Why does AI make healthcare data governance more complicated?
Data governance refers to policies and standards that ensure that data is of high quality, easy to access, secure and reliable. Tracking and maintaining the vast amount of data needed for AI-backed technologies has made healthcare data governance more challenging in several important ways.
Common challenges include:
Update the dataset
Healthcare data is constantly evolving, and AI training models need to reflect these changes to ensure accuracy. “If you don't update your model daily or weekly, you'll miss what's going on with the world and with the patients,” Godden says.
Removing bias
Data may include biases related to factors such as gender, race, and socioeconomic status. Susan Laine, chief field technician at Quest Software, says the data team must have a system in place to identify and remove those biases from the training data. “The data issues are only amplified when they are fed to AI with diagnostic and treatment recommendations,” she warns.
Identify responsibility and accountability
Is the developer, the user, or the system itself responsible if an AI-driven decision leads to unfavorable outcomes? “If you're not transparent about what's going on with your data, you don't know where the real source of the problem or where you need to fix it,” says Laine.
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What are the benefits of AI and data governance?
A robust data governance framework ensures that your AI model receives high-quality information and reduces risk. “Data governance is like having a glass box around AI,” says Laine. “It provides transparency to what we supply to the AI model and to those exposed to that data.”
At the same time, AI itself can improve data management. Can be used for policy enforcement and security pattern analysis. For example, AI can monitor and verify that sensitive patient data is accessed and properly processed.
Chatbots can improve the end-user experience by helping analysts sort and interpret information from large datasets more efficiently.
Additionally, machine learning tools can help healthcare organizations leverage larger data influx. AI automatically processes and learns from the data it collects. This allows the system to be continuously improved.
How can organizations set realistic expectations for AI data governance?
A common challenge is when leaders think that they need to activate all their organizational datasets before they can create value from AI tools. Instead, he encourages them to adjust their expectations and start with smaller goals. “Focus on governing and cleaning only the data needed to identify business opportunities and resolve that particular problem.”
It is important to clearly define the values of your organization and ensure that employees understand them. This provides the necessary guidelines to ensure that employees can properly identify and correct data in the event of anomalies in the company as per their expectations. “There is a bias in the AI model, and corrections come down to the individual who makes the value call,” says Laine.
She adds that the healthcare system needs to remember that AI is not perfect. Human intervention is especially important when determining why anomalies occurred in the data. “If I were a doctor, I feel more at ease knowing that the data governance team is making sense behind the scenes, making sure the data makes sense,” says Laine.
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