured Cube responses (including interlinking supporting evidence) and navigation around the multi-dimensional Cube system analysis or risk mitigation project analysis. Case study-based evaluation. We present preliminary results of a case study where the ARK Platform has been used for Cube socio-technical analysis in a real-world problem.
The remainder of this paper is organised as follows. A motivating use case is presented in Section 2. Section 3 describes the development of the ARK Platform. Section 4 presents a case study and preliminary results. Section 5 discusses related work. Section 6 concludes the paper.
This section describes background on the Cube methodology and the high level use case and requirements for risk management tools.
The Cube methodology has been developed over twenty years by organisational
psychologists working on safety systems for the health, aviation and finance domains. It
supports progressively building a multi-dimensional analysis of complex systems [
Prior to our research, the Cube methodology was manually deployed by a risk management domain expert using a cumbersome spreadsheet as an aid. Limitations of this approach included insufficient navigation flexibility as analysis often requires non-linear completion of questionnaires, a lack of embedded rules or relations between question answers, inability to easily generate different interactive views of the data for analysis, no ability to support for semi-automated analysis or validation of answers, no interlinking to existing datasets, analytics or evidence, limited ability to reuse analysis results from project to project, no sharing or team working support and no embedded reference glossary of terms for safety analysts. A fair characterisation of this system would be to say it was only suitable for highly trained domain experts and provided no automation or data synthesis supports.
Risk management is one of the most important procedures performed within clinical
environments, with strong impacts on the quality of service [
In this section, we present the Access Risk Knowledge (ARK) platform and ontologies that support socio-technical analysis of clinical risk management using the Cube methodology. The ARK Platform relies on Semantic Web technologies and W3C standards to integrate interoperable qualitative clinical risk management data with quantitative operational data and analytics. The platform is composed of the Cube ontology2 and the Project vocabulary3 which is used to support data capture and analysis through the Cube methodology, a safety management taxonomy4 described using W3C’s standard Simple Knowledge Organisation System (SKOS)5, and a prototype web-based application, which allows domain experts to apply social-technical analysis through the Cube methodology. Figure 1 presents an overview of the the ARK platform. 2Available at https://w3id.org/ARK/Cube. 3Available at https://w3id.org/ARK/Projects. 4Available at https://w3id.org/ARK/Terminology. 5https://www.w3.org/2004/02/skos/.
The Cube ontology and the Project vocabulary were developed to enable data capture and
analysis through the Cube methodology. The ontologies are based on two years of
iterative development between mixed teams of organisational psychologists and knowledge
engineers. The ontologies were implemented using the Web Ontology Language (OWL)
specification6 in prote´ge´7. Moreover, the ontologies were designed following Gruber’s
[
The Cube ontology is used in the overall architecture of the ARK Platform in two ways. First, it serves as a repository of the standard Cube questionnaire. Moreover, when there is need for customization, depending on the domain or organization where the Cube methodology is being applied, different Cube questionnaires can be instantiated. Second, 6https://www.w3.org/TR/owl2-primer/ 7https://protege.stanford.edu/ the Cube ontology is used to represent responses to the Cube questionnaire, enabling an evidence base that supports governance within the organization. On the other hand, the Project vocabulary allows organizations to structure risk analysis through the Cube methodology by way of projects and sub-projects.
The safety management taxonomy was defined using SKOS, a W3C Recommendation
designed to support the use of knowledge organization systems. In the ARK Platform,
the developed taxonomy allows users to annotate risk management data described using
the Cube ontology with qualitative operational risk data. Concepts in the taxonomy have
been defined by domain experts and have at least a description and, when available, their
source. Since these domain experts do not have expertise in Semantic Web technologies,
the taxonomy was defined in a collaborative environment which is later transformed.
This transformation was performed using R2RML8 mappings created with the aid of an
editor [
The ARK application was built using Node.js and React, and offers three main interfaces where users can interact with the Cube methodology. The first component allows organizations to define projects. The second component (Figure 3) guides users into using the Cube methodology by allowing questionnaires to be answered. In order to provide experts with a more comprehensive and granular analysis, this component allows users to interlink answers with supporting evidence. Finally, this component also provides the functionality to classify answers with risk-related concepts using the developed safety management taxonomy.
The third component, called Cube summary, focuses on the summarization and visualisation of answers at the end of each stage. The Cube summary component provides 8A a W3C Recommendation allowing one to define customized mappings to convert non-RDF resources to RDF. https://www.w3.org/TR/r2rml/ functionality that enables viewing answers similarly to a Rubik’s Cube, where the visualisation of any stage, aspect and dimension can be configured by the user. Figure 4 presents the Cube summary component.
The ARK platform has been developed with the aim of facilitating risk analysis of complex systems through the Cube methodology. To evaluate the platform, a preliminary case study has been conducted, in which, two participants were asked to use the platform to address a clinical risk management problem and report their experiences through separate interviews.
Participants were recruited and introduced to the case study by one of the research team members. To assess the different levels of expertise, one of the participants was an expert in risk management, while the other was a non-expert user.
The task participants were asked to perform was related to a clinical risk management
case which had previously been described in an MSc thesis [
After task completion, participants were interviewed in relation to their reflection over its use, integrity/consistency of functions in the platform, and efficacy. Other features evaluated include:
Descriptions of concepts defined in the safety management taxonomy concepts within the project analysis interface.
The ability to navigate questions and answers in the ARK platform using different dimensions and aspects.
The ability to hide features/aspects.
The ability to summarize user’s answers.
The ability to provide evidences to qualitative answers.
Participants found training to be necessary when being introduced to the platform. Nonetheless, participants evaluated its learning curve as fairly easy. Participants described the platform as complex in relation to the amount of information needed in each question, and the number of questions to be answered. Participants also described that the large number of questions within the Cube questionnaire contributed to a comprehensive understanding of the complex system presented in the experiment task. In this context, even though the summary component was found useful, participants described having to switch back and forth through different dimensions and aspects continuously. Furthermore, participants also described the use of platform as time-consuming (estimated at 3 to 4 hours to complete). In terms of integrity, the platform was verified as consistent, in which, the relation between different functionalities is clear and easy to navigate. Other features, such as the possibility of interlinking data sources as evidences, taxonomy definitions being available through the interface, the ability to hide features/aspects, have also been considered helpful.
Generally, in terms of efficacy, participants found the platform useful and they confirmed that it gives a comprehensive overview of the analysed use case. The reaction over Semantic Web technologies-driven features to support the Cube methodology has had a positive outcome. Even though our preliminary results are promising, a comprehensive evaluation using other instruments, such as standard usability tests and a larger number of participants, is required to validate our findings.
The use of ontologies for the analysis of organisational change and risk management
has been proposed in different domains. For instance, the authors in [
There are also ontology development efforts towards the semantic modelling of
systematic data collection and analysis tools such as questionnaires. The semantic
modelling of traditional questionnaires [
In this paper, we introduce the ARK platform and ontologies for socio-technical analysis applied to clinical risk management. The proposed platform provides an extensible interface, that is customizable in terms of its representation, and interoperable through its ontology-driven back-end for representing social-technical analysis through the Cube methodology. Our preliminary evaluation of the system shows that our proposed platform is capable of representing core concepts, relationships, customisation, and visualisation of the Cube methodology. A limitation of our evaluation is the number of participants which will be targeted in future experiments.
Future work includes enhancing the platform with additional functionalities including rich domain knowledge related to organisational risk, knowledge extraction from the supporting documents, and improvements described by participants of our preliminary study. Finally, future work will also be focused on evaluating the platform with a wide community of users through standard tests.
This research was conducted with the financial support of Enterprise Ireland under Grant Agreement No. CF 2018-2012 at the ADAPT SFI Research Centre. The ADAPT SFI Centre for Digital Media Technology is funded by 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. The research team would like to thank participants of the case study for their help with the ARK platform evaluation.