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Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Informatics is a relatively new interdisciplinary field which is not very well understood outside of specific disciplinary communities. With a review of the history of informatics and a discussion of the various branches of informatics related to health-care practice, the paper aims to provide an overview designed to enhance the understanding of an information professional interested in this field.

The paper is designed to provide a basic introduction to the topic of informatics for information professionals unfamiliar with the field. Using a combination of historical and current sources, the role of informatics in the health professions is explored through its history and development.

The emergence of informatics as a discipline is a relatively recent phenomenon. Informatics is neither information technology (IT) nor information science but shares many common interests, concerns and techniques with these other two fields. The role of the informaticist is to transform data to knowledge and information. Consequently, while the outcomes may be different, there are many commonalities in informatics with the work information professionals perform.

Most introductions to informatics assume the reader is either an IT professional or a clinical practitioner in one of the health science fields. This paper takes a unique approach by positioning the discussion of the history and application of informatics in the health sciences from the perspective of the information professional.

The purpose of this paper is to identify the benefits of an approach in which document management systems (DMSs) are based on a formal and explicit document model, primarily in terms of facilitating domain-specific customization.

Within this paper, a generic document model is proposed. The model consists of two layers. A general purpose layer, which represents common features of the documents, and a domain-specific layer, modeling properties particular to application domain. The general purpose layer is based on ISO 82045, providing high degree of interoperability with other systems developed with respect to this set of standard.

Splitting document model into the layers enables DMSs to be tailored for each particular domain of application, depending on the general purpose layer. The existence of domain-specific layer allows documents to be interpreted differently in different domains of application.

In order to enable customization of DMS for a particular domain, the implementation of domain-specific document layer is required. Also, the proposed model does not explicitly deal with document dynamics.

The proposed document ontology is general enough to provide the representation of documents not depending on a specific scope of application, yet flexible enough to enable extensions through which domain-specific document features can be expressed. The separation of document model enables development of core DMS offering services relying explicitly on the general purpose layer on one hand, as well as domain-specific customization of DMS on the other.

The purpose of this paper is to identify the gap that currently exists between enterprise and consumer‐focused mashup tools in terms of personalized, trusted collaboration. The authors describe how Sqwelch, a semantically enabled mashup maker, addresses this gap during the design of mashups and in their execution.

Sqwelch enables the composition of mashups based on the concept of trust explicitly specified by users through a visual interface. Taxonomies are used to enable lightweight mediation of payloads delivered through a publish/subscribe mechanism.

The authors demonstrate the use of Sqwelch as a proof of concept in the remote delivery of healthcare, and how Sqwelch has been used to address areas of trust and collaboration in the delivery of telehealth services.

Integrating trust and collaboration across the boundaries of enterprises is required where sensitive data are transferred across those boundaries in the expectation of the delivery of a service. Across these boundaries, the authors find variations in users' skills, their expectations, and their responsibilities. The prototype described here enables users to discover, compose, share and collaborate in the day‐to‐day use of systems that match personalized requirements.

Purpose – The purpose of this chapter is to illustrate the importance of using open source technologies and common standards for interoperability when implementing eHealth systems, and to illustrate this through case studies, where possible.

Design/methodology/approach – The sources used to inform this chapter draw from the implementation and evaluation of the eHealth Program in the context of the Millennium Villages Project (MVP).

Findings – As the eHealth Team was tasked to deploy an eHealth architecture, the Millennium Villages Global-Network (MVG-Net), across all 14 of the MVP sites in sub-Saharan Africa, the team not only recognized the need for standards and uniformity but also realized that context would be an important factor. Therefore, the team decided to utilize open source solutions.

Practical implications – The MVP implementation of MVG-Net provides a model for those looking to implement informatics solutions across disciplines and countries. Furthermore, there are valuable lessons learned that the eHealth community can benefit from.

Originality/value – By sharing lessons learned and developing an accessible, open source eHealth platform, we believe that we can more efficiently and rapidly achieve the health-related and collaborative Millennium Development Goals.

The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph.

The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation.

The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.”

This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.

Recent studies indicated that the level of adoption of health data standards in healthcare organisations remains frustratingly low worldwide although health data standards have been perceived to be an essential tool for interoperability barriers within health information systems. The relevant literature still lacks significant studies concerning the issues of the adoption process of health data standards in healthcare organisations, and in particular those in developing nation. In addressing this gap in knowledge, the purpose of this paper is to investigate the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia, and to develop a technology-organisation-environment list that contains the critical factors influencing their adoption.

A multiple-case study methodology was conducted in Saudi Arabia and different data collection methods were used included semi-structured interviews with different decision makers at various levels and departments of the subject organisations, and documents analysis to identify critical factors to the adoption decision of health data standards.

The findings demonstrated a list of key factors from different aspects impacting the adoption decision of health data standards in the subject organisations. The technological factors are complexity and compatibility of health data standards, IT infrastructure, switching costs, market uncertainties, systems integration and enhancing the use of advanced systems. The main organisational factors are the lack of adequate policies and procedures and information management plan, resistance to change, data analysis and accreditation. The core environmental factors are the lack of national regulator and data exchange plan, national healthcare system and the shortage of professionals.

The results from the qualitative data were difficult to generalise to other populations. For example, the structure of the health sector varies from country to country as each health sector has its own characteristics that affect and are affected by national circumstances. In order to provide a more grounded theory resulting from a qualitative study, further examination by conducting quantitative studies is required. In addition, the TOE approach does not take into account the sociotechnical issues and further research is required in this area.

The investigation into the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia has led to the development of a technology-organisation-environment list that contains the critical factors influencing their adoption. The research outcome has addressed the gap in knowledge of the adoption of health data standards in healthcare organisations. It also provides the decision maker, and in particular those in developing nations, with better understanding of the adoption process of those standards to better judge and to develop suitable strategy of adoption interventions.

Although recent studies indicated that the level of adoption of health data standards in healthcare organisations remains frustratingly low, the prior studies related to health data standards missed out on the exploration of the adoption decision of different types of health data standards in healthcare organisations and the critical factors influencing their adoption. Research on health data standards adoption based out of a developing country such as Saudi Arabia can also potentially provide several new insights on standards practices.