Preparing healthcare facilities in sub-Saharan Africa for future outbreaks: insights from a multi-country digital self-assessment of COVID-19 preparedness.

Background: Despite previous experience with epidemics, African healthcare systems were inadequately prepared and substantially impacted by the coronavirus disease 2019 (COVID-19) pandemic. Limited information about the level of COVID-19 preparedness of healthcare facilities in Africa hampers policy decision-making to fight future outbreaks in the region, while maintaining essential healthcare services running.

Methods: Between May-November 2020, we performed a survey study with SafeCare4Covid - a free digital self-assessment application - to evaluate the COVID-19 preparedness of healthcare facilities in Africa following World Health Organization guidelines.

Information extraction from weakly structured radiological reports with natural language queries.

Objectives: Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models.

Methods: We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports.

Digital health systems strengthening in Africa for rapid response to COVID-19.

The COVID-19 pandemic has painfully exposed the constraints of fragile health systems in low- and middle-income countries, where global containment measures largely set by high-income countries resulted in disproportionate collateral damage. In Africa, a shift is urgently needed from emergency response to structural health systems strengthening efforts, which requires coordinated interventions to increase access, efficiency, quality, transparency, equity, and flexibility of health services. We postulate that rapid digitalization of health interventions is a key way forward to increase resilience of African health systems to epidemic challenges.

Implementing value-based healthcare using a digital health exchange platform to improve pregnancy and childbirth outcomes in urban and rural Kenya.

Maternal and neonatal mortality rates in many low- and middle-income countries (LMICs) are still far above the targets of the United Nations Sustainable Development Goal 3. Value-based healthcare (VBHC) has the potential to outperform traditional supply-driven approaches in changing this dismal situation, and significantly improve maternal, neonatal and child health (MNCH) outcomes. We developed a theory of change and used a cohort-based implementation approach to create short and long learning cycles along which different components of the VBHC framework were introduced and evaluated in Kenya.

Maintaining Continuity of Care for Expectant Mothers in Kenya During the COVID-19 Pandemic: A Study of MomCare.

In Kenya, early coronavirus disease (COVID-19) modeling studies predicted that disruptions in antenatal care and hospital services could increase indirect maternal and neonatal deaths and stillbirths. As the Kenyan government enforced lockdowns and a curfew, many mothers-to-be were unable to safely reach hospital facilities, especially at night. Fear of contracting COVID-19, increasing costs of accessing care, stigma, and falling incomes forced many expectant mothers to give birth at home.