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Anyone who is not in the medical profession and who has wandered into an emergency room may be baffled by the hours of waiting and the mysterious process by which nurses and doctors move patients through the stages of the ER.
Researchers at Yale School of Medicine and Johns Hopkins University wrote recently that an artificial intelligence program they’ve created can improve the emergency room process by making the task of triage more efficient and accurate. Triage is when nurses assess the severity of conditions at the intake of patients.
Lead author R. Andrew Taylor and colleagues describe a three-year experiment spanning 2020 through 2023, in which emergency room nurses at three ERs in the northeastern US used the AI program for 176,648 patients to help the nurses rank the severity of cases at intake.
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The authors found that nurses using the tool were able to move patients through the emergency room process more rapidly — from how long it took to provide initial care to how long it took to assign a bed to how long it took to discharge patients. All of which resulted in decreased time in the ER overall.
The “AI-informed triage” program, a “clinical decision support tool” (CDS), resulted in “improved triage performance and ED [emergency department] patient flow,” they wrote, so that “AI could lead to decreased wait times and ED length of stay.”
But they also found that nurses with the tool were more attentive to when patients needed critical interventions, such as hospitalization, surgery, or admission to the intensive care unit.
A ‘tree’ of possible decisions
In the study, Impact of Artificial Intelligence–Based Triage Decision Support on Emergency Department Care, Taylor and his team describe a computer UI that displays the recommendation of the CDS to the nurse.
The AI program is not a large language model like OpenAI’s GPT. It is a much older, more traditional AI technique known as “random forest,” which relies on neural networks just like GPT but does not generate text outputs. Instead, it navigates a “tree” of possible decisions and chooses the best among them.
Yale School of Medicine, Johns Hopkins University
The CDS was input with the age, sex, arrival mode, vital signs, “chief complaint,” comorbidities (medical condition history that might indicate risk areas such as high blood pressure), and “active medical problems” of each patient at intake. (Interestingly, across all cases, the three most common chief complaints were abdominal pain, chest pain, and shortness of breath.)
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Nurses were asked whether they agreed or disagreed with the computer’s ESI score and were asked to assign their own score as they normally do in the ER. Their agreement or disagreement with the computer was an important variable in the experiment because the study measured what happened when nurses were in accord or not with the AI’s recommendation.
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The immediate payoff, wrote Taylor and his team, was that patients “flowed” through things faster. “There was an observed decrease in time from arrival to the initial care area,” they wrote. There was also a change in how fast people were discharged from the ER, by as much as 82 minutes on average.
The biggest change is that those in the high-acuity category spent less time waiting before being sent to critical care, a reduction of over two hours. “The most notable changes were experienced by those critically ill or those meeting critical care or emergency surgery outcome criteria,” they wrote.
Efficiency isn’t the only outcome
It wasn’t just efficiency, however. The number of patients properly assigned to “critical care” rose when using the CDS, meaning patients who eventually wound up dying in the hospital or being admitted to the intensive care unit were more accurately identified beforehand during triage. With the AI, nurses were becoming more “sensitive” to the cases that required critical care, as Taylor and his team put it.
And the nurses who agreed more often with the CDS ended up having even better sensitivity to the criticality of urgent care, surgery, ICU, etc.
Limitations
The uncertainty about the role of human nurses’ individual acumen is not the only limitation of the study. In addition, different ERs can have seasonal trends that are “confounders,” factors that make the study’s findings problematic.
Another limitation is that the CDS drew upon electronic health records, which have their own limitations, such as a lack of specificity about patients.
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One very intriguing conclusion — and it’s probably relevant for all AI implementations — is that AI needs to be tuned to the particular setting. The experiment was done across three ERs in a particular region of the US, and that clearly plays a role in the outcomes.