Jonasson & Trichakis: Analytics-based Opioid Overdose Prediction

Professors Jónas Jónasson and Nikos Trichakis are continuing their collaboration with industry partner, The Staten Island Performing Provider System (SIPPS) researching methods and models to predict an individual’s risk for any adverse opioid-related event and to assess how providers can use these models in intervene with patients. 

At this point, they have tested their model and come to four main findings:  

1. They have found a way to train their model to predict an outcome that is relatively rare, specifically fatal overdose. Their method outperforms others already in use. 
2. Rather than develop several models, one for each possible outcome, the research team’s approach led to a single model for predicting different outcomes. In the field, this results in simpler implementation and maintenance of the tool for providers. 
3. Their model can identify the small number of patients with whom to intervene that will result in preventing a large fraction of opioid-related harm. Specifically, they can identify the 1% of patients who would account for 68% of all opioid-related adverse events. 
4. The model is sufficiently robust that its predictive performance is not affected by data delays in reporting or extending the forecast in days for an adverse event. 

This project resulted in a model and implementation that could help the SIPPS better serve their at-risk patients. A team of MIT researchers has been formed to support this implementation and to conduct impact evaluation of the intervention system (a combination of predictive analytics and outreach program).