Design of a FAIR digital data health infrastructure in Africa for COVID‐19 reporting and research

Reisen, Mirjam van; Oladipo, Francisca; Stokmans, M.J.W.; Mpezamihgo, Mouhamed; Folorunso, Sakinat Oluwabukonla; Schultes, Erik; Basajja, Mariam; Aktau, Aliya; Amare, Samson Yohannes; Taye, Getu Tadele; Jati, Putu Hadi Purnama; Chindoza, Kudakwashe; Wirtz, Morgane; Ghardallou, M. El; Stam, Gertjan van; Ayele, Wondimu; Nalugala, Reginald; Abdullahi, Ibrahim; Osigwe, Obinna; Graybeal, John; Medhanyie, Araya Abrha; Kawu, Abdullahi Abubakar; Liu, Fenghong; Wolstencroft, Katy; Flikkenschild, Erik; Lin, Yi Chun; Stocker, Joëlle; Musen, Mark A.

doi:10.1002/ggn2.10050

Cited by 31 publications

(32 citation statements)

References 41 publications

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“…All of these sub-principles were mentioned in National Health Data converging idea about the need for the inclusion of the equivalent of FAIR Guidelines. This finding is consistent with research on FAIR Equivalency carried out in other African countries (Uganda, Kenya, Zimbabwe, Nigeria) and Indonesia [3,[15][16][17][18][19].…”

Section: Findabilitysupporting

confidence: 91%

“…With the advent of the Internet, artificial intelligence, genomics and other aspects of the Fourth Industrial Revolution, the health sector has shown remarkable progress in the use of digital technologies and in generating big data [1]. To enhance data use, it is recommended that databases be federated [2] and that the data generated from different federated systems be stored in a defined data repository, so that stakeholders can make use of the data according to clearly defined data sharing and access policies [3].…”

Section: Introductionmentioning

confidence: 99%

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FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

et al. 2022

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This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’ (under well-defined conditions), ‘Interoperable’ and ‘Reusable’ (FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and (re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.

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Section: Findabilitysupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

et al. 2022

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“…Moreover, it is observed that such data does not benefit data analytics at the point of care. The burden of data entry is, therefore, of no benefit to the health facility that produces the data [13].…”

Section: Figure 1 Ugandan Healthcare System [6]mentioning

confidence: 99%

Information Streams in Health Facilities: The Case of Uganda

Basajja

Nambobi

2022

Data Intelligence

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With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation and reuse of data, which can be beneficial in managing future outbreaks. However, there is a general lack of knowledge about the actual flow of information in health facilities, which is also the case in Uganda. In Uganda, where this case study was conducted, there is no comprehensive knowledge about what type of data is collected or how it is collected along the journey of a patient through a health facility. This study investigates information flows of clinical patient data in health facilities in Uganda. The study found that almost all health facilities in Uganda store patient information in paper files on shelves. Hospitals in Uganda are provided with paper tools, such as reporting forms, registers and manuals, in which district data is collected as aggregate data and submitted in the form of digital reports to the Ministry of Health Resource Center. These reporting forms are not digitised and, thus, not machine- actionable. Hence, it is not easy for health facilities, researchers, and others to find and access patient and research data. It is also not easy to reuse and connect this data with other digital health data worldwide, leading to the incorrect conclusion that there is less health data in Uganda. The a FAIR architecture has the potential to solve such problems and facilitate the transition from paper to digital records in the Uganda health system.

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“…It quickly became clear that data interoperability and access represented major barriers for understanding the mechanisms of viral spread and hindered the development of new vaccines and therapeutics. The GO-FAIR Virus Outbreak Data Network ( 27 ) created a computer-readable, semantic representation of the World Health Organization (WHO) case report form and made data from Covid-19 patients available through a network of FDPs, with the first FDPs in Europe and Africa ( 28 ). This made it possible to analyze patient characteristics and the efficacy of medicines without the need to centralize data, whilst maintaining privacy for patients.…”

Section: Example Initiatives and Implementation: Personal Health Train And Vantage6mentioning

confidence: 99%

Federated Networks for Distributed Analysis of Health Data

Hallock

Marshall

Hoen

et al. 2021

Front. Public Health

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Access to health data, important for population health planning, basic and clinical research and health industry utilization, remains problematic. Legislation intended to improve access to personal data across national borders has proven to be a double-edged sword, where complexity and implications from misinterpretations have paradoxically resulted in data becoming more siloed. As a result, the potential for development of health specific AI and clinical decision support tools built on real-world data have yet to be fully realized. In this perspective, we propose federated networks as a solution to enable access to diverse data sets and tackle known and emerging health problems. The perspective draws on experience from the World Economic Forum Breaking Barriers to Health Data project, the Personal Health Train and Vantage6 infrastructures, and industry insights. We first define the concept of federated networks in a healthcare context, present the value they can bring to multiple stakeholders, and discuss their establishment, operation and implementation. Challenges of federated networks in healthcare are highlighted, as well as the resulting need for and value of an independent orchestrator for their safe, sustainable and scalable implementation.

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Design of a FAIR digital data health infrastructure in Africa for COVID‐19 reporting and research

Cited by 31 publications

References 41 publications

FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

Information Streams in Health Facilities: The Case of Uganda

Federated Networks for Distributed Analysis of Health Data

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