Large urban medical centers and research hospitals have led the way in AI adoption, but its long-term impact on rural healthcare might be even bigger. First, AI can help compensate for the staffing shortages in rural hospitals by triaging and flagging the positive cases on the radiologist’s worklist, ensuring the focus is on the right patients first.

An example of how inculcating AI into the rural setting can alleviate workflow and bottleneck issues is intracranial hemorrhage management. In a study at the University of Rochester, AI demonstrated it could cut the average reporting time for life-threatening brain bleeds from 132 minutes to 73 minutes, which can make a tremendous difference in expediting care, whether within a rural care center or in transferring patients to a larger stroke center.


For outpatients, whose conditions may be less acute, the difference is even more dramatic: In rural clinical scenarios, patients could wait for many hours before a radiologist has had the opportunity to view their scan and diagnosis. But AI can reduce that significantly.

Research from the University of Chicago demonstrated that AI flagging within a worklist could, for example, cut the delays of a radiologist viewing a positive brain bleed on a list of head CT exams by around ten hours. This can play a critical role in rural areas, where patients must make long commutes. In the event the AI quickly identifies a positive case on a routine CT exam and notifies the physician, the patient can be immediately cared for rather than sent home and asked later to return on another long trip.

With health systems acquiring more individual hospitals and building their AI strategies, they’ll need to establish a standard of care. Larger facilities might be equipped to offer more specialized procedures, but smaller, more rural centers can still provide high quality, expedient imaging services. In this scenario, AI can standardize the quality of imaging across a health system by helping ensure positive cases are prioritized and thus reviewed first, regardless of whether or not the worklist is longer.

Lastly, AI can also help coordinate care between larger and smaller facilities within health systems. For stroke centers or PERTs addressing patients in smaller or more rural communities, AI can alert multidisciplinary teams instantly of a positive case and expedite the process by automating transfer of relevant patient data and facilitating instant communication to enable teams to decide on the course of treatment.

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