OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

BACKGROUND

Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.

OBJECTIVE

This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.

METHODS

The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

RESULTS

A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.

CONCLUSIONS

This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/22505.

A smart hospital-driven approach to precision pharmacovigilance

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach

Introduction

Information technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These ‘intelligent’ technical paradigms provide a new digital ‘toolkit’ supporting drug safety and healthcare processes, including ‘active pharmacovigilance’. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation.

Objective

We dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm.

Results

To this end, we elaborate on the related ‘business processes’ (i.e. operational processes) and ‘user goals’ identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond.

Conclusions

We highlight the fact that beyond providing analytics and useful statistics to the end user, ‘actionability’ has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.

Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project

BACKGROUND

The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange.

METHODS

KONFIDO focused on the so-called "User Goals", while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework.

RESULTS

The main project outcomes are highlighted and the "Lessons Learned," the technical challenges and the EU context are detailed.

CONCLUSIONS

The main "Lessons Learned" are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.