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Related Experiment Video

Updated: Jan 9, 2026

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Development and Validation of an Electronic Health Record-Based Algorithm for Identifying Patients With Long-Term

Shu Huang1, Tianze Jiao1,2, Serena Jingchuan Guo1,2

  • 1Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States.

Journal of Medical Internet Research
|December 10, 2025
PubMed
Summary

This study developed an electronic health record (EHR) algorithm to identify patients on long-term opioid therapy (LTOT). The algorithm accurately identifies LTOT patients, aiding clinical decision-making and risk stratification.

Keywords:
chronic painclassification algorithmelectronic health recordsopioid-related disordersopioidsvalidation study

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • Pharmacovigilance

Background:

  • Effective monitoring of patients on long-term opioid therapy (LTOT) is crucial for managing risks and benefits.
  • Accurate identification of LTOT patients in routine care is a prerequisite for implementing supportive interventions.
  • Current algorithms for LTOT identification in clinical practice are lacking.

Purpose of the Study:

  • To develop and validate an algorithm for identifying patients on long-term opioid therapy (LTOT) using electronic health record (EHR) data.
  • To assess the algorithm's performance in identifying LTOT episodes within a large healthcare system.
  • To provide a tool that can support clinical decision-making and risk stratification for patients on LTOT.

Main Methods:

  • A cross-sectional study utilized OneFlorida+ EHR data linked with Florida Medicaid claims (2016-2021).
  • An elastic net regression model was employed to identify LTOT episodes based on patient characteristics, clinical features, and medication use.
  • The algorithm was developed and internally validated using 2016-2018 data and externally validated using 2019-2021 data, with a Medicaid claims-based LTOT definition serving as the reference standard.

Main Results:

  • The developed EHR-based algorithm demonstrated strong performance, achieving a C-statistic of 0.83 in both internal and external validation datasets.
  • Sensitivity ranged from 73.4% to 78.8%, and specificity ranged from 73.3% to 76.8% across validation periods.
  • The algorithm successfully identified LTOT patients, with a significant proportion (73.3% internal, 75.5% external) captured within the top risk subgroups.

Conclusions:

  • The electronic health record (EHR)-based long-term opioid therapy (LTOT) algorithm exhibits accuracy comparable to claims-based methods.
  • This algorithm can effectively support risk stratification for patients undergoing LTOT.
  • The tool has the potential to inform clinical decision-making during patient encounters, improving care for individuals on long-term opioid therapy.