Why AI in health care and life sciences will require a new kind of collaboration

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There is general agreement that artificial intelligence (AI) will change our lives. What is less clear is exactly how and when that change will happen, and how it will look. Some of the speculation has been fairly wild. In life sciences and health care, however, the mists are now starting to clear, and more sensible proposals and actual applications are beginning to emerge. At June’s Life Science Forum Basel, we were diving into some of the thinking and practice around new and existing uses of AI in health care and life sciences.

AI in health care and public health

Health care and life sciences are strong candidates for early adoption of AI, not least because staff are already struggling to manage all the tasks necessary to care for patients effectively. There is, of course, fear of “being taken over by robots” among both health care professionals and patients, and this may delay acceptance and adoption of AI.

My sense, however, is that takeover is not really the issue. Instead, AI offers huge potential for partnership with medical and health care professionals.

Better treatment and more time with patients

AI could make care more effective and affordable by identifying the treatment and medication likely to be most efficacious for each patient, and support diagnosis.

#AI could make care more effective and affordable by identifying the #treatment and #medication likely to be most efficacious for each #patient, and support diagnosis. Read a blog by @olivierzaech Click To Tweet

It could also, by doing some of the tedious work of sifting through data and checking for patterns, provide more time for doctors and nurses to spend with patients. At present, doctors and other health care professionals are very much in a position of being information managers. They work through information provided by patients and their own knowledge, together with the vast store of information available electronically, and develop treatment proposals. This could instead be supported by algorithms.

Hyper-personalised health care

By automating tasks currently done by people, like this information sifting, health care can be made more efficient. But AI also has potential to automate tasks that we do not, or cannot, yet do because we do not have the capacity or the ability.

This might include, for example, sifting through phenotypes and identifying common characteristics in patients, matching these to drug characteristics. No doctor has the time for manual searches through thousands of health care records. AI can do this, however, and at speed. It therefore offers the potential for super- or hyper-personalised health care, tailored to fit each individual’s personal genetic makeup, behaviour and environment.

There are significant overlaps and interactions between these three. The use of sensors and biomarkers can help to disentangle some of these issues and support management of physical signs and symptoms. Combining thinking about providing better care with improving the patient experience also leads to thinking about how to provide a service to patients, rather than a series of products. This, in turn, changes the way that patients, health care providers and technology companies interact.

Addressing major public health-related issues

AI also offers the possibility of addressing major public health-related issues, such as tracking possible causes of sepsis by using data on patient movements and activity. The overarching link is that AI makes it possible to trawl through electronic health records for all kinds of data, and then use the insights to improve care both for individuals and populations. There is, therefore, plenty of potential for AI to improve health care provision, both in efficiency and effectiveness.

Less critically, but no less usefully, for pharma companies and health care providers is the potential to use AI to improve general health. Applications and platforms like dacadoo, for example, are helping people improve their lifestyle choices, and therefore their overall health and well-being. Marketing messages and channels can be aligned to individual preferences, making them more likely to be acted upon. There is no question that lifestyle-related diseases are the biggest killers of our time, and finding suitable ways to prevent these could greatly reduce the pressure on health systems worldwide.

Whether you are a data scientist, business sponsor or IT facilitator, the coming AI-driven transformation demands you keep pace with possibilities. Join fellow innovators at Analytics Experience, and learn from a mix of thought leadership driven discussions, concrete case studies and hands-on workshops.

 

Partnership, learning and progress

Perhaps the overarching themes to the Life Science Forum Basel, therefore, are partnership and learning.

Around the world, governments, researchers and health care providers are struggling to solve major issues, including preventing and managing lifestyle-related diseases, and reducing pressure on health care systems that arise with aging populations. The partnership between AI and health care and life sciences offers huge potential to help with these problems.

There is, we have come to understand, very little chance that these issues can be addressed without a radical change in thinking and health care delivery. More of the same is not an option. Machine learning, and even more advanced AI, however, seem likely to offer ways to enhance human capabilities and help find a way forward.

Read an inspiring article about an analytics startup focusing on health care optimization to helping medical providers improve patient care and financial performance.

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About Author

Olivier Zaech

Olivier Zäch is a Senior Account Advisor for the Life Science Industry. His special focus is on making technology useful for business. He helps life science companies to get tangible value out of their technology investments. He joined SAS in 1997 as a presales consultant and then held several positions including presales manager, head of client management Switzerland, business development manager, account manager Swiss SMB market, product manager internet technology, academic program manager and knowledge management officer. Before joining SAS Olivier worked at the Computing Center University of Zurich responsible for new user services such as World Wide Web, Internet Services, and statistical software and connected data bases. Olivier holds an MA degree from University of Zurich in German linguistics and literature, Computer Science and Philosophy; an MBA with a focus on international management consulting from University of Applied Sciences Northwestern Switzerland (FHNW); a Certificate in Advanced Studies (CAS) in the Management of Biotech, Medtech & Pharma Ventures from EPFL and UNIL.

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