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AI as cost saver in healthcare systems?


AI (Artificial Intelligence) is transforming the delivery of healthcare by enabling faster and more accurate point-of-care analytics, leading to faster diagnosis and treatment and improved care outcomes to patients. AI contributes to accelerating medical research, improving disease prevention, increasing the quality of patient care with more reliable, accurate and efficient diagnostics and associated treatments, optimizing the patient experience and providers and increase access to health services.

It also appears that the use of AI in healthcare technologies in remote monitoring, diagnostics and software as a medical solution has accelerated the shift from in-hospital to in-home care and decreased the burden on the healthcare system.

In addition to the benefits for patients and providers that AI creates, AI also has many benefits for businesses. While some healthcare AI solutions can be expensive to develop, their long-term profitability and cost reduction are unparalleled. In 2021 alone, AI would have saved $52 billion in healthcare sector globally. And this will continue to improve thanks to the development and marketing of more efficient and less expensive systems and technologies.

In attached article published in HBR, the authors examine the prospects for improving productivity in American healthcare organizations that are facing productivity decrease. The authors argue that AI can reverse the downward spiral of productivity, for example by automating administrative tasks or improving organizational efficiency or by controlling patient data faster and more reliably. The authors cite some necessary prerequisites to consider for the integration of AI technologies in healthcare organizations successfully.

This article once again adds to the increasingly broad perspectives offered by AI. AI is often perceived as the future solution that will be able to solve all our problems, from the simplest to the most complex, and even sometimes replace humans in certain elaborate tasks. However, behind these promises, hides a more complex reality.

First of all, there is not one AI but a multitude of AI technologies with levels of "intelligence" and therefore of very variable and more or less accomplished complexities.

On the other hand, some technologies such as Machine Learning or Deep Learning are quite recent and require development time to be entirely reliable and profitable. In addition, even the ultra complexity of some algorithms limits their use and distribution.

Finally, increasingly complex algorithms require more powerful and robust computing systems (e.g. quantum computers). But the technological progress in this field is enormous, with systems that are more and more efficient and less and less expensive.

In the field of health, there are also colossal investments from major healthcare companies as well as the development of many start-ups with very high financial valuation.

Link to the article

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