Meeting the needs of the patient with <scp>non‐English</scp> language preference in the hospital setting

Brumbaugh, Jane E.; Tschida‐Reuter, Daniel J.; Barwise, Amelia K.

doi:10.1111/1475-6773.14217

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The United States healthcare system continues to face persistent challenges in providing medical care to an increasingly diverse and multilingual patient population [ 1 – 3 ]. Optimal approaches to providing sufficient and high-quality interpretation remains challenging [ 4 – 6 ]. Studies indicate significant disparities in the quality of care for individuals who have a non-English language preference (NELP) and complex medical conditions [ 7 – 17 ].…”

Section: Background and Significancementioning

confidence: 99%

Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs

Strechen,

Wilson,

Eltalhi

et al. 2024

Trials

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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. Discussion 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. Trial registration NCT05860777. May 16, 2023.

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Section: Background and Significancementioning

confidence: 99%

Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs

Strechen,

Wilson,

Eltalhi

et al. 2024

Trials

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Guidance on the Effective Collaboration With Interpreters and Translators in Speech-Language Pathology

Alani,

Boyer,

Harten

et al. 2024

Perspect ASHA SIGs

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Purpose: The purpose of this literature review is to guide speech-language pathologists (SLPs) on how to increase effective collaboration with interpreters and translators in order to enhance communication with clients and significant others from culturally and linguistically diverse backgrounds. Method: A review of definitions of interpretation and translations and practical guidance on certifications and collaborative practices within a health literacy framework approach is described. Strategies to build effective and ethical SLP–interpreter/translator working relationships are reviewed. Results: Findings emphasize the importance of interprofessional collaboration between SLPs and interpreters/translators. When working with professional interpreters/translators, SLPs application of a brief–intervention–debrief framework, use of plain language, and knowledge of local and national training and practice requirements, are identified as mechanisms to enhance effective and ethical collaboration. Conclusion: Effective collaboration with interpretation and translation services should be a key component of clinical practice to ensure that health information is clearly understood by clients and families.

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Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives

Barwise,

Curtis,

Diedrich

et al. 2023

Journal of the American Medical Informatics Association

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Objectives Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. Materials and methods This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. Results We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply–demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. Discussion This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. Conclusion Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

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Meeting the needs of the patient with non‐English language preference in the hospital setting

Cited by 3 publications

References 31 publications

Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs

Harnessing health information technology to promote equitable care for patients with limited English proficiency and complex care needs

Guidance on the Effective Collaboration With Interpreters and Translators in Speech-Language Pathology

Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives

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