How India’s blood system is transforming with Artificial Intelligence

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The Indian healthcare system is undergoing a transformation in digital health as technologies, such as efficiency, scalability and, in some cases, disruptive systems, are being hampered.

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10/17/2019 How India’s blood system is transforming with Artificial Intelligence and big data https://medium.com/venkat34.k/how-indias-blood-system-is-transforming-with-artificial-intelligence-and-big-data-6d247efd94ea 1/3 How India’s blood system is transforming with Articial Intelligence and big data venkat k Oct 17 · 3 min read The Indian healthcare system is undergoing a transformation in digital health as technologies such as efficiency scalability and in some cases disruptive systems are being hampered. While the private sector is leading the transition with innovative interventions the Government of India has played an active role in laying the foundational layers for the development of this digital ecosystem. Together they have made it possible to achieve a new status quo where emerging technologies are changing the way care is delivered data is used for decision-making and processes are improved through automation.

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10/17/2019 How India’s blood system is transforming with Artificial Intelligence and big data https://medium.com/venkat34.k/how-indias-blood-system-is-transforming-with-artificial-intelligence-and-big-data-6d247efd94ea 2/3 One area where both fields are of great interest is the restoration of India’s fragmented and chaotic blood system. The Indian Blood Scenario faces the challenges of acute scarcity increasing waste the transmission of infections and reliance on old practices and the growing technological disruptions serve as a one-stop-shop for ensuring safe adequate and stable blood for the country and are catalysts in the process. Low hanging fruit and priority areas It is necessary to narrow the gap between demand and supply to repair the blood system. In 2016 –17 the total blood collection in India was approximately 11094145 units compared to 26500000 units or 62.3 donations per 1000 eligible people. Meeting this shortfall of about 15 million units is a huge task and requires focusing on creating new and repeat donors. Donor recruitment and management whether machine learning ML and big data have diverse applications. On the donor promotion side Facebook is working with its AI- enabled tool to naturally use language processing and ML to differentiate blood donation posts and reach potential donors for recruitment. After that it can automatically send notifications to nearby donors whenever a request is created. Since its launch in 2017 it has already collected over 35 million donors worldwide. Some startups in the US are going a step further with big data and accurate ML to contact more and more people who donate using highly personalized messages. However the most important use of AI-tools is in the management of an efficient inventory of blood through supply assessment. Short shelf-life and lack of blood component are the main reasons behind the astonishing 10 –11 waste rate for blood collected in India. Combining existing recruitment solutions with AI-powered CRM can significantly reduce waste collection and promote optimization of inventory by constantly monitoring blood supply levels and sending automated notifications when a particular component or blood type is below the minimum threshold. These tools can be further trained in many use cases and consequently functional improvements for example storing universal blood types in the face of an impending natural disaster. Shifting the focus from vein to vein safety Emerging technologies beyond blood donors to ensure clinical efficacy and safety are still in the experimental stages but have enormous potential. In the US Premier Inc. the Healthcare Improvement Alliance used big data analytics for 645 facilities to determine

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10/17/2019 How India’s blood system is transforming with Artificial Intelligence and big data https://medium.com/venkat34.k/how-indias-blood-system-is-transforming-with-artificial-intelligence-and-big-data-6d247efd94ea 3/3 patterns of blood use and reduce the chances of continuing use of patient outcomes. For example they observed that orthopedic surgeons used excessive blood and worked with facilities to achieve a 75 reduction in cases requiring blood transfusions. AI-enabled clinical support systems therefore can help clinicians become more cost-effective. Inadequate testing for contamination faulty cross-matching and congestive infections are major challenges in venous-to-vein safe blood functioning. Extensive and transparent digital footprint can help the clinician reduce errors and allow for pattern recognition and tracking. Combined with this automation helps reduce the amount of manual touch. Systems can be trained to make decisions about whether to specify samples for centrifugation or to distribute them directly into whole blood. Articial Intelligence Aiservices Aisolutions Machine Learning Mlservices About Help Legal

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