In the American healthcare industry, language access for patients with limited English proficiency (LEP) has been a hot-button issue for years. In many cases, mandates requiring healthcare providers to offer translation and interpretation to LEP patients have failed to bring medical outcomes for LEP populations up to par with those of non-LEP patients.

United Language Group (ULG) has dealt with this issue firsthand. Health-risk assessment forms can prevent unnecessary hospital readmissions — a problem that costs USD 17bn annually and occurs more frequently among LEP patients. 

So when a client wanted to increase the form completion rate among LEP patients, ULG’s translation services were a good start, but not enough to help the client achieve its end-goals.

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Speaking at SlatorCon Remote March 2022, ULG CEO Nic McMahon explained that this realization prompted ULG to transition to a new, outcome-driven method: Define the problem; identify contributing factors; and, where appropriate, implement AI and technology.

McMahon believes that language service providers (LSPs) will need to expand their solution offerings in order to bridge the gap to their clients’ desired outcomes. “I think as the technology develops, it’s an inevitable progression for our industry,” he added.

In the case of health-risk assessments and LEP patient engagement, ULG provided additional training for interpreters and adapted marketing to be culturally relevant to target communities.

As a result, the client saw a 23% higher rate of completion of health-risk assessment forms among LEP patients. 

The higher completion rate helped healthcare providers avoid fines while providing better support for patients — a win-win. And beyond the immediate impact, McMahon said, the data coming from completed health-risk assessment forms helped eliminate disaparities down the line.

Technology has played a more central role in ULG’s translating 8,000 medical claims per month. Much to the chagrin of highly qualified linguists, the client pushed for lower costs and tighter turnaround times. 

“A significant portion of the medical claims process didn’t need full human quality in terms of output,” McMahon said. “We needed to route it to a solution that would allow us to get the human quality at the right parts of that process.”

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ULG ultimately deployed 19 integrated AI systems, including machine translation, to create an automated workflow that led to a 60% reduction in time spent and 30% reduction in total costs on medical-claim translations.

Observing a shift to outcome-driven language solutions on a macro level is one thing, but the work of bringing it to life at an individual LSP is ongoing, according to McMahon.

“You must create a feedback loop that allows you to go back into the system and say, ‘OK, where are we meeting these outcome objectives? And where are we falling short?’” he said.

Watch “Driving Outcomes Beyond Words Alone” with Nic McMahon, and the full SlatorCon Remote March 2022 event, on demand here.



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