Identifying patients at risk of suicide using data from health information exchanges

Suicide is within the top 10 causes of death in the US and rates have increased over 30% in the last two decades. Identifying suicide risk in healthcare settings has become a national priority and predicting risk using statistical models has emerged as a promising approach to achieve this. Numerous previously published models drawing on information in hospital records, insurance claims, or single healthcare networks perform reasonably well. With recent federal mandates established to develop health information exchanges (HIEs) that include patient information from both diverse healthcare settings and multiple healthcare networks, it is imperative to determine whether this newer data source offers an opportunity to model suicide risk with robust yet largely non-curated, real-world patient information. We examined the characteristics and performance of suicide risk models developed from a HIE, the Kansas Health Information Network.

In this retrospective cohort study, utilizing HIE data between 2012 and 2017, we developed predictive models of suicide attempts in 18-64 year old Kansas residents. Predictors included patient age, gender, and medical diagnosis codes. We evaluated model performance metrics, predictor importance, and characteristics of patients accurately labeled as high risk.

A total of 501,595 18-64 year old Kansas residents were included in the study, with a suicide attempt rate of 0.4% (n = 1,914). Our best model had an average area under the ROC curve (AUROC) of 0.82 that was typical of published models (AUROCs = 0.73-0.85), and predictors of suicide attempts included diagnosis codes found in other published models but also reflected the diversity of clinical settings contributing to the HIE.

Real-world data from the diverse clinical settings captured in an HIE may provide unprecedented opportunities to predict suicidal behavior.