By now, everyone in healthcare is aware of the problem of burnout among clinical staff and its consequences, which exacerbate staff shortages.
Health IT leaders at hospitals and health systems have already begun experimenting with artificial intelligence (AI), which is experiencing a huge surge in healthcare, to try to reduce the problem of burnout among clinical workers.
Could AI ever replace doctors? Some experts say it could — but only in limited, small cases. For example, Dr. Bruce Darrow, interim chief digital and information officer and chief medical information officer at Mount Sinai Health System in New York, says that in some cases where the clinical accuracy of doctors and AI is nearly the same, Part of clinical care could indeed migrate to AI in the future.
The point is: AI can help reduce physician workloads and, in turn, clinician burnout. Even with AI in a supporting role only, experts say the technology can help alleviate the enormous workloads clinicians carry today.
Dr. Eve Cunningham is right at home in this topic. She’s group vice president and chief of virtual care and digital health at Providence, and founder and chief executive of MedPearl. The MedPearl platform is an AI-enhanced clinical intelligence engine designed by clinicians, for clinicians.
Cunningham will speak on this topic at the HIMSS AI in Healthcare Forum in a case study session scheduled for Thursday, September 5.
We interviewed Cunningham about AI and burnout and got a sneak peek of her presentation.
Q. What exactly will you discuss in your session on AI and clinician burnout?
A. During this session, we aim to provide three real-world examples of the application of AI in our healthcare system, specifically in relation to clinical workflows and clinical staff burnout.
I want to give examples of: a self-developed (built) AI-enhanced technology, MedPearl; an example of partnering with a vendor (buying) to implement an AI-enhanced solution; and an example of a desired solution that gets stuck due to technical challenges related to infrastructure, technical debt, and misaligned incentives.
While AI has the potential to revolutionize healthcare by improving decision-making and optimizing workflows, its implementation is not without challenges. A key aspect of our discussion revolves around ensuring AI applications and capabilities are useful tools for clinicians and do not pose an additional burden.
The relevance of this topic is underlined by the ongoing discussions in healthcare institutions about the balance between technological progress and people-centered care.
Q. What is an example of AI in action at your organization?
A. An example of AI technology at our organization is the MedPearl platform, a clinical intelligence engine specifically designed by and for clinicians. This platform is an example of our approach to integrating AI in healthcare organizations.
MedPearl improves clinical decision making by consolidating clinical knowledge, patient data, and suggested next steps at the point of care into a single interface, optimizing clinicians’ workflow and reducing cognitive load.
The development of this platform has been achieved through collaboration between clinicians and technologists. The AI roadmap has been carefully designed, taking into account not only the technological aspects, but also the clinical safety and human factors involved.
Because we view AI as a tool and capability embedded in a larger product designed to support clinicians, rather than as a standalone entity, we’ve been thoughtful about the layering of AI capabilities we enable within the platform.
This example serves as a microcosm of our broader approach to AI in healthcare: thoughtful, tailored integration to enhance the role of the clinician and create confidence in the product from a quality and safety perspective.
Q. What two lessons do you hope session participants will learn and apply to their healthcare provider?
A. The first conclusion is that AI integration should be viewed as a deliberate, gradual process that aligns with specific clinical needs and strategic goals, rather than a quick, one-size-fits-all solution. Implementing AI in healthcare settings requires thoughtful consideration of the problems it aims to solve and a clear understanding of the potential impact on clinical workflows and provider well-being.
Second, we emphasize the importance of strategic use case choices. It is critical to prioritize AI applications that align with an institution’s strategic priorities and address high-value clinical or operational problems.
We share real-world examples from our experience at Providence, where the selection and implementation of AI use cases closely align with our strategic objectives and are designed to support, rather than replace, human expertise.
Attend this session at the HIMSS AI in Healthcare Forum taking place September 5-6 in Boston. More information and registration.
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