Massachusetts Medical Society: MMS Information Technology Award: 2020 Winners

MMS Information Technology Award: 2020 Winners

Dan Chonde – RadTranslate

Category: Resident

COVID-19 has disproportionally affected Hispanic/Latinx populations, including those with limited English proficiency. During that first wave in Massachusetts, increased demand and shortages of interpreter services were prolonging image acquisition, reducing image quality, and limiting triage of suspected COVID patient with limited English proficiency at our ambulatory triage clinics.

To enhance multilingual care, we created a freely available web application (RadTranslate™) that provides multilingual radiology exam instructions. The device-agnostic web application leverages artificial-intelligence speech-to-text to provide standardized, human-like spoken exam instructions in the patient’s preferred language. The tool was successfully piloted at into our ambulatory clinic in Chelsea, Mass.

In the 63-day test period following launch, there were 1267 app uses across the United states, with all non-Spanish speaking technologists at our pilot clinic voluntarily switching exclusively to RadTranslate for Spanish-speaking patients. 100% of responding technologists agreed that RadTranslate was user friendly, reduced care delays, increased efficiency, and made their job easier.

Feyisayo Eweje – Critical Healthcare Information Integration Network

Category: Student

In rural and underdeveloped areas of Africa, community health workers play a crucial role as the first point of entry to the health system for millions of Africans. The COVID-19 pandemic has highlighted the importance of ensuring that these frontline workers are adequately trained and equipped with accurate medical reference information to guide their interactions with patients in the community. But while access to medical references among health workers has been shown to improve outcomes, on-demand access to medical references for CHWs is limited due to poor internet usage penetration on the continent.

The Critical Healthcare Information Integration Network (CHIIN) is a free SMS messaging system providing validated medical information on the causes, presentation, management, and associated complications of context-relevant diseases to community health workers practicing in rural settings. Our team synthesizes materials from well-respected online medical reference sources (e.g. Mayo Clinic disease guides, CDC and WHO) into digestible text messages to be sent to offline community health workers. Users register and interact with a SMS chatbot powered by Twilio, a cloud communications platform that enables developers to make and receive text messages. We internally parse the disease information into four categories and store it via Cloud Firestore, a cloud-hosted NoSQL database with which the chatbot interacts.

Our team is conducting a pilot of CHIIN with an NGO based in northern Nigeria in 2020.

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