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Your Clinical AI Needs an Operating System: Here’s Why

aiOS, C-Suite, Information Technology, System Integration, Workflow

What Is it: An AI Operating System (aiOS™) allows for the clinical use of numerous AI applications over one unified system that integrates into an existing IT stack. Such a platform is necessary to scale clinical AI beyond one or two use cases and into an enterprise-wide solution with upstream and downstream ROI potential. An aiOS orchestrates the bridge between clinical data and image data, providing an infrastructure for both clinical workflows and analytics.

Problems an aiOS™ Can Help Solve

  1. Eliminating data silos: Imaging data is unstructured, and existing systems utilizing DICOM and HL7 don’t always contain organized and complete data. To implement AI at scale, data must be structured and organized. As health systems adopt multiple AI use cases, with up to millions of potential imaging metadata and various protocols, without an AI platform it is impossible to automatically orchestrate the AI.
  2. Data drifts over time: Protocols and scanners change over time. Without a central operating system actively monitoring for accuracy and yield, the expected performance of the deployed algorithms may change. The coordination of how, when and why an algorithm is applied is just as important as the diagnostic accuracy of the algorithm.
  3. Hospital IT resource constraints: Resources are stretched thin with limited capacity. A vendor with deep IT infrastructure expertise is needed for seamless integration and efficient deployment. This is also important in future expansions and upgrades, which are made more complex without an AI platform that can add additional first, second- and third-party AI solutions with nearly no hospital IT effort.
  4. Big picture metrics: The performance of AI needs to be measured beyond the traditional diagnostic accuracy metrics of sensitivity and specificity. Improving operational efficiency and patient outcomes ultimately is related to other downstream metrics, such as a reduced length of stay and increased ED throughput.
  5. Communication breakdowns: The value of AI is difficult to realize when the insights delivered are not timely, actionable or specific to the user receiving the information. To achieve tangible AI value, a single user interface is needed to coordinate and manage patient care at different touch points throughout the entire pathway of care.

Organizational AI strategy must consider short-term needs – like AI proof of value – with long-term infrastructure and usability implications that an aiOS™ can provide. Learn more about how an AI platform is critical to implementing a scalable and strategic enterprise-wide solution.

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