Kaiser combines EHR data, survey responses to build suicide risk-predictive model

A team of researchers has developed a predictive model it hopes will be used to prevent suicides among patients in the 90 days following a mental health specialty or primary care outpatient visit.

Published in the American Journal of Psychiatry,  the researchers' study, "Predicting Suicide Attempts and Suicide Death Following Outpatient Visits Using Electronic Health Records," was carried out in five Oakland, Calif.-based Kaiser Permanente regions as well as the Henry Ford Health System in Detroit and the HealthPartners Institute in Minneapolis. In all, nearly 8 million people were involved in the research, which examined almost 20 million visits.

The research team from the Mental Health Research Network was led by Kaiser.

The researchers reviewed patients' last five years of EHR data to identify the strongest predictors of suicide risk, which include prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency room care and scores on a standardized depression questionnaire.

The study noted three key findings:

1. Patients in the highest 1 percent of predicted risk were 200 times more likely to attempt or die from suicide than patients in the bottom half of predicted risk.

2. Patients who were seen by a mental health specialist in the top 5 percent of predicted risk made up 43 percent of suicide attempts and 48 percent of suicide deaths.

3. Those seen for mental health in the primary care setting and placed in the top 5 percent of predicted risk accounted for 48 percent of suicide attempts and 43 percent of suicide deaths.

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