Patients in South Yorkshire attempting to book doctor appointments over the phone are facing a surprising technological barrier: their own regional accents. A new artificial intelligence receptionist introduced at several medical practices in Rotherham has reportedly struggled to comprehend local Yorkshire dialects, leaving callers frustrated and forcing some to travel to surgeries in person.
The automated assistant, named Emma and developed by tech company QuantumLoopAI, was deployed to eliminate long phone queues by answering incoming calls instantly. While the technology promises seamless service, local health watchdog Healthwatch Rotherham has received complaints from residents who cannot get the system to understand their requests.
Kym Gleeson, manager at Healthwatch Rotherham, explained that the wide variety of regional twangs across South Yorkshire poses a major hurdle for the AI. "It seems the system isn't always able to understand, so that causes frustration," Gleeson noted. In one instance, a patient reported hanging up without booking an appointment after repeated failed attempts to communicate with the virtual receptionist.
The issue has raised particular concerns for older residents, military veterans, and individuals with disabilities or low digital literacy. According to Healthwatch, the difficulty of navigating the automated system has driven some patients to make physical trips to their GP surgeries instead of calling. The watchdog reminded medical practices of their legal duty to make reasonable adjustments for patients who require alternative methods to access healthcare services.
In response, QuantumLoopAI stated that Emma is trained to recognize a wide spectrum of dialects and supports 17 languages in addition to English. The company emphasized that the system does not make clinical decisions and is programmed to transfer calls to human staff whenever an inquiry cannot be resolved. Furthermore, callers can request a human staff member at any time.
Despite these safeguards, the trial highlights the ongoing challenges of deploying speech-recognition technology in regions with rich local speech patterns.