JARGON is a patient-centered medical translation app designed to make communication
between providers and patients more valuable and efficient.

The most important role a doctor may play in the treatment of their patient is a communicator:
a deliverer of good news and bad news. At the core of every doctor-patient interaction lies the communication that is exchanged between them. In these messages, the patient entrusts their provider with their concerns and questions, and in return physicians offer their expertise and skills vital to their recovery. However, there are some patients who are unable to engage in this important dialogue necessary to take full advantage of our healthcare system. Even today, some patients experience a steep language barrier that obstruct their access to good healthcare. Through the development of a patient-centered medical translator, we hope to reduce that barrier.

JARGON was designed to elevate patients’ quality of experience by addressing communication
complications that may interfere with their medical treatment. The main feature of JARGON
includes a text translator that converts the input language to the target language, with a special
focus on identifying and translating medical terminology. By communicating with patients and
providing supplemental informational resources in their spoken language, we aim to increase
health literacy and facilitate independency.

In the future, we hope to integrate additional features to the app that will enhance the experience of patients, not only in the hospital but also at home. We hope to include a live communication application for the exchange of high-resolution medical images (e.g. CAT scan, MRI, X-ray), as well as a live-chat feature directly connected to their healthcare facility.

Acknowledgments: This project is supported by the US Ignite Smart Gigabit Communities Program that funded via NSF Cooperative Agreement CNS-1531046.

Team Information: University of Illinois at Urbana-Champaign, Ana Paik – Project Lead, [email protected]
Chieh-Li “Julian” Chin
Michael Haberman
Lisa Bievenue
Tracy Smith

Paik, A., Haberman, M., Chin, C., Bievenue, L., & Smith, T. (2018). Clarify: Advancement of AI in Medical Translations.

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