AIM: Nurses assess patients pain using several validated tools. It is not known what disparities exist in pain assessment for medicine inpatients. Our purpose was to measure differences in pain assessment across patient characteristics, including race, ethnicity, and language status. METHODS: Retrospective cohort study of adult general medicine inpatients from 2013 to 2021. The primary exposures were race/ethnicity and limited English proficiency (LEP) status. The primary outcomes were 1) the type and odds of which pain assessment tool nursing used and 2) the relationship between pain assessments and daily opioid administration. RESULTS: Of 51,602 patient hospitalizations, 46.1% were white, 17.4% Black, 16.5% Asian, and 13.2% Latino. 13.2% of patients had LEP. The most common pain assessment tool was the Numeric Rating Scale (68.1%), followed by the Verbal Descriptor Scale (23.7%). Asian patients and patients with LEP were less likely to have their pain documented numerically. In multivariable logistic regression, patients with LEP (OR 0.61, 95% CI 0.58-0.65) and Asian patients (OR 0.74, 95% CI 0.70-0.78) had the lowest odds of numeric ratings. Latino, Multi-Racial, and patients classified as Other also had lower odds than white patients of numeric ratings. Asian patients and patients with LEP received the fewest daily opioids across all pain assessment categories. CONCLUSIONS: Asian patients and patients with LEP were less likely than other patient groups to have a numeric pain assessment and received the fewest opioids. These inequities may serve as the basis for the development of equitable pain assessment protocols.

Background

Patients experiencing homelessness have higher rates of substance use and related mortality, often driven by opioid overdose. Conversely, opioid use disorder (OUD) is a leading risk factor for homelessness. Our goal was to test the efficacy of an electronic health record (EHR) screen in identifying this vulnerable population during hospitalization and to assess the feasibility of a bundled intervention in improving opioid safety.

Methods

We assessed patients' housing status, substance use, previous MOUD treatment, barriers to MOUD treatment and readiness to take MOUD in and out of the hospital. For each post discharge follow up call, patients were asked about their MOUD status, barriers accessing treatment, current substance use, and housing status. We also assessed team members perceptions and experiences of the study.

Results

We enrolled 32 patients with housing insecurity and OUD. The mean age was 44, the majority self-identified as male (78%), and mostly as White (56%) or Black (38%). At each follow up within the 6-months post-discharge, reach rates were low: 40% of enrollees answered at least 1 call and the highest reach rate (31% of patients) occurred at week 4. At the third and sixth-month follow ups, >50% of subjects still taking MOUD were also using opioids.

Conclusion

Our clinician augmented EHR screen accurately identified inpatients experiencing OUD and PEH. This intervention showed high rates of attrition among enrolled patients, even after providing cellphones. The majority of patients who were reached remained adherent to MOUD though they reported significant barriers.