5 key challenges will be addressed during the workshop.
Challenge 1: Vaccination data managment for Low and Middle Income Countries and people on the move, prompted by the Global Society of Humanitarian Paediatrics, Médecins Sans Frontières and the Geneva University Hospital.
Medical data management across borders in LMICs and in the field is manual and nondigital, increasing the socioeconomic gap between third world countries and rest of the world.
Pain points:
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No current solution for access and safe transfer of medical data across borders.
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No shared record between agencies for common good.
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Lack of data sharing across borders, servers and agencies for people on the move.
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Lack of digitization of medical records.
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Lack of standardization, and incompatible data records.
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Lack of trust among actors, users and data providers.
Challenge 2: Data analytic tools for insights on correlations between climate change and perinatal health, prompted by Terre des Hommes.
Policy makers lack essential information and tools to understand the correlation between climate change and the health of mothers and newborns, hindering the ability to make informed decisions to protect vulnerable populations.
Pain points:
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Insufficient research on the impact of climate change on nutrition, vector spread, and air quality among mothers and newborns.
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Limited access to local data for use in predictive data analysis.
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Lack of insight into the correlation between climate change and perinatal health.
Challenge 3: Management of malaria and the impact of interventions, prompted by Swiss Tropical and Public Health Institute and Dorian Job/Yenni Febrina.
Utilizing malaria knowledge and epidemiological data assessment is crucial for devising tailored control strategies that are effective both regionally and globally.
Pain points:
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Late diagnosis of malaria - due to factors such as limited medical knowledge & limited access to medical centres - leads to a high mortality rate in the affected communities.
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Gap in ensuring that extensive malaria data constantly amassed, both at the individual and community levels, is used effectively for influencing malaria control initiatives.
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Absence of a standard definition of essential data and a global dataset for malaria-related information.
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Lack of real-time assessments and evidence-based interventions derived from model simulations of malaria control dynamics.
Challenge 4: Occupational safety of health care professionals from radiation exposure, prompted by Atta Bhatti and supported by CERN.
As the utilization of radiation-based diagnostic tools and treatments becomes more widespread, addressing and mitigating radiation exposure among medical professionals is critical.
Pain points:
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Ionizing radiation, a common diagnostic & treatment tool in clinical practice, is responsible for the greatest dose of radiation to medical professionals.
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Radiation exposure to health care professionals is not detected in real time.
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Need for material research and modern designs to precisely detect and protect from radiation exposure.
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Gap in the education on radiation exposure training to clinical teams.
Challenge 5: Multimodal Health Data Integration as a transformative approach for global smart healthcare, prompted by EATRIS.
Multimodal health data integration emerges as a transformative approach in planning a global smart healthcare, enabling comprehensive analysis and understanding of individual and population health and facilitating personalized and community-oriented health interventions. Whether people are healthy or not is determined by their individual circumstances and precise ecosystem conditions. Factors such as where we live, our environmental conditions, the unique genetic and biological characteristics, our income and education level, and our relationships with the society in our local ecosystem all have considerable impacts on health and also on our perception of health.
Pain points:
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A large, and often poorly understood, number of factors combine to affect the health of individuals and communities.
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The integrative analysis of data sets from various sources for a precise understanding of the health status of individuals or communities can be improved. These datasets are often:
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Multimodal by nature as generated by different communities.
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Non-compliant with FAIR (Findable, Accessible, Interoperable, Reusable) principles.
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