Session ID: 
198

Leveraging Machine Learning to Reduce Opioid Prescribing

4:00pm - 5:00pm Wednesday, March 11
Orlando - Orange County Convention Center
W230A

Description

Unwarranted variation and overprescribing of outpatient opioid medication have long been a problem in healthcare. With opioid related deaths accounting for more than half of all the drug overdose related deaths in the United States and the average duration of opioid prescriptions increasing, healthcare organizations have the responsibility to reduce the opioid utilization. Locally, Advocate Aurora Health (AAH) has made an effort to reduce opioid medications prescribed; however, has struggled to make any significant impact. Traditional analyses and summary statistics were not sensitive enough to detect clinical variation within a large, high-performing organization. Machine learning (ML) is effective at uncovering subtle patterns within large amounts of data and identifying unwarranted variation in opioid prescribing (provider and specialty level). Using unsupervised ML, opioid prescriptions for 56,000 patients from 1,500 providers were evaluated, and an algorithm that detected unwarranted variation in prescribing was developed.

Learning Objectives

  • Describe ML and the advantages it has over the standard classical and descriptive statistics in uncovering unwarranted variation
  • Discuss challenges preparing the data for the ML algorithm
  • Describe how ML is used with physician expertise to identify outlier opioid prescribers
  • Discuss how the ML results can be implemented and operationalized to reduce clinical variation

Speaker(s)

Business Intelligence Consultant,
Advocate Aurora Health

Continuing Education Credits

ABPM
1.00
ACPE
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

Data Scientist
Nurse or Nurse Practitioner
Physician or Physician’s Assistant

Level

Advanced