Today’s healthcare landscape is inundated with vast amounts of patient data due to the digitalization of healthcare. However, a large issue exists as much of this data remains unstructured, leading to difficulties in extracting valuable insights. This problem is discussed in detail in the segment of our whitepaper, The Clinical AI Scorecard: How Different Integration Approaches Handle Deployment Challenges.
The data used in healthcare is often unpredictable and not standardized. It usually includes things like clinical notes, patient-generated data, and radiology reports, all of which lack a predefined format or organization. This lack of structure adds significant complexity when it comes to processing and analyzing the data using typical methods, thereby hindering the extraction of insights for AI algorithms.
For example, an AI algorithm designed to detect pulmonary embolism needs to analyze all chest CT scans conducted with contrast, but such informational consistency is often found missing in the metadata. Many have attempted to solve this problem by manually defining a new set of instructions, or protocols, for each scan. However, this method isn’t feasible due to the sheer volume of scans and the changing nature of protocols, which leads to more patient data requiring sorting and analysis.
This scenario underlines the necessity for effective data governance and the optimal usage of metadata, and thus, presents us with three prominent AI integration options – Point Solutions, Marketplaces, and Platforms.
Understanding the power of clinical AI platforms can be crucial, as these platforms can help transform raw data into valuable action. The wide-ranging benefits of an AI platform for implementation and deployment can be found by downloading Pt. I of the whitepaper.
To put it succinctly, the data deluge in healthcare presents a daunting challenge. Rightly deploying AI can help navigate through this data ocean, but the question remains – will AI sink or swim? With proper structure, data governance, and optimal AI integration, the transformation of data into action holds promising potential, and AI’s ability to manage the data surge could be pivotal for future healthcare.
