Clinician burnout, negative patient experiences and poor health outcomes are all problems that generative artificial intelligence tools could mitigate. From automated documentation to clinical decision support, the technology has the potential to transform healthcare workflows and care delivery. Generative AI can handle rote tasks and give clinicians a more accessible look at patient data. However, the ability of an AI algorithm is dependent on the quality of the data itself.
At the AWS Summit in June in Washington, D.C., AI experts and healthcare leaders discussed the benefits, risks and practical applications of generative AI in healthcare. Dr. Naqi Khan, physician executive for generative artificial intelligence and machine learning healthcare industry solutions at Amazon Web Services, explored the importance of data quality as a foundation for healthcare AI implementations.
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AI Success Requires Healthcare to Be Data-Driven
“Data is the differentiator when it comes to AI and generative AI,” said Khan, who recommended that healthcare leaders spend more time thinking about their data.
The healthcare industry is experiencing annual data growth of 30 terabytes for electronic health records data, 100 terabytes for patient monitoring data, 2 petabytes for medical imaging data, 2 petabytes for omics data and 5 petabytes for digital pathology data, according to Khan. In total, hospitals generate 50 petabytes of data annually, according to the World Economic Forum. However, 97 percent of this data goes unused because it’s unstructured.
“Everyone wants to be data-driven, but only about a quarter of organizations feel they are being successful,” Khan said.
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