In the wake of ChatGPT, Artificial Intelligence (AI) has exploded in popularity and has a strong hold on mainstream conversations. One area of impact that is less in the limelight, however, is the utilization of AI in healthcare. While those less keen on the inner workings of AI might initially think of robotic physicians when hearing about healthcare AI, the reality is far more in line with a “man and machine” dichotomy. AI applications in healthcare have effectuated a wide range of benefits both for health systems and, more importantly, patients. In this article, we’re going to outline three real-world examples explaining how AI is used in healthcare, making drastic improvements to hospital workflows for a variety of pathologies.
The role of AI in healthcare is as expansive as it is effective, leading to a sizable impact on varying service lines. As the cockpit of innovation, one area that was particularly ripe for the benefits of AI from the outset was the reading room.
As it stands, AI provides exceptional support in mitigating radiology workloads that are only increasing as the field experiences a palpable shortage of physicians. One study has found that AI-based reprioritization of the reading worklist with AI “has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage.” The results included a reduction in queue-adjusted wait time between “negative (15.45 minutes) and positive (12.02 minutes), saving time for patients in immediate need of medical intervention.
A separate study for incidental pulmonary embolism patients found that AI-based triaging led to a “median iPE wait time reduction of 90% (863 minutes).”
As the enterprise-wide platform approach takes hold over AI in healthcare, additional service lines outside of radiology have begun to reap the benefits of the novel technology in several areas, some expected and some unexpected.
The application of AI in neurology had led to some remarkable improvements in not only triaging in the radiology department, but offers various benefits to service line specialists, including:
In fact, one study conducted at Cedars-Sinai Medical Center found a decreased hospital length of stay for ICH patients after adopting an AI-augmented radiological worklist triaging system, highlighting the impact that AI is having not only in health systems but on patient outcomes.
The graphic below shows an example of a stroke patient’s time to treatment, the first scenario in a health system without AI, and the second in a health system with AI:
Amongst the many types of AI used in healthcare is cardiovascular AI, which, thanks to a variety of AI-driven tools, have found similar benefits to the neuro field, including:
The aforementioned Cedars-Sinai study also found a connection between AI adoption and length of stay for pulmonary embolism patients, resulting in a 2.07 day reduction (26.3%).
Another case study from Yale New Haven Hospital showed that AI’s support of their pulmonary embolism response team (PERT) increasing advanced therapy at a spoke facility by 40% thanks to:
Despite its strong impact throughout radiology, neuro departments and cardiovascular workflows, AI is still gaining momentum worldwide. As hospitals continue to see the impact on performance metrics like turnaround time, length of stay and increased advanced therapy, AI’s application in healthcare will continue to grow, and we’ll see its positive impact in areas we may not have expected.
Want to learn more about the impact of AI on healthcare? Check out our eBook AI’s Promise for Healthcare.
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