Andy Pollen

How Health Systems Can Mitigate AI Implementation Risks

Artificial intelligence is rapidly transforming the healthcare industry — improving patient care, streamlining workflows and reducing costs. However, clinical AI solutions also come with certain risks that have slowed broader adoption, including integration, workflow and ease of use.

What are the risks of implementing AI solutions?

Many of the risks associated with AI are not due to the technology, rather operational and structural challenges within the complex healthcare industry itself. Still, the ability for clinical AI to overcome these hurdles is essential to successful implementation:

  • No Governance: Without a standardized approach to AI implementation, individual departments may make decisions that are not in the best interests of the facility or health system as a whole.
  • Lack of Native Integrations: AI solutions must integrate to systems of record and schedules to deliver complete workflows for each of the designed use cases. Lacking a single integration point diminishes the utility of what is delivered, especially with acute care.
  • Insufficient Security:  Each additional AI service connected and deployed can increase risk because security standards can vary among disparate vendors. Plus numerous vendor connections to your systems of record elevates exposure risk.
  • Manual Orchestration: Without automated AI orchestration, teams will need to rely on manual processes or rule-based auto-forwarding, both of which are inefficient and can lead to errors.
  • Data Drift: AI algorithms drift, meaning that performance can degrade. Without drift mitigation processes in place, the value of AI solutions will diminish and user trust will be eroded.
  • Confusing User Experience: End users need a way to easily visualize and interact with AI-derived findings. Without a unified approach, they may need to deploy multiple mobile or desktop applications, creating confusion and complications. 
  • Limited Scope: AI can be used for both acute and subacute patient management, but it requires the ability to orchestrate multiple solutions concurrently to be effective.

How to overcome these AI adoption risks

An AI operating system helps health systems mitigate the risks associated with large-scale technology shifts by serving as the full stack orchestration, monitoring and action platform for all AI solutions. This means:

  • Minimize errors: A standardized framework for implementing and managing AI solutions across the enterprise ensures consistency.
  • Reduced IT workload: Pre-built integrations with a wide range of systems of record, eliminating custom integrations or the need to monitor solutions individually. 
  • Eliminate manual orchestration: Multiple AI activities can run on a single virtual machine, improving data center utilization and reducing time to launch for new solutions.
  • Limit security risks: Removes redundant connections into systems of record and confirms all AI solutions on platform adhere to the same security standards.
  • Reduced risk of algorithm drift: Built-in data drift mitigation features help ensure that AI solutions continue to perform accurately over time.
  • Enabling technology consistency: Delivers a standard user experience, making it easier for clinicians to use AI solutions and reduce the risk of errors.
  • Reduced patient leakage: Automatic post-acute workflows allow health systems to monitor patients who need additional care beyond their initial acute finding, whether image- or narrative-based.

Explore the Aidoc aiOS™

Aidoc is setting the standard for how AI should be scaled in healthcare with the aiOS™, the only unified platform for enterprise AI adoption. The aiOS™ helps health systems reap the full benefits of transformative AI technology. Learn more in this infographic.

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Andy Pollen