Building a Strong Foundation for AI and ML in Healthcare

The promise of artificial intelligence and machine learning in healthcare is tremendous, offering possibilities ranging from administrative process optimization and associated cost reduction to personalized treatment plans. Yet, for all the energy invested into these technologies, their success hinges on a foundational element that is often overlooked: the data. In healthcare, the ‘iceberg effect’ of data is profound—much of it lies beneath the surface, unseen yet critical. To leverage AI and ML effectively, a robust and clean data foundation is not just helpful; it’s essential.

AI and ML are often seen as technological powerhouses ready to solve complex problems with ease. However, in the healthcare sector, where data is as complex as it is vast, these tools can only function optimally with a well-laid data foundation. Before healthcare systems can “bolt on” ML and AI solutions or run pilots with AI companies, they must ensure their data is unified and cleansed across both administrative and clinical datasets.

Applying AI and ML to health system data requires a unified approach that spans the chasm between different data types. This means bringing together multiple teams to manage and harmonize datasets, a process that sets the stage for all subsequent AI-driven initiatives. Without this step, AI projects—no matter how sophisticated—might progress at a snail’s pace, hindered by fragmented and inconsistent data.