The research, which was published July 7 in JAMA, details how the use of natural language processing — which leverages AI to find meaning in text and spoken word in a way similar to how a human would interpret them — can allow clinicians to more easily search and view electronic patient health records after the technology categorizes them based on analyzing keywords like “COVID-19 infection.”
In the study of 10,172 patients and 3,048 messages, the NLP model showed a 94 percent accuracy rate at detecting COVID positive cases, even if positivity was not mentioned in those messages.
The categorization can allow clinicians to triage prompt care to patients who need antiviral prescriptions like Paxlovid within the necessary five-day treatment time frame.
“When responses to patient messages occurred faster, patients were more likely to receive antiviral medical prescription within the five-day treatment window,” researchers wrote. “Although additional analysis on the effect on clinical outcomes is needed, these findings represent a possible use case for integration of NLP algorithms into clinical care.”
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