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Algorithms to Identify Nonmedical Opioid Use.

Kimberley C Brondeel1, Kevin T Malone2, Frederick R Ditmars1

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Improved opioid use disorder (OUD) detection is crucial. Algorithms using electronic health records (EHR) and manual screenings show high sensitivity for identifying nonmedical opioid use (NMOU) risk.

Keywords:
AlgorithmDrug abuseDrug abuse predictionDrug addictionOpioid abuse

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Area of Science:

  • Medical Informatics
  • Public Health
  • Clinical Medicine

Background:

  • Nonmedical opioid overdoses have increased significantly over the past two decades, highlighting the need for effective detection technologies.
  • Manual opioid screening exams are sensitive but time-consuming; algorithms offer potential efficiency gains.
  • Previous studies suggested electronic health record (EHR)-based neural networks surpassed manual screenings, but recent data indicates comparable or lesser performance.

Purpose of the Study:

  • To review and discuss various manual opioid screening methods and algorithms.
  • To provide recommendations for clinical practice in identifying individuals at risk for opioid misuse.
  • To evaluate the effectiveness of different screening approaches, including EHR-based algorithms and manual methods.

Main Methods:

  • Discussion and synthesis of existing literature on manual opioid screening tools.
  • Analysis of multi-algorithm approaches utilizing EHR data for predicting opioid use disorder (OUD).
  • Evaluation of the performance of a specific algorithm, POR (Prove Opiate Risk), in a small sample size.

Main Results:

  • All reviewed screening methods and algorithms demonstrated high sensitivity and positive predictive values for detecting opioid misuse risk.
  • A multi-algorithm approach using EHR data showed strong predictive value for OUD in a large sample.
  • The POR algorithm exhibited high sensitivity for opioid abuse risk in a small sample.
  • EHR-based neural networks, when corroborated with manual screenings, also showed significant effectiveness.

Conclusions:

  • Algorithms, particularly multi-algorithm EHR-based approaches, show significant potential for identifying nonmedical opioid use (NMOU) and OUD.
  • These algorithmic tools can reduce provider costs and enhance care quality.
  • Combining algorithmic tools with clinical interviewing and further refining EHR-based neural networks can improve detection and management of opioid use disorders.