Understanding the Ethical and Privacy Concerns with Suicide Risk Prediction Algorithms
Student Researcher: Sarah Logan
Mentor: Dr. Michael Zimmer
Suicide is currently one of the leading causes of death in the United States and suicide rates have continued to increase in recent years. Many suicide decedents have contact with the healthcare system in the month before their death, indicating that there is a significant opportunity to identify patients who are at risk for a suicide attempt when they visit their healthcare provider. Current algorithms for predicting suicide risk use data only 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.
- Write a brief (1-3 sentence) statement of the problem/issue we are trying to address
- Draft the Research Questions
- Map out a literature review
- Outline rough methodology
- Map out process and timeline for collecting our data
- Build and deploy the survey in Qualtrics
- Organize and “clean” our data
- Data analysis
- Start writing up what we’ve found
- Types of output: research poster, paper draft, etc.