As healthcare professionals are facing massive pressure not only to ensure the quality of care, but also to come up with new solutions, cures and treatments, they are becoming increasingly dependent on advanced technologies like artificial intelligence (AI) and machine learning (ML).
But it is hardly a smooth partnership. The issues of skills shortages at the entry-level and of “messy data” in leveraging patient records at the high end are merely book-ends for a range of challenges that span these fields.
Last week’s annual Amazon Web Services Re:Invent conference, one of the largest cloud-focused events in the world, saw the launch or demonstration of a range of new cloud-based tools that are ideal for health research and treatment. ML, defined as computer algorithms that improve automatically through experience, was at the heart of these.
The tools raised two key questions in terms of global and local relevance, namely how messy data is addressed, and how relevant these are to South Africa.
We asked a man at the heart of AWS’s health initiatives, Shez Partovi, AWS director of worldwide business development for healthcare, life sciences, and genomics. It all starts with ML, he says.
“In South Africa, we have seen how providing access to advanced technologies such as ML is vital to stopping the spread of COVID-19 and helping individuals quickly find medical help when they fall ill. GovChat, South Africa’s largest citizen engagement platform, launched a COVID-19 chatbot in less than two weeks using Amazon Lex, an AI service for building conversational interfaces into any application using voice and text.
“The chatbot provides health advice and recommendations on whether to get a test for COVID-19, information on the nearest COVID-19 testing facility, the ability to receive test results, and the option for citizens to report COVID-19 symptoms for themselves, their family, or household members.”
ML in particular is being roped in globally to address the massive volumes of data being gathered from a variety of unrelated sources, he says.
“ML has the potential to serve as an assistive tool for healthcare professionals, providing the support they need to process and analyse the increasing amount of data generated by doctors, hospitals, researchers, and organisations, including structured data like Electronic Health Record forms, as well as unstructured data, such as emails, text documents, and even voice notes.
“ML is being used in a variety of tasks such as analysing medical images to advancing precision medicine. Tools that leverage natural language processing, pattern recognition, and risk identification are also fuelling new models for predictive, preventive, and population health and have the potential to help providers identify gaps in care and improve the health of individuals and communities.”
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