Project Details -

Ongoing

Communication problems in clinical settings can lead to poor health outcomes and increased hospitalization, especially among patients with limited English proficiency (LEP)--those who have a limited ability to read, write, or understand English. Evidence strongly suggests that medical interpreters improve communication, patient satisfaction, and clinical outcomes when incorporated into care. In-person medical interpretation reduces cultural, linguistic, and literacy barriers, supporting more equitable care. Despite this evidence, interpreters in healthcare are often underutilized. As a result, patients with LEP and complex care needs are significantly impacted, receiving lower-quality care than patients who are proficient in English. There is a critical need to prioritize interpreter engagement to support decision making and enhance patient-centered care for LEP patients with complex care needs.

There are substantial gaps in clinical operations to increase interpreter use. This research aims to promote equitable care for patients with LEP and complex care needs by increasing the utilization of interpretation services. The research team will develop an application that identifies patients with LEP and complex care needs to increase appropriate and timely interpreter use utilizing digital healthcare technology.

The specific aims of the research are as follows:

Engage stakeholders in testing and validating a health information-based screening tool or “sniffer” to reliably identify patients with LEP and complex care needs.
Engage stakeholders in designing an effective process for proactively reaching patients with unmet interpreter needs early in their hospitalization.
Examine the preliminary effectiveness of the developed intervention on interpreter use and time to interpreter use among patients with LEP and complex care needs using a stepped-wedge cluster randomized trial.

The research team will codesign the technology with participant stakeholders, recruiting clinicians, and interpreters to build a process to connect the patients identified with the tool to interpreter services. A machine learning predictive analytic framework used in a former study will be modified in this application to identify patients with LEP and complex care needs. Taking place in the Mayo Clinic Hospital-Rochester, the deployment of this application in clinical workflow will allow for stakeholder feedback and evaluation in a real-life setting. The research team hypothesizes this intervention will increase appropriate and timely interpreter use among patients with LEP and complex care needs.

A Machine Learning Algorithm to Improve the Use of Interpreters for Hospitalized Patients with Complex Care Needs

Theme:

Supporting Health Systems in Advancing Care Delivery

Subtheme:

Improving Equity in Healthcare with Digital Healthcare Solutions

A machine learning, predictive analytic intervention has the potential to improve healthcare, making it more equitable for patients with a non-English language preference and complex care needs by supporting timely interpreter use to facilitate decision making and promote patient-centered care.

In-person interpreters improve communication between patients and their families and providers, supporting more equitable care

For hospitalized non-English language preference (NELP) patients, especially those with complex care needs, communication between patients and their families and inpatient healthcare providers is often difficult, and these communication barriers can lead to lower quality of care, poor health outcomes, and increased length of hospital stay. In-person medical interpretation in a patient’s preferred language improves communication, patient satisfaction, and clinical outcomes. Moreover, using in-person interpreters reduces cultural, linguistic, and literacy barriers, supporting more equitable care.

“Interpreters can help patients and clinicians understand each other and relay information, but they can also help inform the clinicians about potential other background issues that might be important, so clinicians have a better understanding of why decisions are being made, or which people in the family are likely to be important for decision making.”- Dr. Amelia Barwise

Complex medical conversations are hard, even among English-language preferred speakers

Using in-person interpreters encounters several barriers, beyond the overall nationwide shortage. Getting an interpreter to the bedside takes substantial coordination and logistics, and some clinical providers are hesitant to engage with that process in busy hospital environments in which they are likely caring for multiple patients. Frequently, providers make do with using family members to interpret or their own limited language skills, but that is not ideal.

“Families can either not understand all of the medical jargon, so there's the accuracy piece, but then they can also withhold information.”- Dr. Amelia Barwise

An interpreter is critical to having these conversations, especially as they relate to treatment, end-of-life care or withdrawal of care, or to any other sensitive discussions that are already difficult among English-language preferred speakers.

Dr. Barwise and her research team in Mayo Clinic Rochester want to find a better way to identify which patients need an interpreter and prioritize the use of interpreters for these patients. The team is developing machine learning algorithms to reliably identify NELP patients with complex care needs early in their hospitalization.

Use of machine learning algorithms will improve appropriateness and timeliness of using interpreters for patients who need it most

The team is developing the tool with input from clinicians and interpreters to build a process to connect identified patients with timely interpreter services. Once developed, they will integrate the tool into the clinical workflow at Mayo Clinic Rochester and study whether use of the tool increases appropriate and timely interpreter use among NELP patients with complex care needs. In addition to using the tool to identify when to provide an interpreter, they also hope to normalize the use of interpreters to reduce cultural, linguistic, and literacy barriers, and in doing so, support more equitable care.