AI could speed up access to COVID-19 treatment

Researchers at Emory University School of Medicine and the Georgia Institute of Technology are studying how the use of artificial intelligence (AI) could expand access and increase the efficiency of diagnoses and treatments for COVID-19.

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The use of telehealth and electronic health record (EHR) messaging has increased significantly during the COVID-19 pandemic, and the widespread availability of home testing has enabled patients to report a positive test and begin treatment without go to your doctor’s office. While this shift in health care delivery has many benefits, the researchers noted that the influx of messages without a digitized triage system can slow responses and delay access to timely treatment.

Now, researchers say AI could help sift through these messages and streamline processes.

We’re trying to take a mountain of incoming data and extract what’s most relevant to people who need to see it so patients can get treatment faster, said senior author Blake Anderson, MD, CEO of Switchboard and Emory’s primary care physician, in a press release.

The study, published in JAMA open network, examined how natural language processing (NLP) AI could speed up the time from a patient-initiated message, a physician response, and access to antiviral treatment for COVID-19. Building on previously tested deep learning predictive models, the team developed a new NLP model to classify patient-initiated EHR messages and evaluated its accuracy at 5 Atlanta-area hospitals between March 30 and September 1. 2022.

Over the course of the study, 3048 messages reported positive test results for COVID-19. The NLP model started when a positive test was reported via EHR.

The results of the study show that the NLP model classified patient messages with 94% accuracy. Additionally, when responses to patient messages occurred more quickly, patients were more likely to receive an antiviral prescription within a 5-day treatment window.

We were thrilled to see how NLP accurately and instantly evaluated messages from patients reporting a positive COVID-19 test and helped improve patient access to treatment, said study lead author Nell Mermin — Bunnell, a third-year student at Emory School of Medicine, in the press release. While this model has proven effective for this specific application, opportunities exist to broaden the scope beyond COVID-19 diagnoses.

The GNP model used in the study, called eCOV, was initially developed by Anderson. As more patients began to use the EHR to communicate with their care team, Anderson saw the need to better organize incoming messages to ease the burden on clinical staff and alleviate burnout. Anderson and his colleagues conducted experiments to evaluate the performance of the models and identified an algorithm to take into account message context, not just keywords.

The findings illustrate the power of using advanced NLP models in accurately identifying patients at risk for a given disease in real time, said study co-author May Wang, PhD, professor and Wallace H. Coulter Distinguished Faculty Fellow at Georgia Tech, in the press release. You have shown that the speed of patient access to healthcare can be significantly increased.

More analyzes are needed to measure the model’s impact on clinical outcomes, according to the study authors. Even without this data, however, the researchers said it is becoming increasingly clear that AI has the potential to reshape the way medicine is practiced as it is further integrated into traditional healthcare delivery.


Emory, Georgia Tech Uses Artificial Intelligence to Accelerate Access to COVID-19 Treatment. Press release. Emory University. July 11, 2023. Accessed July 12, 2023.

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