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Algorithms to Identify Nonmedical Opioid Use

  • Acute Pain Medicine (R Urman, Section Editor)
  • Published:
Current Pain and Headache Reports Aims and scope Submit manuscript

Abstract

The rise in nonmedical opioid overdoses over the last two decades necessitates improved detection technologies. Manual opioid screening exams can exhibit excellent sensitivity for identifying the risk of opioid misuse but can be time-consuming. Algorithms can help doctors identify at-risk people. In the past, electronic health record (EHR)–based neural networks outperformed Drug Abuse Manual Screenings in sparse studies; however, recent data shows that it may perform as well or less than manual screenings. Herein, a discussion of several different manual screenings and recommendations is contained, along with suggestions for practice. A multi-algorithm approach using EHR yielded strong predictive values of opioid use disorder (OUD) over a large sample size. A POR (Proove Opiate Risk) algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size. All established screening methods and algorithms reflected high sensitivity and positive predictive values. Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings. This review highlights the potential of algorithms for reducing provider costs and improving the quality of care by identifying nonmedical opioid use (NMOU) and OUD. These tools can be combined with traditional clinical interviewing, and neural networks can be further refined while expanding EHR.

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Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. University of Texas Medical Branch, University of Texas, 301 University Blvd, 77555, Galveston, TX, USA

    Kimberley C. Brondeel, Frederick R. Ditmars & Bridget A. Vories

  2. School of Medicine, Louisiana State University Health Sciences Center at Shreveport, LA, 71103, Shreveport, USA

    Kevin T. Malone

  3. Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA, 71103, USA

    Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett & Alan D. Kaye

Authors
  1. Kimberley C. Brondeel
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  2. Kevin T. Malone
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  3. Frederick R. Ditmars
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  4. Bridget A. Vories
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Contributions

Study concept and design: Kimberley C. Brondeel, Kevin T. Malone, Frederick R. Ditmars, Bridget A. Vories, Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett, Alan D. Kaye. Analysis and interpretation of data: Kimberley C. Brondeel, Kevin T. Malone, Frederick R. Ditmars, Bridget A. Vories, Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett, Alan D. Kaye. Drafting of the manuscript: Kimberley C. Brondeel, Kevin T. Malone, Frederick R. Ditmars, Bridget A. Vories, Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett, Alan D. Kaye. Critical revision of the manuscript for important intellectual content: Kimberley C. Brondeel, Kevin T. Malone, Frederick R. Ditmars, Bridget A. Vories, Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett, Alan D. Kaye. Statistical analysis: Kimberley C. Brondeel, Kevin T. Malone, Frederick R. Ditmars, Bridget A. Vories, Shahab Ahmadzadeh, Sridhar Tirumala, Charles J. Fox, Sahar Shekoohi, Elyse M. Cornett, Alan D. Kaye.

Corresponding author

Correspondence to Sahar Shekoohi.

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Conflict of Interest

Kimberley C. Brondeel has nothing to disclose. Kevin T. Malone has nothing to disclose. Frederick R. Ditmars has nothing to disclose. Bridget A. Vories has nothing to disclose. Shahab Ahmadzadeh has nothing to disclose. Sridhar Tirumala has nothing to disclose. Charles J. Fox has nothing to disclose. Sahar Shekoohi has nothing to disclose. Elyse M. Cornett has nothing to disclose. Alan D. Kaye has nothing to disclose.

Human and Animal Rights and Informed Consent

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Acute Pain Medicine

Highlights

• A multi-algorithm approach using EHR yielded strong predictive values of OUD over a large sample size.

• A POR algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size.

• All established screening methods and algorithms reflected high sensitivity and positive predictive values.

• Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings.

• A manual screening approach leads to a high sensitivity; such methods as OWLS or Rapid Opioid Dependence Screen (RODS) are quick and sensitive and can be tailored to an individual’s practice.

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Brondeel, K.C., Malone, K.T., Ditmars, F.R. et al. Algorithms to Identify Nonmedical Opioid Use. Curr Pain Headache Rep 27, 81–88 (2023). https://doi.org/10.1007/s11916-023-01104-7

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