Principal lead data scientist Jasmine McCammon discusses her opioid model
Data Science

Geneia Conversations: Opioid Predictive Model

December 16, 2020
Principal lead data scientist Jasmine McCammon discusses her opioid model.
Vice President, Marketing


Imagine you’re a few weeks away from having your wisdom teeth extracted or your knee replaced. The standard of care is to manage expected pain, often with an opioid prescription. Yet, you remain concerned about taking opioids. After all, research by the CDC suggest people can become addicted to opioids in as few as five days.

Until recently, there was little providers or patients could do to measure an individual’s risk of becoming addicted to opioids. Enter Geneia’s opioid model*+ to predict opioid abuse and overdose.

Opioid misuse statistics

In short, the model, for those who have received at least one opioid prescription, the Geneia Data Intelligence Lab’s model predicts that person’s likelihood of an opioid abuse diagnosis or an overdose in the coming six months.

Just as importantly, our opioid model works. Evaluated on several independent datasets, Geneia’s predictive model accurately identified 80 to 88 percent at risk for opioid misuse in the next six months.

The creator of the model, Jasmine McCammon, principal lead data scientist, recently joined the Geneia podcast to discuss her model. In the podcast, McCammon discusses what makes Geneia’s opioid model unique, how she collaborated with Geneia clinicians to create the model and how healthcare organizations can use this model. Listen now.

*Certain data used in this study were supplied by International Business Machines Corporation. Any analysis, interpretation, or conclusion based on these data is solely that of the authors and not International Business Machines Corporation.

+Predictive models, by their very nature, contain certain assumptions. This is not an attempt to practice medicine or provide specific medical advice, and it should not be used to make a diagnosis or to replace or overrule a qualified healthcare provider's judgment.