Author Biographical Info

Mireille Signe Autobiography

Mireille Signe, BSN, RN, is a Doctor of Nursing Practice (DNP) student in Texas and a registered nurse with clinical experience in acute care settings. Her professional interests center on quality improvement, evidence‑based practice, and health equity, with a particular emphasis on improving communication for patients with Limited English Proficiency (LEP).

Throughout her clinical training, Signe recognized the critical role that accurate, timely healthcare translation plays in patient safety, informed decision‑making, and overall patient satisfaction. This awareness shaped her scholarly focus and guided the development of her DNP quality improvement project, which addressed communication barriers in the acute care environment and examined strategies to enhance interpretation services for LEP populations. Her work reflects a commitment to culturally responsive care and to reducing disparities associated with language barriers.

Her academic efforts highlight the integration of informatics and system‑level interventions to support equitable healthcare delivery. She is particularly interested in leveraging innovative translation technologies to improve clinician–patient communication and promote patient‑centered outcomes in diverse populations.

Upon completion of the Doctor of Nursing Practice degree, Signe intends to continue advancing nursing practice through leadership and advocacy, with the goal of improving access, safety, and quality of care for linguistically underserved communities.

Date of Doctor of Nursing Practice Project Completion

Spring 5-1-2026

Faculty Advisor

Dr. Susan Alderman

Abstract

Purpose:

To evaluate whether artificial intelligence (AI) verbal translation improved communication-related satisfaction during routine nurse–patient interactions.

Background:

In the ICU, brief, frequent conversations with patients with Limited English Proficiency (LEP) often occur without timely access to professional interpreters, contributing to delays, improvised communication, and lower satisfaction. AI tools offer immediate access for low‑complexity exchanges but require evaluation for feasibility and effectiveness as a supplement to certified interpreter services.

Methodology:

Twelve nurse-LEP proxy patient dyads were evaluated using a mixed-methods design (8/2025-12/2025) in a 16-bed ICU during QI PDSA cycles. Standardized care communication scenarios were completed using the AI tool. Process and outcome included session completion, interaction time, communication‑related satisfaction, perceived accuracy, and nurse‑reported burden.

Results:

The project aimed to achieve a ≥20% improvement in communication‑related satisfaction compared with prior interpreter experiences. Satisfaction among proxy patients increased from 63.6% to 100%. Relative to the interpreter baseline mean score (n=8.3%), the AI‑supported sessions produced substantially higher mean satisfaction (n=63-100%), demonstrating marked improvement in perceived clarity and overall communication effectiveness. Qualitative feedback highlighted improved immediacy and workflow integration, with accent and language variability noted as limitations.

Implications:

An AI translation tool is a feasible adjunct for routine ICU communication with LEP patients, improving timeliness and clarity without replacing certified human interpreters for high‑risk discussions. Implementation should pair brief training with infrastructure readiness and ongoing monitoring to support sustained, equitable communication

Keywords: Artificial intelligence, patient communication, translation, ICU

Keywords

Artificial intelligence, patient communication, translation, ICU

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