An integrated opioid prescription optimization framework for total joint replacement surgery patients

Abstract

Opioid overdose, addiction, and death have become a nationwide crisis in recent years. Opioid leftover due to over-prescription at hospitals to treat chronic or surgical pains is one of the main contributors to the epidemic. To reduce leftovers, opioid prescriptions should be adjusted and tailored to patients’ needs. However, insufficient prescription may result in frequent refills for patients with high opioid-use levels, which can lead to inefficiency to patients, physicians, and pharmacists. Therefore, developing an optimal opioid prescription model to provide the necessary and patient-specific amount of opioids with minimal refills has a significant importance. In this paper, we introduce an integrated analytical framework, which intends to optimize both opioid prescription and number of refills based on stratification of patients’ opioid usage levels and corresponding stochastic programming. A case study for total joint replacement surgery patients at a community hospital is then introduced to illustrate the applicability and benefits of the framework.

Keywords:

• Opioid prescription
• total joint replacement
• classification
• stochastic programming
• optimization

Additional information

Funding

This work was partly funded by National Science Foundation.