KT to build digital healthcare system on Trans-Siberian trains
May 24, 2018
- Telemedicine Pilot Project with Russian Railways & SNU Bundang Hospital
- Expansion of ICT-based Medical Treatment by the Health-Welfare Ministry
SEOUL, South Korea (May 24, 2018) – KT Corp. (KRX: 03200; NYSE: KT) announced today that it will install a telemedicine system on the trans-Siberian railway and facilitate digital healthcare solutions by connecting hospitals affiliated with the state-owned Russian Railways.
The pilot plan is part of the ICT-based Medical System Expansion Project spearheaded by the Korea Health Industry Development Institute, an agency of the Ministry of Health and Welfare. KT, South Korea’s leading telephone and Internet provider, also participated in the project in 2017, constructing a digital healthcare platform and solution for 12 hospitals in Kazakhstan.
KT and Russian Railways signed an agreement in December 2017 to jointly create a mobile diagnostics solution for primary care at hospitals in Russian rail stations as well as on trans-Siberian trains. Russian Railways, one of the world’s three largest rail transportation companies, manages 173 hospitals across Russia.
Under the agreement, KT will work with the Seoul National University Bundang Hospital to set up a telemedicine system for six hospitals, one in Moscow and five in various regions, by June. KT will supply a digital healthcare solution, and the SNU hospital will offer diagnostic advice to Korean and Russian medical staffs and assist in personnel training. The telemedicine system for trans-Siberian trains will be maintained until the end of this year.
Koh Yoon-Jeon, Chief of KT’s Future Business Development Unit, she expects that KT will successfully undertake the project with its outstanding capabilities accumulated in digital healthcare over the years.
“We further look forward to expand our endeavors in Russia, particularly in the fields of online medical care, healthcare platform, and ICT-based operations of diagnosis centers and convalescent homes,” Koh said. “To this end, we are advancing our expertise in big data analysis and artificial intelligence (AI) technology, and also accelerating our efforts to achieve cooperation in e-healthcare with local partners, including the state-owned Sberbank.”
Jheon Sang-Hoon, President of the Seoul National University Bundang Hospital, said, “The convergence of healthcare and information technology is a major trend these days, and our hospital has been making diverse efforts in this field to make our way into the global market. This will be a good opportunity to introduce Korea’s prowess in ICT in medicine.”
KT’s Digital Healthcare Solution and Platform won the Award for Best Mobile Innovation for Health at the Mobile World Congress 2018, held in February, in Barcelona, Spain
The trans-Siberian railway, operated by Russian Railways, connects Moscow and Vladivostok. A one-way trip takes seven days. Currently, the trans-Siberian trains only have basic diagnostic devices such as thermometers and blood pressure monitors. Therefore, it is difficult to check the condition of passengers who should be monitored closely such as pregnant women and heart patients or realize a passenger has an internal organ problem that requires urgent treatment.
KT plans to install its digital healthcare solution devices on the trains. They will enable rapid diagnosis with a small amount of blood and urine on such cardiovascular diseases as angina and myocardial infarction, as well as respiratory diseases, diabetes and prostate cancer. The solution also provides ultrasonography to diagnose kidneys, liver and gallbladder, fetal sonography, and basic blood and musculoskeletal system tests.
Notably, KT’s digital healthcare platform features mobile diagnostics software containing Bluetooth and Wi-Fi for management of diagnostic data and storage of patients’ medical history in the “mobile electronic medical record (Mobile EMR).” It also utilizes AI technology for precise and accurate diagnosis, as exhibited by chest X-ray interpretation based on machine learning.
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