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Preparing Data for AI Implementation in Healthcare
Healthcare organizations rely on many core applications to keep operations running, including electronic health record systems, customer relationship management tools and enterprise resource planning solutions, to name a few.They’ve made a significant investment in these applications, and they’ve captured a lot of valuable data through them. Unfortunately, that can mean data is siloed in disconnected environments, unable to be leveraged for broader actionable insights.Data quality and governance continue to be major areas of interest as healthcare organizations explore the role of artificial intelligence in their workflows, especially as they seek to support overextended clinical teams amid tight budgets.Across industries, organizations are examining their data efforts as they prepare to implement generative AI solutions.
Medigy Insights
Data governance lays the foundation for how data is treated as an asset in an organization, and that involves how data is managed, protected and used. It should not be a supplementary consideration; it needs to be a core part of an organization. It also allows business and technical teams to connect more on data and clarifies and redistributes responsibilities.Organizations can usually mature data governance alongside AI governance, as AI solutions require data to be effective. AI governance includes standards and approaches to account for bias, transparency and risk associated with a tool; these align with the principles and practices of data governance.
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