Better Technology, Needs Better Tuning
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Better Technology, Needs Better Tuning

By Vasee Sivasegaran, Associate CIO, Penn Medicine

The advancements in AI haven’t reached the healthcare sector, at least not to its full potential. Recently, many healthcare organizations have started to focus on AI. Usually, the focus is more on tactical operational aspects, blocking and tackling healthcare issues, and even implementing electronic medical record.

"There is no hard and fast rule here. This is a brand new space, and every organization is trying to push hard and dig deeper to conquer the space"

Our company, for instance, chooses technology to stay ahead of the competition, but we are also very keen to use some of the new solutions that are available like AI chatbots and machine learning to solve real-world problems. We are one of the few organizations that are looking into this area.

Developing the Platform

Some of the large organizations are well-funded, they have the resources, and they have the data scientists and data analysts to build their machine learning models. They mainly build the environment gradually, host these engines, and put it into use to solve operational problems or local problems for those organizations. We have a hybrid environment in which we take the traditional approach of standing up with our infrastructure and virtual server, including automation engines, which we built from the scratch. Now we are definitely a lot more advanced when we measure ourselves compared to the industry peers.

Suggestions for Industry Peers and Decision-Makers

There is no hard and fast rule here. This is a brand new space, and every organization is trying to push hard and dig deeper to conquer the area. Some of the variable factors that are going to be used to make the right decisions are financial such as having the money to employ data engineers and data scientists and building their infrastructure and then spending the time and resources to develop the AI platform. That is going to force organizations to go and use what has already been built by large-scale organizations like Microsoft, Oracle, Amazon, and a few others. So it depends upon the organization, their resources, and the availability of funds.

Some Common Misconceptions in the AI Landscape

There is much hype in the market. There is a common misconception that AI is going to solve all the problems in the industry, which is not true. AI as any other technology requires tuning. However, the hurdles are being identified and also the trends are being interpreted carefully, so there is a much manual override to it. AI and machine learning is still a nascent domain. It is probably in the first and second generation and it may take these AI models a lot more time to have enough data point and accuracy. Maybe four or five generations from now these models and these engines will be a lot more user-friendly and a lot more intuitive, but that is not the case right now. AI is not the Holy Grail at present.

Future of AI in Healthcare

We know AI will play a vital role in the future of the industry. But it is too early to comment on how it is going to be. There is much hype at this point. We will not be able to come across any white papers or articles where healthcare organizations have successfully used AI to materially shape the corporations or solve a real-world clinical problem yet. The will is there and there is enough data which is being collected by healthcare organizations to drive solutions, but the AI engines lack maturity even in the modern day. Much research has to be put into this to sort out the hurdles and bring top-notch solutions, which will cater to the domain of health care.

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