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In preliminary testing, Conte’s team and MIT Sloan researchers found their model was highly accurate at predicting overdoses and fatal overdoses, even with delays in the data of up to 180 days. The model trains on more than a hundred of these variables to predict future likelihood of an overdose. Staten Island’s program relies on health records, death records, and insurance claims to “learn” about an individual’s demographics, diagnoses, and prescriptions. And unlike traditional computer models, these algorithms can process vast amounts of data to make informed forecasts.

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The past data gives them a sense of how to predict certain future outcomes. Machine learning algorithms like the ones being used by Conte’s organization are trained using historical data sets that often include hundreds of thousands of patient insurance claims, death records, and electronic health records. In other words: Are the models ready, and are we ready for them? These researchers stress that more work needs to be done to make sure that the most harmful tendencies of AI don’t rear their ugly digital heads.

However, experts are still grappling with key issues, like validating the models, reducing bias, and safeguarding patients’ data privacy. Other communities are close behind, increasingly looking to AI as a tool to prioritize outreach and support in an overburdened health care system. In contrast, Staten Island’s program is one of the first of its kind to apply a predictive machine learning algorithm to opioid overdose in a way that directly affects patient care. But most of these models have thus far existed exclusively in closed experiments, measured against retrospective data rather than applied to prospective, real-world decision-making. The beauty of these algorithms lies in their ability to synthesize large data sets, recognize hidden trends, and make actionable predictions-capabilities that are desperately needed for an issue as complex and varied in cause as the opioid crisis. Through data that has not been publicly released yet, The Daily Beast learned the program cut hospital admissions in half for the more than 400 people enrolled, and reduced their emergency room use by 67 percent.Īs health records have evolved to allow clinicians access to vast quantities of patient data in time with advances in processing power, predictive modeling has gained traction as a potential way to predict and prevent opioid overdose deaths. Joseph Conte, Staten Island Performing Provider SystemĮarly signs suggest that it’s working.