Affiliated organisation : African Health Business
Site of publication : www.ahb.co.ke
Type of publication : Webinar report
Date of publication : May 2021
AI in health in Africa
Despite the potential of AI, there are several challenges which hinder adoption in Africa.
- How to collect, clean and model the data you can trust and write algorithms that can trend themselves and make high predictions is a challenge, especially in healthcare. Africa’s healthcare tends to be inconsistent, incomplete, and in other times complex. It becomes labor intensive to extract meaningful medical information therefore causing physicians burnout and a lot of cases of errors.
- The data issue is not an African problem particularly in the areas of health. The healthcare value chain is overly complex and with many interconnected different specialties. This brings up the issue of data governance and management of the whole healthcare data framework. Africa lacks the right security mechanisms that comply with the already existing regulations. There is also inability to build right frameworks and models which allow users to open doors into available data sets without creating a room for data breach. There is need to consider how we structure the data, how we exchange the data on technology platforms and how that data gets analyzed.
- There is limited AI knowledge and awareness in on the continent.
Opportunities for AI in the African healthcare space
- AI can make treatment more accessible and affordable through ensuring reliable health systems. This can be in form of modernizing the care infrastructure whereby customer engagement is increased to a better level, for example through using Chatbot.
- There is an opportunity to build models and algorithms which reduce things like cyber threats and increase security assurance and improve the protection patient data.
- AI can assist in surveillance and self-management at home. This will help patient with self-trials and basic treatment and ultimately improve efficiency, effectiveness, and cost reduction.
This brings up the issue of data governance and management of the whole healthcare data framework. Africa lacks the right security mechanisms that comply with the already existing regulations. There is also inability to build right frameworks and models which allow users to open doors into available data sets without creating a room for data breach
- Improving and accelerating diagnosis is another opportunity. AI can enable the usage of data to build models, learn from that data and identify hidden patterns or trends which can accelerate the rate at which healthcare facilities providing diagnosis and assist in precision and accuracy in terms of clinical decision-making. Examples are the models which can predict reoccurrence of cancer. These models can be structured in such a way that they follow specific protocols and procedures to diagnose specific conditions. This can be one way of standardizing healthcare systems.
- Stakeholders who are involved in the policy within the healthcare industry can have more data driven decision making processes powered by AI or machine learning algorithms and this can help in managing population health by the anticipation on predictive analysis and data.
- AI can be used to analyze people’s behavior patterns and match medical related products with people’s interests.
AI and the Covid-19 pandemic
The COVID-19 pandemic has forced healthcare sector across the continent to react abruptly. This has brought an increased need for solutions to diagnose and monitor COVID-19. Adopting new and alternative technologies should become an essential part of reducing resource limitations and decreasing the spread of existing and new virus strains. AI can, and should, be used to detect, monitor, prevent, inform, and respond to COVID-19 pandemic.
Essential building blocks for a sustainable implementation of AI in Africa’s healthcare space
To leverage the opportunities for AI in healthcare in Africa, there is need to address the main building blocks that are essential to delivering a sustainable AI solutions.
- A proper digital infrastructure to store data and develop a strong data culture within health facilities that value data collection, understanding and makes tools and resources accessible to clinicians to capture and report quality data.
- Africa also needs to consider adoption of local solutions by comprehending and finding suitable solutions to promote self-reliance and assisting in cultivating the local ecosystem.
The COVID-19 pandemic has forced healthcare sector across the continent to react abruptly. This has brought an increased need for solutions to diagnose and monitor COVID-19
- There is a need for more targeted funding for the AI health start-ups in Africa that links entrepreneurs with corresponding financiers and reduces the risk for private investors.
- To harness AI in an ethical, inclusive, and non-biased way, institutions responsible for managing data sets need have the right mechanisms and the right technology to differentiate the roles of every actor within the organization and determine what level of depth to reach when it comes to accessibility of data. They also need to have an audit trail to show who accessed what, at what time and for what purpose.
- There is no AI without the right data source and experts need to get it right when curating data. Data needs to be clean and structured to allow learning.
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