PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：有语言障碍的患者遇到医疗保健差异，这可以通过利用口译员技能来减少文化，语言,和识字障碍，通过改善双向交流。证据支持使用现场口译员，特别是涉及复杂护理需求的患者的互动。不幸的是,由于口译员短缺和临床医生对口译员的使用不足，有语言障碍的病人往往得不到他们需要或有权得到的语言服务。卫生信息技术（HIT），包括人工智能(AI)，有可能简化流程，提示临床医生使用现场口译员，和支持优先级。
方法：从2023年5月1日至2024年6月21日，一项单中心阶梯式楔形整群随机试验将在罗切斯特梅奥诊所圣玛丽医院和卫理公会医院的35个单位内进行。明尼苏达。这些单位包括医疗，外科,创伤,以及混合的ICU和医院楼层，可容纳急性内科和外科护理患者以及急诊科（ED）。研究阶段之间的过渡将以60天的间隔开始，导致12个月的研究期。对照组的单位将接受标准护理，并依靠临床医生主动要求口译服务。在干预组中，研究小组将每天生成一份有语言障碍的成年住院患者名单，根据其复杂性分数（从最高到最低）对列表进行排序，并与口译员服务分享，谁会向床边护士发送安全聊天消息。这种参与将由基于姑息治疗评分的预测性机器学习算法触发，辅以其他复杂性预测因素，包括住院时间和护理水平以及程序，事件,和临床笔记。
结论：这种务实的临床试验方法将把预测性机器学习算法集成到工作流程中，并评估干预的有效性。我们将比较对照组和干预组之间亲自口译员的使用情况和首次使用口译员的时间。
背景：NCT05860777。2023年5月16日。
方法：从2023年5月1日至2024年6月21日，一项单中心阶梯式楔形整群随机试验将在罗切斯特梅奥诊所圣玛丽医院和卫理公会医院的35个单位内进行。明尼苏达。这些单位包括医疗，外科,创伤,以及混合的ICU和医院楼层，可容纳急性内科和外科护理患者以及急诊科（ED）。研究阶段之间的过渡将以60天的间隔开始，导致12个月的研究期。对照组的单位将接受标准护理，并依靠临床医生主动要求口译服务。在干预组中，研究小组将每天生成一份有语言障碍的成年住院患者名单，根据其复杂性分数（从最高到最低）对列表进行排序，并与口译员服务分享，谁会向床边护士发送安全聊天消息。这种参与将由基于姑息治疗评分的预测性机器学习算法触发，辅以其他复杂性预测因素，包括住院时间和护理水平以及程序，事件,和临床笔记。
结论：这种务实的临床试验方法将把预测性机器学习算法集成到工作流程中，并评估干预的有效性。我们将比较对照组和干预组之间亲自口译员的使用情况和首次使用口译员的时间。
背景：NCT05860777。2023年5月16日。
