Researchers from Accenture wanted to see if opportunities lay within smaller data sets that go unused by organizations. For their study, the researchers focused on annotations added to medical charts by medical coders. With their tens of annotations on each of several thousand charts, the annotations are much smaller compared to data sets with a billion columns and rows.
In the experiment, the coders studied RNs who regularly used AI in their coding processes to link medical conditions with proper codes. The researchers wanted to find out how to transform the coders into AI trainers.
The 12-week study showed that close attention to human factors is needed to create and transform work processes through small data sets.
Three principles on human interaction with AI arose:
- “Balance machine learning with human domain expertise”
- “Focus on the quality of human input, not the quantity of machine output”
- “Recognize the social dynamics in play on teams working with small data”
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