Understanding the Ethical and Privacy Concerns with Suicide Risk Prediction Algorithms
Student Researcher: Sarah Logan
Mentor: Dr. Michael Zimmer
Suicide is one of the leading causes of death in the United States and suicide rates have been increasing in recent years. Many suicide decedents have contact with the healthcare system in the month before their death, meaning there is a significant opportunity to identify patients who are at risk for a suicide attempt. Current algorithms for predicting suicide risk use only data from electronic health records. It is proposed that these algorithms could be improved through the incorporation of publicly available socioeconomic data, in addition to electronic health record data. We will be investigating the ethical implications of accessing and utilizing this socioeconomic data in the suicide risk prediction algorithms by measuring public opinion on the potential privacy threats of such efforts.