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Digital Health Innovation on Display in Taiwan

Five medtech brands from Taiwan presented their latest digital health innovations in a webinar organised by Taiwan External Trade Development Council (TAITRA), Taiwan’s leading trade promotion organisation, and Taiwan’s Bureau of Foreign Trade, Ministry of Economic Affairs (MOEA).

A year into the pandemic, Taiwan remains as one of the best-managed societies against Covid-19, having one of the lowest mortality rates and one of the lowest infection rates, partially based on Taiwan’s digital health development.

The purpose of the webinar was to spotlight the country’s strengths and to call for a healthcare rethink, highlighting the need to enable and improve the quality of healthcare through technology. Taiwan has managed to have a fully digitised healthcare system, interlinking all hospitals, clinics and related government administrations. This technologically advanced environment has helped Taiwan develop the most cost-efficient and high-quality medical devices and software, ideal for the future of healthcare.

The smart medical solutions being displayed are suitable for health facilities and communities. Namely, the product launch featured Leltek’s handheld wireless ultrasound technology, Wincomm’s Medical AI Panel PC, Acer Healthcare’s AI-assisted diagnostic software for diabetic retinopathy, MiiS’s medical image solution, and Apollo Medical Optics’ OCT (Optical Coherence Tomography) solution, a non-invasive skin analysis device.

According to an article, the President of Taiwan states that the government will accelerate the digital transformation of Taiwan’s healthcare and secure enough supplies of key raw materials to build up the country’s healthcare capability.

Taiwan is leading the world in terms of digital transformation in healthcare and will integrate various types of technology such as the Artificial Intelligence of Things (AIoT), real-time communications and cloud computing to build Taiwan into a global digital healthcare innovation hub.

The country will also ensure that it has sufficient key raw materials and critical components necessary for the production of medical testing devices, medical products, pharmaceutical ingredients and vaccines to cope with the COVID-19 pandemic. The government will also accelerate its sustainability assessment of critical raw materials.

As reported by Opengov Asia, Taiwan’s adoption of machine learning (ML) to detect medication errors is an example of the nation’s commitment to digital transformation in healthcare. ML can help doctors to make better decisions and improve patient safety and quality of care. The results were recently published in the Journal of Medical Internet Research – Medical Informatics.

Medication errors are a growing financial and healthcare burden that results in economic costs. Medication errors can occur during any stage of the medication process, including prescribing, dispensing, administration, and monitoring, with errors in prescribing accounting for 50% of the total.

When medicating patients, physicians go through complex decision-making processes to accurately write a prescription. First, they must clearly define the patient’s problem and list the therapeutic objective before selecting an appropriate drug therapy based on age, gender, and possible allergies. They must also consider dosing, drug-drug interaction, potential discontinuation of the drug, drug cost, and other therapies — and all of these need to be done instantly and simultaneously.

Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. Hence, ML can provide insights on patterns and predictions to help doctors make data-driven decisions. For technology to assist in solving these problems, ML learning must understand these variables. For this to be successful, data must be properly collected, organised, and maintained.

Data-driven medicine demands huge and diverse medical data sets. The biggest challenge is successfully implementing data-driven applications in clinical practice, from local to global, without compromising patient safety and privacy.

The system can immediately provide adaptive suggestions to help the doctor better complete the prescription whenever physicians prescribe diagnoses or medications that cannot be explained. The new model has been deployed in several hospitals and has since been expanded to the eastern and western United States to catch medication errors before they make an impact.

The result is a breakthrough in the international transferability of medical AI. It demonstrates a way to provide practical data-driven prescribing support to improve patient safety even though it could be challenging to obtain data to develop these systems locally.

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