Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

Journal: American journal of surgery
PMID: 33820654

Abstract

BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients.

Authors

  • Jaewon Hur
    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
  • Shengpu Tang
    Division of Computer Science and Engineering, Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI.
  • Vidhya Gunaseelan
    Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
  • Joceline Vu
    Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
  • Chad M Brummett
    Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
  • Michael Englesbe
    Department of Surgery, University of Michigan, Ann Arbor, MI, USA; Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
  • Jennifer Waljee
    Department of Surgery, University of Michigan, Ann Arbor, MI, USA; Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA. Electronic address: filip@umich.edu.
  • Jenna Wiens
    Computer Science and Engineering, University of Michigan, Ann Arbor.

Keywords

External Resources

Popular Topics
Recent Journals