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Aidoc Staff

Can an AI Strategy Be Successful With a Point Solution?

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

TL;DR: Point solutions are effective at addressing specific tasks, such as identifying certain pathologies, but they do not support a scalable AI strategy without significant time from IT staff and facility investment. This is where an AI platform can serve as an all-in-one implementation solution, helping teams effectively manage data, clinical workflows and ongoing maintenance.

Short answer: Yes. Long answer: No. 

A single algorithm- a point solution- can manage multiple AI functions such as computer vision, natural language processing and deep learning, but it will only be for one use case, such as intracranial hemorrhage (ICH) detection and stroke workflow coordination. 

If the value you need AI to bring to your facility can be solved by a single solution, then the strategy was successful. 

However, there’s two ways to approach an organizational AI strategy: 

  1. Looking at the enterprise (i.e. the entire facility or integrated delivery network) or
  2. Looking within a department, like radiology or emergency

At face value, the second approach is simpler. It allows you to try one application and see if it meets expectations. However, the long-term implications of the point solution could be a data silo. 

Learn From the Past: The Importance of Interoperability

Consider how electronic health record (EHR) technology was initially introduced. 

Different vendors, incompatible systems, lack of standardized data and department-by-department implementation meant each unit was operating independently and unable to share patient information effectively.

You run a similar risk by relying on AI point solutions. 

More than 500 devices were categorized as “artificial intelligence and machine learning enabled medical devices” on the FDA website in October 2022. It’s reasonable to assume that – given the explosive growth of healthcare AI – in five years the average hospital will run 100 “must-have” clinical applications, each requiring monitoring, integration, processes and a mechanism to measure ROI.

EHRs showed the importance of interoperability, and that’s why many health systems are exploring an enterprise-wide platform that leverages an AI-based operating system (or aiOS™) that serves as the operational layer of AI – connecting systems and service lines with a scalable and purpose-built AI solution.

Dive further into this topic in “AI’s Promises for Healthcare: Will It Deliver or Disappoint?”

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