Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Machine learning is a big idea, but hospitals need business plans first

Feb 8, 2018 | 4800 Views

Machine learning has the potential to transform healthcare through new knowledge discovery and improved productivity, but many health systems do not have a business plan in place to support advanced analytics beyond research and development.

As health systems consider how best to leverage machine learning and artificial intelligence, it will require a shift in IT strategy to focus on not just data, but managing the model itself. This means, among other things, defining the value of machine learning and providing a framework for evaluation and application.

Health systems need to keep things simple when moving into machine learning, said Elizabeth Clements, business architect at Geisinger Health.

"When working with new technology and developing a service from scratch, it can be easy to get lost and slow progress down with the complexity of a large use-case," Clements said. "If you keep your scope narrow and define near-term goals, you will find you are able to make more meaningful progress in a short amount of time."

And healthcare professionals dealing with machine learning must themselves learn how to partner with the business.

"Understanding the current and future state use of the machine learning solution is critical," Clements said. "If you don't consciously determine how much you want or need human intervention with the model, it will make your solution much more difficult to implement and gain buy-in."

Clements said a simple framework for thinking about machine learning in the context of the business is needed. That includes understanding its value and use-cases before embarking on this type of analytics advancement, as well as knowing the basic challenges and how to design a program that takes those into account.

"Machine learning is the next wave of advanced processing technology offering us new avenues for information discovery and productivity enhancement," she said. "It has the potential to transform how we conduct business; however, it will require a shift in our IT strategy."

It is not just about the data or the application, it is also about the model itself. IT leaders should consider how to complement their existing IT and data scientist teams with new skill sets and consider how machine learning can advance existing task execution, she added.

Clements will be speaking in the HIMSS18 session, "Managing Machine Learning: Insights and Strategy," at 11:30 a.m. March 7 in the Venetian, Palazzo D.

Source: Healthcare IT News