After developing the model with data from 2,500 patients and validating it with a second set, the researchers put it to use during a two-week pilot program. During that time, the model was able to anticipate 68 of the 207 ED visits (33 percent) required by lung cancer patients. The predictions also showed promise in categorizing patients into risk levels. Of the 131 patients identified as “high-risk”, 13 (10 percent) presented to the ED. For the 678 patients grouped as “low-risk”, only 10 (1.5 percent) required an ED visit. This demonstrates that the model successfully differentiates between high and low risk patients, as patients designated as high risk were 6.6 times more likely to visit the ED compared to those designated as low risk.

“Our hope is that triage nurses and physicians will be able to use this information to intervene before an ED visit is necessary,” Berman said. These interventions can include reaching out to the patient to preemptively schedule an outpatient visit, taking action to better manage the symptoms that would lead to the ED visit, or other proactive measures.


Researchers say the next step is to categorize the reasons for each ED visit and the actions physicians took during the pilot phase. They also plan to incorporate natural language processing elements into the model in order to improve its predictive value.

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