Twenty-five million people in the USA have limited English skills (LEP). Despite the rising numbers of patients with limited English proficiency (LEP), our healthcare system keeps letting down this group when it comes to interpretation. As LEP patients get worse care because of language obstacles, we need to look at whether new tech can fix this long-standing issue.
In this piece, we’ll delve into the problems language barriers create for LEP patients, how AI language models might help solve this, and what work lies ahead to make this solution safe and real.
A look into orthopedic clinics in California revealed that a mystery shopper “patient” was told to use an unqualified interpreter at 80% of clinics and to bring along a friend or family member to interpret at 28% of clinics. A 2023 report on pediatric hospitalists showed that just 65% of those surveyed always used interpreters for admissions, 57% for discharges, and 40% during rounds. These findings point to the fact that most clinical encounters do not involve professional interpreters.
Given the importance of interpretation for quality care, why isn’t it used more? Cost often creates a barrier for smaller, community-based clinics and free clinics that take care of marginalized populations. Telephone interpreters cost between $1.25 and $3 per minute, video interpreters range from $1.95 to $3.49 per minute, and in-person interpreters charge $45 to $150 per hour.
14 states offer reimbursement for interpreter costs through funded insurance programs like Medicaid and the Children’s Health Insurance Program (CHIP).
New AI tools are making a big difference in how we get interpreters. They’re building on what tech has already done. Now you can call or video chat with interpreters for tons of languages right away. It’s quicker and cheaper even for languages not many people speak.
Online courses make it easier to become a certified translator so we’re getting more interpreters. In places with very few resources, people from all over volunteer to help refugees and immigrants understand things.
New breakthroughs in AI language models, the widespread use of smartphones by healthcare providers, and the resulting chance to automate bedside translations could help overcome these obstacles.
However, we can’t leave our patients without any way to communicate while we wait for better access to traditional interpreting services. Interpreter services and effect on healthcare – a systematic review of the impact of different types of interpreters on patient outcome.
Still, AI language models offer answers that can boost health fairness while these system-wide changes take place. Having auto-translate on doctors’ phones changing words in real time between doctor and patient, gives an answer, without patients needing smartphones, to close the gap in getting and using interpreters.
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