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Detecting Language-Related Disparities in Spiritual Care Using Artificial Intelligence
Claire Stephenson1, Yuwen Ji1, Nahid Zeinali1,2
1The University of Iowa, USA.
Introduction:
Language barriers perpetuate health care disparities, yet traditional data sources overlook inequities in relationship‑based care. This study uses artificial intelligence to examine language‑related disparities in spiritual care, a domain largely documented in unstructured clinical notes.
Methods:
We analyzed electronic health records of 7,360 deceased adults aged ≥65 years from a tertiary hospital (2010-2023). Natural language processing identified spiritual care documentation, and structured flowsheets captured formal chaplain visits. Logistic regression assessed associations between patient language and spiritual care access and timing.
Results:
Non‑English speakers had 59% lower odds of receiving formal spiritual care (odds ratio = 0.41, p = .003) and received support nearly 10 months later (median 136 vs. 432 days, p = .020). Among non‑English speakers, non-chaplain providers more often first documented spiritual needs.
Discussion:
Artificial intelligence-supported analysis of unstructured documentation revealed language‑related disparities missed by structured data, offering a culturally grounded approach to identifying inequities in relationship‑based care.