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Aidoc Staff

Leveraging Analytics for Enhanced Clinical AI Performance

The integration of clinical Artificial Intelligence (AI) has emerged as a transformative force in a domain ripe for its benefits, ranging from improved efficiency and diagnostic accuracy to improved patient outcomes, to name a few. However, the effective use of AI in these high stakes environments calls for a detailed approach, one that is guided by robust analytics to measure performance and the overall value it adds to the organization. Without an analytically sound approach to AI adoption, its usage and thus, its long term effectiveness, can be compromised.

Guiding Strategy With Analytics

To optimize the application of AI to clinical workflows, it’s crucial to leverage analytics effectively as a means to guide the evolution of your AI strategy. Your AI partner can help by presenting your institution’s data in three crucial areas:

  1. Technical Performance: A comprehensive analysis of all cases in the purview of your AI use cases should unearth key discoveries, including timeliness, AI prevalence and overall algorithm performance. Without a proven high-performing algorithm notifying and activating the right clinicians on the right cases at the right time, AI-driven workflow enhancements can’t live up to their promise.
  2. User Engagement: To justify widespread adoption, healthcare tech is beholden to three criteria: it must be useful, usable and used. AI has in fact been proven to drive clinical workflow enhancements, and yet, intuitive functionality does not equal intuitive adoption. In other words, simply providing insights is not the promise of AI: it’s the action clinicians can take as a result of an AI-driven notification. Therefore, it’s crucial to measure your facility’s user engagement and acceptance rates. This can be measured through system engagement, acceptance surveying, training andQ&A sessions or all of the above.
  3. Value Demonstration: Once you have established satisfactory levels of technical performance and user engagement, you can begin to truly demonstrate the downstream value of AI. How can your AI vendor highlight the downstream effects of implementing AI? Can it lead to reduced read times, quality improvement, length of stay and time to treatment, efficiency and case prioritization? These are just some examples of how you can establish measurables to prove AI’s impact at your facility.

Key Questions for Facility Leaders

Those contemplating their AI strategy are encouraged to pose critical questions to address key concerns, including but not limited to:

  • Data Drift: What is the stability of the solution, and its performance hold strong over time?
  • Timely Analysis of Cases: Are algorithms running strictly on protocols, or do they run automatically in the background? How does this impact the timeliness of case analysis and subsequent notifications?
  • Effective Monitoring: How can we gauge the performance of a given algorithm and its impact on workflows? How can it be adjusted to better meet our facility’s goals? 

By asking these questions early on, facilities can better navigate the complexities of AI implementation and ensure its sustained success.

Unveiled Value: The Journey Forward With AI

The widespread adoption and integration of clinical AI holds immense promise, but to realize its full potential, facilities are best off approaching AI analytically. By measuring the success of their AI strategy on data-driven insights, facilities can better navigate challenges, measure algorithm performance and uncover new opportunities, ultimately enhancing the delivery of care and patient outcomes in an increasingly digital age. 

Click here for a deep dive on AI integration options, and why a platform approach is the key to a scalable, future-proof AI strategy.

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