According to a recent report by information technology services and consulting company Accenture, artificial intelligence may aid the global nursing shortage by automating a significant portion of the modern nurse’s day-to-day administrative tasks.
With an anticipated 13 million nurse jobs in need of replacement around the globe over the coming years, the International Council of Nurses declared the worldwide nursing shortage a “global health emergency” in a March 2023 report. In 2019 alone, a health care industry — not yet exasperated by the pandemic — the global nursing shortage reached 30.6 million jobs.
To combat nursing burnout and the consequential nursing shortage, Accenture suggested utilizing AI-enabled robots.
“AI-enabled robots can relieve the burden of the tasks nurses complete in a 12-hour shift,” the company reported. “They help patients with their daily routines, remind them to take medications and answer medical questions.”
Nurses only spend an estimated 21% of their time on direct patient care, with much of the rest of their shifts dedicated to clinical documentation and administrative tasks.
“Understanding the potential of new technologies that are built on a digital core and applying them in novel ways can enable breakthrough innovation across clinical tasks,” the report argued. “Time-saving technologies, such as ambient listening and generative AI, can reduce inefficiencies and improve nursing work. Research shows that 30% of administrative tasks for nurses can be automated or reassigned.”
The use of artificial intelligence inside the health care industry may lead to other administrative innovations as well.
“Rather than clinical staff rushing to keep up with an overbooked operating room (OR) schedule, they are turning to AI,” the report said. “A large healthcare provider is able to do 7% more surgical cases despite having to close 20% of its ORs at times thanks to AI -powered solutions integrated with electronic health records (EHRs) that predict OR availability, automate operating room scheduling requests and use machine learning models to predict staffing needs.”
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