FAIR Equivalency with Regulatory Framework for Digital Health in Uganda

This study explores the possibility of opening a policy window for the adoption of the FAIR Guidelines—that data be Findable, Accessible, Interoperable, and Reusable(FAIR)—in Uganda’s e Health sector. Although the FAIR Guidelines were not mentioned in any of the policy documents relevant to Uganda’s e Health sector, the study found that 83% of the documents mentioned FAIR Equivalent efforts, such as the adoption of the National Identification Number(NIN) as a unique identifier in Uganda’s national Electronic Health Management Information System(e HMIS)(findability), the planned/ongoing integration of various information systems(interoperability), and the alignment of various projects with international best practices/standards(reusability). A FAIR Equivalency Score(FE-Score), devised in this study as an aggregate score of the mention of the equivalent of FAIR facets in the policy documents, showed that the documents at the core of Uganda’s digital health/e Health policy have the highest score of all the documents analysed, indicating that there is a degree of alignment between Uganda’s National e Health Vision and the FAIR Guidelines. Therefore, it can be concluded that favourable conditions exist for the adoption and implementation of the FAIR Guidelines in Uganda’s e Health sector. Hence, it is recommended that the FAIR community adopt a capacity building strategy through organisations with a worldwide mandate, such as the World Health Organization, to promote the adoption of the FAIR Guidelines as part of international best practices.

Curriculum Development for FAIR Data Stewardship

The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable(FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub(DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.

Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.

FAIR Machine Learning Model Pipeline Implementation of COVID-19 Data

Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines(that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and(re)usability of data, so that new and robust analytical tools, such as machine learning(ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.

Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic

Rapid and effective data sharing is necessary to control disease outbreaks,such as the current coronavirus pandemic.Despite the existence of data sharing agreements,data silos,lack of interoperable data infrastructures,and different institutional jurisdictions hinder data sharing and accessibility.To overcome these challenges,the Virus Outbreak Data Network(VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated,but,instead,algorithms can visit the data and query multiple datasets in an automated way.To make this possible,FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines(that data should be Findable,Accessible,Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box(Vi B).Vi B is a set of multiple FAIR-enabling and open-source services with a single goal:to support the gathering of World Health Organization(WHO)electronic case report forms(e CRFs)as FAIR data in a machine-actionable way,but without exposing or transferring the data outside the facility.Following the execution of a proof of concept,Vi B was deployed in Uganda and Leiden University.The proof of concept generated a first query which was implemented across two continents.A SWOT(strengths,weaknesses,opportunities and threats)analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.

FAIR Guidelines and Data Regulatory Framework for Digital Health in Nigeria

Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable(FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/e Health policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines(or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles(73%) and some of the remaining nine documents also contained some FAIR Equivalent principles(between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector.