Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin

In this paper, we discuss and explore the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work in conjunction with a 3D digital twin. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as important research challenges for AI systems and digital twins.Comment: Submitted to Workshop on AI for Digital Twins and Cyber-physical applications at IJCAI 2023, August 19--21, 2023, Macau, S.A.

Induced Technological Change in a Limited Foresight Optimization Model

The threat of global warming calls for a major transformation of the energy system the coming century. Modeling technological change is an important factor in energy systems modeling. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with limited foresight that includes learning-by-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific polices which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions in different sectors, primarily in the transport sector

Do electronic patient information systems improve efficiency and quality of care? An evaluation of utilisation of the Discovery HealthID application

Background. Electronic health records (EHRs) appear to offer a number of potential benefits, but practitioners are often hesitant to make the transition to using them.Objectives. To determine whether the use of one such system, designed and offered by a health insurer (HealthID; Discovery Health), makes a difference to the efficiency and quality of doctor-patient consultations.Methods. A descriptive study using mixed methods was designed. A qualitative phase of individual interviews of purposefully sampled respondents was followed by a quantitative survey of a random sample of general practitioners and specialists who were registered users of the system.Results. In the qualitative findings, 18 respondents reported their perceptions of the ease of use of the application, their motivation for using it, its functions and benefits, the impact on efficiency and quality of care, and the challenges they experienced. In addition, they reported on the details of the challenges of using the system, and made suggestions for improvements, particularly with regard to the need for training and IT support. The quantitative results from the majority of 93 respondents confirmed that while the use of the app improved patient care through positive effects on specific functions such as access to accurate patient records and easier Chronic Illness Benefit applications, they felt that it had an equivocal impact in other areas, such as maintaining patient confidentiality and enhancing teamwork and efficiency. The financial incentives offered by Discovery Health, as well as possibly the training and support provided, appear to be more influential for high-frequency than for low-frequency users. The majority said that it did not help with referrals or script writing, or with access to International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes.Conclusions. EHR systems like Discovery Health’s HealthID could improve the efficiency of medical consultations by increasing access to stored health information without requiring data entry by clinicians, and thereby have the potential to indirectly improve the quality of care, provided that certain conditions are met.

OASIS II

Information and communication technology for health (ICT4H) can provide solutions for health practices in low resource settings leading to improved health and health care. The presentation provides perspectives on health information systems that can be integrated into African healthcare systems. Possible designs and methods are surveyed in relation to the Open Architecture, Standards and Information Systems (OASIS) framework and with reference to examples such as the WHO electronic Recording and Reporting Portal

Establishing a health informatics research lab in South Africa

Aim/Purpose: The aim of this project was to explore models for stimulating health informatics innovation and capacity development in South Africa. Background: There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery Methodology: We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Capacity Development and Innovation Ecosystem (HeAL-CDIE) designed to be a long-lasting and potentially reproducible output of the project. Contribution: We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i) a successful partnership model; (ii) a sustainable HeAL-CDIE; (iii) research papers; (iv) a world-class software product and several technology demonstrators ; and (iv) highly trained staff. Findings: Our main findings are that: (i) it is possible to create a local ecosystem for capacity development and innovation that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings. Recommendations: for Practitioners:Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes. Recommendation for Researchers: Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions. Impact on Society This research demonstrates a model that can deliver valuable software products for public health. Future Research: It would be useful to implement the model in other settings and investigate whether the model is more generally usefu

BioAfrica's HIV-1 Proteomics Resource: Combining protein data with bioinformatics tools

Most Internet online resources for investigating HIV biology contain either bioinformatics tools, protein information or sequence data. The objective of this study was to develop a comprehensive online proteomics resource that integrates bioinformatics with the latest information on HIV-1 protein structure, gene expression, post-transcriptional/post-translational modification, functional activity, and protein-macromolecule interactions. The BioAfrica HIV-1 Proteomics Resource is a website that contains detailed information about the HIV-1 proteome and protease cleavage sites, as well as data-mining tools that can be used to manipulate and query protein sequence data, a BLAST tool for initiating structural analyses of HIV-1 proteins, and a proteomics tools directory. The Proteome section contains extensive data on each of 19 HIV-1 proteins, including their functional properties, a sample analysis of HIV-1(HXB2), structural models and links to other online resources. The HIV-1 Protease Cleavage Sites section provides information on the position, subtype variation and genetic evolution of Gag, Gag-Pol and Nef cleavage sites. The HIV-1 Protein Data-mining Tool includes a set of 27 group M (subtypes A through K) reference sequences that can be used to assess the influence of genetic variation on immunological and functional domains of the protein. The BLAST Structure Tool identifies proteins with similar, experimentally determined topologies, and the Tools Directory provides a categorized list of websites and relevant software programs. This combined database and software repository is designed to facilitate the capture, retrieval and analysis of HIV-1 protein data, and to convert it into clinically useful information relating to the pathogenesis, transmission and therapeutic response of different HIV-1 variants. The HIV-1 Proteomics Resource is readily accessible through the BioAfrica website at

Endogenous Technological Change in Energy Systems Models: Synthesis of Experience with ERIS, MARKAL, and MESSAGE

Technological change is widely recognised as a key factor in economic progress, as it enhances the productivity of factor inputs. In recent years also the notion has developed that targeted technological development is a main means to reconcile economic ambitions with ecological considerations. This raises the issue that assessments of future trajectories of for example en-ergy systems should take into account context-specific technological progress. Rather than tak-ing characteristics of existing and emerging technologies as a given, their development should be a function of dedicated Research, Development and Demonstration (RD&D) and market de-ployment under varying external conditions. Endogenous technological learning has recently shown to be a very promising new feature in energy system models. A learning, or experience curve, describes the specific (investment) cost as a function of the cumulative capacity for a given technology. It reflects the fact that tech-nologies may experience declining costs as a result of its increasing adoption into the society due to the accumulation of knowledge through, among others, processes of learning-by-doing and learning-by-using. This report synthesises the results and findings from experiments with endogenous technologi-cal learning, as reported separately within the EU TEEM project. These experiments have been carried out by three TEEM partners using three models: ERIS (PSI), MARKAL (ECN and PSI), and MESSAGE (IIASA). The main objectives of this synthesis are: to derive common methodo-logical insights; to indicate and assess benefits of the new feature, but also its limitations and issues to solve; and to recommend further research to solve the main issues. This synthesis shows that all model applications are examples of successful first experiments to incorporate the learning-by-doing concept in energy system models. Incorporating the learning-by-doing concept makes an important difference. The experiments demonstrate and quantify the benefits of investing early in emerging technologies that are not competitive at the moment of their deployment. They also show that the long-term impact of policy instruments, such as CO2 taxes or emission limits and RD&D instruments, on technological development can be assessed adequately with models including technology learning. Adopting the concept of endogenous learning, several types of RD&D interventions can be addressed that aim at accelerating the market penetration of new technologies. The directions into which such interventions might lead have been illustrated in some of the experiments. However, quantitative relationships between R&D policy and learning data parameters are still unknow

Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa

Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors