AI and ML solutions are already being used by thousands of companies with the goal of improving the healthcare experience. For example, Babylon Health is changing the way we manage and better understand health. Founder, Ali Parsa developed the app in 2013 with a mission of providing accessible and affordable healthcare to every individual on earth. Babylon’s AI system has been designed to understand and recognise the way humans express their medical symptoms and it can interpret symptoms and medical questions through a chatbot interface and match them to the most appropriate service. It can recognise most healthcare issues seen in primary care and provide information on next steps to take.
The conversation around artificial intelligence (AI) and machine learning (ML) in healthcare continues to grow. Research in cutting-edge areas like machine learning continues to demonstrate that computers have the potential to predict outcomes and optimise clinical operations in a wide variety of settings.
Healthcare stands poised for a transformation driven by AI and ML, and fuelled by an abundance of data sources – electronic health records, claims data, genomic sequences, mobile devices, medical imaging, and even embedded sensor data.
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Building a foundation for AI and ML
Data is the fundamental raw material required to power AI and ML systems, and is an essential ingredient that enables healthcare organisations to increase efficiency, improve outcomes, and enhance quality of life for both patients and providers.
While the demands of treating patients and developing new therapies often relegate data collection and analysis to a back burner in healthcare, new tools enable developers to integrate ML and other capabilities easily into the routine process of developing and delivering treatments. Far from being an exclusive province of researchers and technology companies, AI and ML are now accessible to all.
As these use cases expand, success is dependent on several ingredients. First, such initiatives require large quantities of carefully curated, high-quality data, which may be hard to come by in healthcare where data is often complex and unstructured. High-quality data sets are required not only to operate AI and ML-driven systems, but even more importantly, to feed the training models upon which they are built.
Second, these systems need to be optimised for the compute-intensive jobs typically required by AI applications. And finally, IT resources supporting AI applications must comply with industry standards and regulations and adhere to the highest security and privacy standards to protect patient and other sensitive data.
One company that has successfully rooted itself in developing and curating its data is Touch Surgery The company is transforming professional healthcare training through the delivery of a unique platform that links mobile apps with powerful data back-end. Touch Surgery uses cognitive mapping techniques coupled with cutting edge AI and 3D rendering technology to codify surgical procedures. They have partnered with leaders in Virtual Reality and Augmented Reality to work toward a vision of advancing surgical care in the operating room. With over 1 million users, the firm are recording vast amounts of usage data to power their data analytics product, which in turn allows users to learn and practice over 50 surgical procedures, evaluate and measure progress, and connect with physicians across the world.
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A crucial technology that provides storage capacity, compute elasticity, security, and analytic capabilities needed to implement AI and ML – and drive innovation - is cloud computing. Cloud computing platforms make it easy to ingest and process data, whether structured, unstructured, or streaming and simplifies the process of building, training, and deploying machine learning-based models. Healthcare organisations that can use cloud computing to make themselves more efficient and effective will be the most successful in coming years, particularly as the industry shifts to value-based care.
For the National Health Service (NHS), AI and ML are having a huge impact on its ability to cut costs, while improving patient services. The NHS is the UK’s largest employer and health provider. NHS Business Services Authority (NHS BSA), a Special Health Authority and an Arm's Length Body of the Department of Health and Social Care, provides a range of critical central services to NHS organisations, NHS contractors, patients and the public. As such, the NHS BSA’s call centre staff handle around five million calls per year. The organisation decided to implement a cloud-based contact centre and deep learning chatbot service using Amazon Connect and Amazon Lex to help improve the user experience, reduce call centre load, increase efficiency and cut costs. By moving to the cloud, the NHS BSA has identified around $650,000 in cost savings per annum from a reduction in average call times alone.
Healthcare companies, whether established or new start-ups, are increasingly looking to AI and ML to drive innovation and transformation at their company and across the healthcare industry. These organisations share a common goal of reducing time to discovery and insight, improving care quality and enhancing the patient and provider experience. As the availability and volume of data sources continue to grow, the essential ingredients for AI and ML success will remain the same: high-quality data, cloud computing to remove undifferentiated heavy lifting, and ML services accessible to everyday developers. Once these foundational elements are established, AI and ML have the potential to power more efficient and effective care, enhanced decision making and the ability to drive greater value for patients and providers.
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Shez Partovi, M.D., Director of Global Business Development, Healthcare, Life Sciences and Agricultural Technology, AWS (opens in new tab)