Affected person information is more and more voluminous in at the moment’s age of digitized healthcare. The issue is that this information is usually unstructured, making it tough to derive high-value, actionable insights.
This section of our whitepaper, The Scientific AI Scorecard: How Totally different Integration Approaches Deal with Deployment Challenges, provides a short overview of this very downside. Click on right here to obtain Pt. I of the whitepaper and find out about the advantages of an AI platform for implementation and deployment.
Unreliable and Unstandardized Knowledge
A good portion of healthcare information is unstructured, which means it lacks a predefined format or group. This contains narrative medical notes, radiology experiences and patient-generated information.
Unstructured information is difficult to course of and analyze utilizing conventional strategies, making it tough to extract priceless insights for AI algorithms. As an example, a pulmonary embolism algorithm wants to research all chest CT exams with distinction, however this info is just not all the time constant within the metadata.
A standard answer is to manually outline a brand new sequence of directions or tips for every scan – generally known as protocols – however this strategy is just not scalable as a result of sheer quantity. Moreover, as protocols change, the foundations and formatting of metadata additionally adjustments, which creates increasingly affected person information that should be sorted and analyzed. This highlights the significance of knowledge governance and the necessity to successfully make the most of metadata. Beneath is a fast take a look at the favored AI integration choices working on this context: Level Options, Marketplaces and Platforms.
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