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Subst Abus. 2019 Jan 24:1-7. doi: 10.1080/08897077.2018.1545729. [Epub ahead of print]

Predictors of Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement among patients with opioid use disorder across 7 health systems.

Author information

1
a Division of Research , Kaiser Permanente Northern California , Oakland , California , USA.
2
b Department of Psychiatry, Weill Institute for Neurosciences , University of California San Francisco , San Francisco , California , USA.
3
c Institute for Health Research, Kaiser Permanente Colorado , Aurora , Colorado , USA.
4
d Colorado Permanente Medical Group , Aurora , Colorado , USA.
5
e Health Research Institute, Kaiser Permanente Washington , Seattle , Washington , USA.
6
f Department of Health Services , University of Washington School of Public Health , Seattle , Washington , USA.
7
g Henry Ford Health System , Detroit , Michigan , USA.
8
h Center for Health Research , Kaiser Permanente Northwest , Portland , Oregon , USA.
9
i Essentia Institute of Rural Health , Essentia Health , Duluth , Minnesota , USA.
10
j Department of Research and Evaluation , Kaiser Permanente Southern California , Pasadena , California , USA.

Abstract

BACKGROUND:

The prevalence of opioid use disorder (OUD) has increased rapidly in the United States and improving treatment access is critical. Among patients with OUD, we examined factors associated with the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures of alcohol and other drug (AOD) treatment initiation and engagement.

METHODS:

Electronic health record and claims data between October 1, 2014, and August 15, 2015, from 7 health systems were used to identify patients (n = 11,490) with a new index OUD diagnosis (no AOD diagnosis prior <60 days) based on International Classification of Diseases (ICD)-9 codes. Multivariable generalized linear models with a logit link clustered on health system were used to examine the associations of patient demographic and clinical characteristics, and department of index diagnosis, with HEDIS measures of treatment initiation and engagement.

RESULTS:

The prevalence of OUD among all AOD diagnoses varied across health systems, as did rates of AOD initiation (5.7%-21.6%) and engagement (7.6%-24.6%). Those diagnosed in the emergency department (adjusted odds ratio [aOR] = 1.58, 95% confidence interval [CI] = 1.27,1.97) or psychiatry/AOD treatment (aOR = 2.92, 95% CI = 2.47,3.46) were more likely to initiate treatment compared with primary care. Older patients were less likely to initiate (age 50-64 vs. age 18-29: aOR = 0.42, 95% CI = 0.35, 0.51; age 65+ vs. age 18-29: aOR = 0.34, 95% CI = 0.26, 0.43), as were women (aOR = 0.72, 95% CI = 0.62, 0.85). Patients diagnosed in psychiatry/AOD treatment (aOR = 2.67, 95% CI = 1.98, 3.60) compared with primary care were more likely to engage in treatment. Those identified in an inpatient setting (aOR = 0.19, 95% CI = 0.14, 0.27 vs. primary care), those with medical comorbidity (aOR = 0.70, 95% CI = 0.52, 0.95), and older patients (age 50-64 vs. 18-29: aOR = 0.64, 95% CI = 0.46, 0.88; age 65+ vs. 18-29: aOR = 0.36, 95% CI = 0.22, 0.57) were less likely to engage in treatment.

CONCLUSIONS:

Rates of initiation and engagement for OUD patients vary widely with noticeable room for improvement, particularly in this critical time of the opioid crisis. Targeting patient and system factors may improve health system performance, which is key to improving patient outcomes.

KEYWORDS:

HEDIS; Health services research; opioid use disorder; quality indicators; treatment

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