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Current landscape and future trajectory for AI in healthcare

por Lauren Dubinsky, Senior Reporter | February 04, 2019
Artificial Intelligence Health IT
From the January/February 2019 issue of HealthCare Business News magazine

It automatically surfaces groups of similar procedures and generates clinical pathways that help to achieve the best patient outcome at the lowest cost.

Gurjeet Singh
“It discovers the sequences that are similar amongst patients to group them together,” said Gurjeet Singh, CEO and co-founder of Ayasdi. “Then having them grouped together, it predicts the best sequence per patient, and having those predictions in hand, it justifies these to a clinician.”

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Four months after Flagler Hospital in St. Augustine, Florida implemented the CVM application, it was able to reduce the average cost of treating patients with pneumonia by 30 percent, reduced admissions by seven times, and decreased the average length of stay by about 2.5 days.

“For this one protocol, Flagler officials have discovered that by using our software, it will benefit them by roughly a million dollars per year,” said Singh. “They expect to have 18 protocols in production over the next 12 months, and expect to save about $20 million over the next three years.”

What constitutes artificial intelligence?
“AI” has become a very general term in recent years, and Singh would go as far as to say that it doesn’t mean anything specific anymore. He jokes with his friends that anyone with a Microsoft Excel spreadsheet can claim themselves to be AI these days.

According to him, an AI system should have five important characteristics in order to be considered legitimate. Those include the ability to discover, predict, justify, act and learn.

First off, an AI system needs to be able to learn from large, complex data sets without any human interaction. That is a phenomenon known as unsupervised learning and it’s so important because the system must discover all of the patterns that exist in the data without a human having to ask a question.

The AI system should also be able to use the large data sets to predict what is likely to happen in the future with a high degree of accuracy. Clinicians are still the main clinical decision makers, but AI can provide them with information on future needs, costs, disease burdens and patient risks.

The most critical characteristic of an AI system is its ability to justify or explain its results. That includes every recommendation, prediction and segment of the anomaly.

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