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JMIR Med Inform. 2019 Oct 31;7(4):e15794. doi: 10.2196/15794.

Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study.

Author information

1
Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, United States.
2
Information Technology Services, Yale New Haven Health, New Haven, CT, United States.
3
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, United States.
4
North Carolina Translational and Clinical Sciences Institute, University of North Carolina School of Medicine, Chapel Hill, NC, United States.
5
Department of Emergency Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, United States.
6
Department of Emergency Medicine, Mayo Clinic, Rochester, MN, United States.
7
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

Abstract

BACKGROUND:

Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department.

OBJECTIVE:

This study aimed to derive and validate an electronic health record-based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard.

METHODS:

A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative).

RESULTS:

Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978).

CONCLUSIONS:

This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.

KEYWORDS:

algorithms; electronic health records; emergency medicine; opioid-related disorders; phenotype

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