Generative artificial intelligence is helping healthcare organizations increase productivity and advance clinical care, but it’s only as reliable as the data it is trained on. This has made healthcare data governance increasingly important.
A new survey from Amazon Web Services and Harvard Business Review reveals that chief data officers across multiple industries are concerned that their data assets are not up to the task. Fifty-two percent of respondents rated their organization’s readiness for generative AI as “inadequate,” according to a press release on the survey, and 39% cited data issues as the top challenge preventing them from effectively scaling AI.
However, the healthcare industry’s regulatory framework makes it uniquely prepared to leverage AI, says Thomas Godden, enterprise strategist with AWS. “Data governance is fundamentally the bedrock for ensuring patient safety,” says Godden, who previously served as CIO for Foundation Medicine.
“Healthcare organizations have already needed to clean and control their data. So, in a lot of ways, they’re better positioned for AI than other industries,” he says.
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Why Does AI Make Data Governance in Healthcare More Complex?
Data governance refers to the policies and standards that ensure data is high-quality, easily accessible, secure and trustworthy. Tracking and maintaining the massive amounts of data that AI-backed technologies require has made data governance in healthcare more challenging in several key ways.
Common challenges include:
Keeping Data Sets Updated
Healthcare data is constantly evolving, and AI training models must reflect those changes to ensure accuracy. “If you’re not updating the models daily or weekly, you’re going to miss things that are happening in the world and with your patients,” Godden says.
Removing Biases
Data may contain biases related to factors such as gender, race and socioeconomic status. Susan Laine, chief field technologist at Quest Software, says data teams must have a system in place to identify and remove those biases from the training data. “Data problems will only be amplified when fed into AI for things like diagnoses and treatment recommendations,” she warns.
Identifying Responsibility and Accountability
If an AI-driven decision leads to an adverse outcome, is the developer, the user or the system itself responsible? “If you don’t have transparency around what’s happening with your data, then you won’t know the true source of the problem or where a fix is needed,” Laine says.
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