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From improving patient outcomes to predicting heart attacks to streamlining cumbersome processes, the potential for artificial intelligence (AI) to transform healthcare is vast.

“If you have enough data, you can build a model,” David Parish, a healthcare AI leader for Google Cloud, said Tuesday at HIMSS18 in Las Vegas. “If the data is consistent and accurate enough, that model will work very well.”

And therein lies the catch: Just as the human brain can’t develop properly without adequate information, or inputs, the effectiveness of AI is dependent on the quality and quantity of data it is given.

As Healthcare IT News reports, Parish cited several obstacles to AI being able to access the data it needs for developing healthcare models, including data siloes, the need to de-identify data, and patient consent to allow providers to use their data.

“The model is not going to be smarter or more accurate than the examples I give it,” Parish said. “We all need to work together to minimize these roadblocks. The future is aggregating large amounts of healthcare data and using that data to make healthcare more effective. The question is how quickly we can bring this together to make the vision a reality.”

At another HIMSS18 panel session, speakers identified another data-related barrier to AI in healthcare: Lack of cooperation among various stakeholders.

“Data is such a natural resource and sharing data across multiple stakeholders requires trust,” said IBM Chief Health Officer Kyu Rhee. “It requires trust from the patient, requires trust from the provider and the payers, and all these different stakeholders that need to connect this data together to bring these insights out.”

“All enterprise systems want to own the data,” said Mayo Clinic Chief Information Officer Cris Ross. “It’s part of how they compete, it’s part of their raison d’être, they just do it that way. And the challenge is that transactional systems are wonderful for transactions but they’re not great for insights.”

 

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