Preventing Opioid Overdose: From Prediction to Operationalization

37 Pages Posted: 22 Jul 2021

See all articles by Jónas Oddur Jónasson

Jónas Oddur Jónasson

MIT Sloan School of Management

Neal Kaw

Deeksha Sinha

Massachusetts Institute of Technology (MIT), Operations Research Center, Students

Nikolaos Trichakis

Massachusetts Institute of Technology (MIT)

Anyi Chen

Staten Island Performing Provider System

Joseph Conte

Staten Island Performing Provider System

Ashley Restaino

Staten Island Performing Provider System

Salvatore Volpe

Staten Island Performing Provider System

Date Written: May 9, 2021

Abstract

The opioid epidemic remains a significant public health challenge in the US. A catalyst for reducing the incidence of opioid-related harm could be the development and operationalization of patient risk stratification models. Prior work has focused on the statistical performance of such models, usually tailored to specific adverse events or sub-populations, without considering operational implications. A particular challenge in this context is predicting the most severe outcomes (fatal overdoses) due to imbalanced datasets. We develop a single population-level model for predicting a full range of adverse opioid-related events. We further explore the capacity requirements of interventions based on our model and address salient implementation trade-offs. We partner with Staten Island Performing Provider System to access claims data and electronic health records for the patient population on Staten Island. We develop a machine learning model for predicting adverse opioid-related outcomes. Subsequently, we conduct a rolling horizon analysis to evaluate the capacity requirements of intervention policies leveraging the model. Finally, we quantify the trade-off in predictive performance against concerns that arise in implementation, such as interpretability, data delay, and prediction horizon length. Our single model risk stratification framework achieves an Area Under the Receiver Operating characteristic Curve (AU-ROC) of 0.95, 0.87, 0.83 for the outcomes of any adverse opioid event, opioid overdose, and fatal opioid overdose, respectively. This model can be used to identify a small intervention cohort (1% of the highest-risk patients) which includes the majority (69%) of adverse opioid events. Our results suggest that predictive performance does not need to be sacrificed to satisfy implementation constraints. Accurate risk stratification models are operationally feasible for implementation, even in the absence of training data on fatal overdoses. The implementation of such models allows for targeted interventions with limited intervention capacity and in the presence of operational constraints.

Note:
Funding Statement: This research was partially supported by the MIT Sloan Health Systems Initiative.

Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethic Approval Statement: All data were anonymized, and the Staten Island Performing Provider System approved data collection and analysis. The research was conducted in accordance with the requirements of the Health Insurance Portability and Accountability Act.

Keywords: Opioid Epidemic, Behavioral Health, Preventive Health, Machine Learning, Health Analytics

JEL Classification: I10

Suggested Citation

Jónasson, Jónas Oddur and Kaw, Neal and Sinha, Deeksha and Trichakis, Nikolaos and Chen, Anyi and Conte, Joseph and Restaino, Ashley and Volpe, Salvatore, Preventing Opioid Overdose: From Prediction to Operationalization (May 9, 2021). Available at SSRN: https://ssrn.com/abstract=3842424 or http://dx.doi.org/10.2139/ssrn.3842424

Jónas Oddur Jónasson

MIT Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Deeksha Sinha (Contact Author)

Massachusetts Institute of Technology (MIT), Operations Research Center, Students ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Nikolaos Trichakis

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Anyi Chen

Staten Island Performing Provider System ( email )

Joseph Conte

Staten Island Performing Provider System ( email )

Ashley Restaino

Staten Island Performing Provider System ( email )

Salvatore Volpe

Staten Island Performing Provider System ( email )

No contact information is available for Neal Kaw

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