DIGITAL DOCTOR:
The AI-powered telecare technology could recognize speech in multiple languages and quickly identify people who need hospital treatment most
National Taiwan University Hospital (NTUH) on Thursday presented its newly developed telecare system that uses artificial intelligence (AI) to predict the occurrence of patient-related emergency events by analyzing text conversations between patients and their live-in caregivers.
The “Intelligent Voice Emergency Prediction System of the Fourth-Generation Real-Time Synchronous Telehealth Care Platform” developed by NTUH and Chunghwa Telecom won the “21st National Innovation Award-Clinical Innovation Award” in November last year.
At a news conference, Ho Yi-lwun (何奕倫), director of the Department of Internal Medicine and Telecare Center at NTUH, said the system converts conversations between patients and caregivers into text, and could analyze keywords in conversations, such as blood pressure and other physiological data, for earlier predictions for emergency treatment, thereby reducing unnecessary complications in patient care.
Photo: Chiu Chih-jou, Taipei Times
Taking people with acute myocardial infarction as an example, Ho said if intervention occurs within the golden six hours for treatment, there is a chance occluded blood vessels could be recanalized.
The intelligent voice emergency prediction system could accurately identify people with emergency needs, distinguish between high and low-risk patients, and allow those with greatest need to receive necessary treatment quicker, he added.
This system would not only help reduce emergency room visit rates, but also the length of hospital stays and the risk of death, due to immediate medical responses, Ho added.
Hung Chi-sheng (洪啟盛), a doctor from NTUH’s Telehealth Center, said that since its establishment in August 2009, the Telecare Center has handled 540,000 phone calls and cared for more than 7,000 patients.
It cooperated with Chunghwa Telecom to introduce the telecom’s multi-language, medical speech recognition technology and conduct AI model training, which could recognize speech in Mandarin, English and Hoklo (also known as Taiwanese), Mandarin accents of migrant caregivers, medical terms, Chinese and English names of drugs, and convert the speech into text.
Through language processing and analysis of integrated nursing records, the AI system could monitor and predict the probability of people experiencing emergency events within two weeks of analysis, Hung said.
The system’s predictive accuracy rate is roughly 90 percent, he said.
Telecare could improve patients’ blood pressure, reduce mortality, reduce emergency room visits, and the number and length of hospital stays, Hung added.
Home-based telecare has become an important tool to address healthcare needs in Taiwan’s aging society, particularly during the COVID-19 pandemic, he said.
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