Accommodations and Interventions to Decrease Unnecessary ED Utilization in Patients with Limited English Proficiency

2021 
The purpose of this article is to explore the frequency with which mobile health (mHealth) interventions designed to keep patients out of the emergency department (ED) cater to low English proficiency (LEP) patients and whether there is an association between LEP accommodations and success of the intervention. This rapid review, a simplified version of a systematic review, captures trends in mHealth interventions with regard to inclusion of LEP patients. The authors selected studies that measured the effect of mHealth interventions on ED utilization through searches of PubMed and Scopus between January 2009 and May 2020. Subgroup comparisons were run for various study characteristics (e.g., participant demographics, mHealth features, outcomes, etc.) between the two subsets (LEP vs. non-LEP) using chi-square analysis. Assessment of ED utilization was categorical (i.e. increased, decreased, or no change) based on the index study analysis. A total of 78 articles were included. LEP studies reported certain patient demographics (e.g., ethnicity, income, education, etc.) more frequently than non-LEP studies. LEP studies also described a focus on underserved patient populations more than their counterparts and their mHealth interventions were more successful in achieving their ED utilization goals (80% vs. 45%, P = 0.04). Results show that most mHealth applications designed to decrease ED utilization do not include accommodations for LEP patients and that improved ED utilization is more common amongst interventions that contain LEP accommodations. This is key since LEP patients are disproportionately underserved, and thus, use the ED for non-acute care due to barriers in accessing other medical services. More research is needed on the capacity of mHealth to meet the unique needs of LEP patients which should be done through collaborations with software developers, providers, and patients from diverse backgrounds.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    41
    References
    0
    Citations
    NaN
    KQI
    []