Aidoc Staff

AI in Hospitals: A Journey of Discovery, Depth and Deployment

Though AI has been used in medical environments as early as 1970, its ability to intricately transform and empower the many moving parts along the path of physician workflows were more broadly recognized within the last decade. While there’s a variety of healthcare AI players pushing the envelope and innovating the scope of AI’s capabilities in modern medicine, lingering questions arise when considering its impact and ROI. Given the ever-evolving landscape, let’s peel back the layers of healthcare AI to reveal the pivotal questions that should be explored of AI’s transformative power as you explore your AI strategy (and why you should ask them in the first place):

1. Where is AI impacting care?

Health systems face immense pressure as they work to improve their performance metrics across every aspect of the patient journey. All the while, financial cutbacks and labor limitations loom. The positive impact of AI in addressing these issues in health systems is a tangible reality, not some distant pipedream. With that in mind, there comes an array of things to consider as it relates to AI what it does at the level of clinical care: 

  • What does AI help hospitals accomplish right now? 
  • How widely has AI been adopted? 
  • What level of impact is AI accomplishing throughout health systems?  

A number of startups have emerged over the past few years to address the issues faced by today’s healthcare facilities, but the questions above act as a proverbial North Star when assessing the actual impact of AI in clinical care.

2. How broad is a vendor’s FDA-cleared offering?

AI as a whole has been a major conversation piece in public discourse, especially as it relates to the need for regulatory bodies to establish AI best practices before its widespread adoption. However, not all AI is equal in that regard. As Aidoc CEO Elad Walach said, “the distinction between generative AI, ChatGPT, is significantly different from healthcare AI, which has been on a fundamentally different trajectory for some time.” 

To date, the FDA has over 850 AI/ML-enabled medical devices listed on its website. With an influx of products flooding the market, here are some points you ought to consider:

  • A majority of FDA-cleared AI algorithms are based on medical imaging, meaning:
  • While a vendor may provide an ample imaging solution to address a specific pathology, there is a notable difference between companies offering point solutions and companies offering an enterprise-wide AI platform.
  • There are also non-clinical types of FDA approved AI that address a variety of hospital needs. E.g. population health, EMR integrations, data analytics, care coordination.

3. How will AI fit into my hospital in the long term?

Every hospital has its unique aims and metrics by which they measure the success of healthcare AI post-implementation, whether that be enhanced follow-up protocols, improved triaging, relieved administrative burden, shortened patient length of stay, reduced turnaround time and more.

However, as AI becomes more deeply intertwined in disparate hospital workflows, health systems need to look at AI from an enterprise-wide perspective, meaning: 

  • Healthcare AI can be deeply ingrained in hospital workflows beyond medical imaging. Vendors whose offering goes beyond  single-point solutions and are mindful of the enterprise-wide approach are built for the long haul.
  • There are more opportunities to produce meaningful ROI when implementing AI at scale

As health systems continue revamping C-Suite positions, there is an increased focus on finding ways to enhance experiences for both patients and clinicians. The adoption of enterprise-wide AI has brought about both positive clinical and financial outcomes for hospitals. 

Learn more about the transformative impact of enterprise-wide AI and the aiOS™

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Aidoc Staff