“Healthcare organizations have been attempting to leverage massive quantities of data to their advantage for years,” data analyst Kyle Rybarczyk wrote in a June 1 blog post. “The problem: The current algorithms are ineffective, the data is siloed, missing or inefficient, and there is significant bias.”
Mr. Rybarczyk suggests that systems build robust data teams by recruiting data analysts and scientists from the financial and business intelligence sectors.
He said with healthcare generating more data than any other industry — an estimated 30 percent of the world’s data — systems must utilize the cloud to “help securely collect, store and aggregate” that information and “leverage it to ultimately reduce errors and drive interoperability and provider collaboration” and enable “population health management and precision medicine.”
Mr. Rybarczyk also called natural language processing, pattern detection and deep learning a “business necessity.” He said AI and machine learning have been found to detect cardiac arrhythmias better than trained experts.
“This technology also allows for process automation of normal tasks such as updating patient records or medical billing with reduced error,” he wrote. “All these benefits increase efficiency and reduce workload on clinicians, which in turn reduces burnout.”
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