Stratification Tool for Opioid Risk Mitigation (STORM)
Description
Patients exposed to opioid drugs often have complex medical needs and are at risk of multiple negative outcomes, including overdose, suicide, development of addiction, and other behavioral health challenges. Numerous strategies have been developed to minimize risks and treat conditions that underly them, but coordination of care across patient treatment providers and conditions can be challenging and interventions to reduce risk can be time consuming. Decision support to systems are needed to ensure recognition of a patient’s current conditions and consideration and tracking of recommended interventions. Risk estimation is needed to ensure that the most complex and at-risk patients receive adequate clinical attention in time-constrained health care environments.
Detailed example
The STORM predictive model provides an estimate of the likelihood of an overdose, suicide event, or death in the next year, documented in a health care system.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The effectiveness of the targeted prevention program was tested during a randomized staged roll-out of the targeted prevention program using a stepped wedge evaluation design. The targeted prevention program utilizes the STORM model to identify patients for interdisciplinary case review (i.e. by a team of clinicians which includes those with pain, behavioral health and recovery expertise). Inclusion in the mandate for team-based case review of patients, the predictive model estimated that "very high" risk of overdose or suicide events in the next year was associated with a significant 22% reduction in all-cause mortality within 4 months of inclusion (Strombotne et al., 2023). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060407/ Inclusion in the case review program was also found to reduce all-cause mortality and reduce likelihood of opioid analgesic discontinuation, in the subpopulation of patients on long-term opioid analgesics (Li et al., 2023).
Controls / human review
ATO: Not reported; PIA: Not published