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Psychol Serv. 2017 Feb;14(1):34-49. doi: 10.1037/ser0000099.

Development and applications of the Veterans Health Administration's Stratification Tool for Opioid Risk Mitigation (STORM) to improve opioid safety and prevent overdose and suicide.

Author information

1
Veterans Affairs (VA) Program Evaluation and Resource Center, VA Office of Mental Health Operations.
2
VA Program Evaluation and Resource Center, VA Office of Mental Health Operations.
3
VA Northeast Program Evaluation Center, VA Office of Mental Health Operations.
4
VA Academic Detailing Program, VA Pharmacy Benefits Management Services.
5
VA Center for Innovation to Implementation, Palo Alto Health Care System.
6
Parsimony, Inc.
7
VA Program Evaluation and Resource Center, VA Office of Mental Health Operations, VA Center for Innovation to Implementation, VA Palo Alto Health Care System.

Abstract

Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g., dose, substance use disorders) and risk mitigation interventions (e.g., urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation (STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 (FY2010: 10/1/2009-9/30/2010) EMR-data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 (2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well (>.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk. (PsycINFO Database Record.

PMID:
28134555
DOI:
10.1037/ser0000099
[Indexed for MEDLINE]

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