BACKGROUND: Patients with language barriers encounter healthcare disparities, which may be alleviated by leveraging interpreter skills to reduce cultural, language, and literacy barriers through improved bidirectional communication. Evidence supports the use of in-person interpreters, especially for interactions involving patients with complex care needs. Unfortunately, due to interpreter shortages and clinician underuse of interpreters, patients with language barriers frequently do not get the language services they need or are entitled to. Health information technologies (HIT), including artificial intelligence (AI), have the potential to streamline processes, prompt clinicians to utilize in-person interpreters, and support prioritization.
METHODS: From May 1, 2023, to June 21, 2024, a single-center stepped wedge cluster randomized trial will be conducted within 35 units of Saint Marys Hospital & Methodist Hospital at Mayo Clinic in Rochester, Minnesota. The units include medical, surgical, trauma, and mixed ICUs and hospital floors that admit acute medical and surgical care patients as well as the emergency department (ED). The transitions between study phases will be initiated at 60-day intervals resulting in a 12-month study period. Units in the control group will receive standard care and rely on clinician initiative to request interpreter services. In the intervention group, the study team will generate a daily list of adult inpatients with language barriers, order the list based on their complexity scores (from highest to lowest), and share it with interpreter services, who will send a secure chat message to the bedside nurse. This engagement will be triggered by a predictive machine-learning algorithm based on a palliative care score, supplemented by other predictors of complexity including length of stay and level of care as well as procedures, events, and clinical notes.
CONCLUSIONS: This pragmatic clinical trial approach will integrate a predictive machine-learning algorithm into a workflow process and evaluate the effectiveness of the intervention. We will compare the use of in-person interpreters and time to first interpreter use between the control and intervention groups.
BACKGROUND: NCT05860777. May 16, 2023.

BACKGROUND: Latine people, comprising 18.5% of the US population, constitute the largest ethnic minority group, with nearly one-third self-identifying as having non-English language preference (NELP). Despite the importance of the telephone in health care access, there is limited understanding of how NELP patients navigate telephone calls with primary and specialty care clinics.
OBJECTIVE: This qualitative study aims to capture how Spanish speakers with NELP characterize their telephone call experiences with primary and specialty care clinics.
METHODS: Semi-structured interviews were conducted with 24 Spanish-speaking participants from primary care clinics with a sizeable proportion of patients who prefer to communicate in a language other than English at an urban academic medical center in Boston, MA.
METHODS: Participants were selected from primary care clinics that were well-equipped to serve Spanish-speaking patients. A total of 24 Spanish-speaking patients with NELP, mainly women (83%), with a mean age of 55.8 years, participated. They represented diverse countries of origin, with an average length of time in the USA of 21.7 years.
METHODS: Interview questions prompted participants to describe their telephone call experiences with front desk staff, with attention to interpreter availability, ancillary assistance, health outcomes stemming from a lack of language services, and emotional consequences of language discordance on calls.
RESULTS: Patients perceived primary care clinics as providing familiarity and language concordance during telephone interactions, contrasting with specialty care clinics, seen as sources of monolingual English communication. Participants utilized various strategies, such as requesting interpreters, using concise English phrases, or seeking assistance from acquaintances, relatives, or primary care clinic staff, to mitigate language barriers.
CONCLUSIONS: The findings underscore significant challenges faced by Spanish-speaking patients with NELP in ambulatory specialty care telephone calls. The study emphasizes the importance of creating inclusive multilingual telephone environments, standardizing interpreter access, and reflecting the diversity of the communities served.

In 2019, a new variant of coronavirus, SARS-CoV-2 (COVID-19) created a global pandemic that has highlighted and exacerbated health disparities. Educating the general public about COVID-19 is one of the primary mitigation strategies amongst health professionals. English is not the preferred language for an estimated 22% of the United States population making effective mass communication efforts difficult to achieve. This study seeks to understand and compare several topics surrounding COVID-19 health communication and healthcare disparities between individuals with English language preference (ELP) and non-English language preference (NELP) within the United States. A survey available in seven languages asking about knowledge and opinions on COVID-19, vaccines, preferred sources of health information, and other questions, was administered February-April 2021 to patients at an urban federally qualified health center that also serves global refugees and immigrants. Descriptive statistics and comparative analysis were performed to identify differences between ELP and NELP individuals. Analysis of 144 surveys, 33 of which were NELP, showed 90.97% of all patients agreed that COVID-19 was a serious disease and 66.67% would receive the COVID-19 vaccine. There were numerous differences between ELP and NELP individuals, including trust in government, symptom identification, preferred source of health information, and feelings that cultural needs had been met. This study has identified several significant differences in patient perceptions relating to the COVID-19 pandemic when comparing NELP to ELP and highlighted areas where improvement can occur. Applying this information, easily utilized targeted resources can be created to quickly intervene and address health disparities among patients seeking care at an urban community health center.