Addressing Consequential Public Health Problems Through Informatics and Data Science

Extract

In 2020 and 2021, Journal of the American Medical Informatics Association (JAMIA) published many papers related to the COVID-19 pandemic. Despite the COVID-19 pandemic’s prominence as a public health issue, other consequential public problems continue to plague our society and, in some instances, have been exacerbated by the pandemic. In this editorial, I highlight papers related to suicide, opioid use disorder, and child abuse.

Given that accurate identification of self-harm presentations to Emergency Departments (EDs) can lead to more timely mental health support, aid in understanding the burden of suicidal intent in a population, and support evaluation of public health initiatives related to suicide prevention, Rozova et al¹ developed an automated system for the detection of self-harm presentations from brief ED nursing triage notes. They applied natural language processing to 477 627 free-text triage notes from ED presentations in a single site; 1.4% were labeled as related to self-harm by 2 annotators. They evaluated the performance of multiple machine learning models finding that the calibrated Gradient Boosting model had the best performance for classifying the presence of self-harm in ED triage notes. Although a major limitation was that the ED triage notes were from a single site, the findings show promise for identifying patients who would benefit from mental health follow-up as well as for supporting population surveillance of self-harm and evaluating the impact of suicide prevention efforts.