A study has demonstrated the international transferability of a Taiwanese artificial intelligence model for detecting medication errors in EHR systems in the United States.

The study was jointly conducted by Taiwan-based medical AI startup Aesop Technology, Taipei Medical University, Harvard Medical School and Brigham and Women's Hospital. Its results were announced last week in a press release.

WHY IT MATTERS

The "biggest challenge" in data-driven medicine is the successful implementation of data-driven applications in clinical practice from local to global settings without compromising patient safety and privacy, according to Dr Yu-Chuan Jack Li, a professor at Taipei Medical University.

The study, whose findings were published in the Journal of Medical Internet Research - Medical Informatics in January, found "good" transferability of Aesop's machine learning model in the EHR systems of two training schools under Harvard Medical School – Brigham and Women’s Hospital and Massachusetts General Hospital.

A federated learning (FL) approach was applied to the model which enhanced its performance. The said approach is an emerging technique that addresses the issues of isolated data islands and privacy. 

"FL provides the solution by training algorithms collaboratively without exchanging the data itself," Dr Yu-Chuan said. 

"The study has shown that the model trained by federated learning achieves remarkable performance comparable to the other two models trained by individual data sets," Aesop Technology co-founder and CEO Jim Long also said.

Incorporated in Aesop's MedGuard system, the AI medication safety model was trained using the 1.3 billion prescription data set from the National Health Insurance Administration in Taiwan.

In the statement, Aesop said its system can "immediately" provide adaptive suggestions to help doctors better complete their prescriptions. The AI model has since been expanded to eastern and western hospitals in the US.

THE LARGER TREND

Despite the wide adoption and optimisation of EHR systems in US hospitals, those systems still pose risks given varying safety performance, according to a 2020 study by researchers from the University of Utah and the Brigham and Women’s Hospital.

Medical errors are costing the US around $20 billion each year, leading to over 250,000 deaths. These can occur at any stage of the medication process with errors in prescribing happening half of the time.

The use of an AI system in preventing medication errors was already validated as early as 2017 by researchers at Harvard Medical School. In the same year, MedAware, the Israel-based startup which developed the algorithmic system, raised $8 million to scale its AI-powered solutions.

ON THE RECORD

"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. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham and Women's Hospital and Professor of Medicine at Harvard Medical School.

​New study paves the way for collaboration on artificial intelligence modeling and medication error reduction globally

Researchers at Harvard Medical School, Brigham and Women's Hospital, Taipei Medical University, and Aesop Technology, a Taiwan-based startup, announced today the results of a new joint study into the international transferability of machine learning (ML) models to detect medication errors. The results were recently published in the Journal of Medical Internet Research - Medical Informatics.

Working to Reduce Medication Errors

Medication errors are a growing financial and healthcare burden that results in economic costs of around US$ 20 billion and more than 250,000 deaths annually in the U.S. alone.

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. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr. David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham & Women's Hospital and Professor of Medicine at Harvard Medical School.

For technology to assist in solving these problems, it is critical that machine learning understands these variables. For this to be successful, data must be properly collected, organized, and maintained.

Taiwan is one of the world's few countries with a centralized and well-structured electronic health records (EHRs) system organized by Taiwan's National Health Insurance Administration. This gives it a competitive edge in developing medical AI systems that use machine learning based on medical record data.

The Future of Healthcare: Global Collaborative Intelligence

The study was conducted in partnership with Harvard Medical School, Taipei Medical University, and Aesop, the first federated learning model for preventing medication errors that are optimized by combining models from multiple countries.

"Our AI model for medication safety has been trained by one of the world's largest prescription databases, 1.5 billion well-coded prescriptions from the U.S. and Taiwan, to learn the association between diagnosis, medication, and complex prescribing behavior of doctors from different countries. The study has shown the model trained by federated learning (FL) achieves remarkable performance comparable to the other two models trained by individual data sets," said Jim Long, CEO, and Co-founder of Aesop Technology.

Through implementing, 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.

"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. FL provides the solution by training algorithms collaboratively without exchanging the data itself." said Dr. Yu-Chuan Jack Li, President-elected of the International Medical Informatics Association (IMIA) and Distinguished Professor of Taipei Medical University.

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 in the U.S., even though it could be challenging to obtain data to develop these systems locally.

Referred from: yahoo finance

Digging for data

Any AI system needs to learn from data – the more the better. Taiwan has a trove of data to feed on thanks to its National Health Insurance (NHI)system, which was launched in 1995 and covers more than 99% of the population. In recent years, the government has allowed access to this extensive data for research and development.

One startup to take advantage of this opportunity is AESOP Technology, which has offices in Taipei, Berkeley, California, and Cambridge, Massachusetts. AESOP is trying to reduce a major risk to patients’ health globally: medical errors, specifically prescription errors. In Taiwan, they analyzed NHI data and found at least 3 million incorrect prescriptions each year, from incorrectly filled out forms to prescribing the wrong drugs or dosages.

“Everyone thinks hospitals are high-tech, but in hospitals, the staff records patients’ status on white boards,” said Jim Long, CEO of AESOP. “Doctors spell drug names wrong – even common drug names that sound like and look like other drug names.”
Traditionally, the industry has used rule-based reminders, which look at one or two variables – such as whether two specific drugs are prescribed together.

A lot of noisy alerts are generated that physicians often ignore or turn off. AESOP is unique in that it considers many more variables – from four to more than 20 – when deciding whether to send an alert. These include a patient’s age, gender, the hospital department involved, and the diagnosis. 

Its system uses machine-learning to flag outliers, signaling a potential mistake. Long and his business partner founded AESOP – a spin-off startup from Taipei Medical University – last year but had first started trying to address the problem of prescription errors 20 years ago.

Two developments over the past five years have been instrumental in making this technology possible, says Long. 

The first is greater computational power. Given that there are 60,000 possible clinical diagnoses adjusted for patients’ gender, ages, medication, and so on, just five years ago Long and his team estimated that “to finish a round of machine learning would take 150 years.” 

The second key change was the opening of Taiwan’s NHI Administration’s database to biotechnology and healthcare firms through academic research cooperation. Since 2016, such access to anonymized healthcare data for Taiwan’s population going back to 1995 has been available upon application. This is much more than the maximum 50 million prescriptions AESOP is allowed to access and purchase from public sources in the U.S.,Long said.

In a bid to protect privacy, researchers and companies are not allowed to take any notes inside the data center in Nangang. They can only use the center’s computers and then provide the result of their searches to the staff, who then check it and send it to them.

AESOP’s MedGuard system has been in use in three hospitals in Taiwan since 2017. This year, the company has signed up more hospitals in Taiwan, and physicians at Harvard Medical School’s Brigham and Women’s Hospital are also giving the system a trial.

“Our target will still be the U.S.,” says Long. The U.S. is a bigger market than Taiwan and uses the same drug coding system and language when prescribing, he says. One prescription error it caught in Taiwan involved a 9-year-old girl with lower back pain.  

The physician had meant to prescribe Solaxin, a muscle relaxant. Instead, he chose Solian, an antipsychotic used to treat schizophrenia. The drug should not be prescribed to children and the dosage far exceeded the maximum recommended amount.

AESOP’s MedGuard system flagged the error when it recognized that the drug wasn’t being prescribed as it had been in the past.

Berkeley SkyDeck, UC Berkeley's startup accelerator, today hosts its Demo Day featuring 21 innovative global startups specializing in a wide range of industries including AI, enterprise software, robotics, health, bioscience, and consumer solutions. Due to the COVID-19 pandemic, this is the first time SkyDeck's Demo Day will take place virtually.

In fact, instead of coming together in the Bay Area, this cohort is the first that ever participated in the entire accelerator program remotely from their home locations -- around the U.S. and around the world from such countries as Armenia, Canada, Chile, India, Israel, Russia, Taiwan, and Turkey.

"This year has been one like no other with many challenges, and I could not be more proud of our startups for their grit, determination, and perseverance to succeed," said Caroline Winnett, Executive Director, Berkeley SkyDeck. "Major challenges force us all to be even more innovative and creative in the way we problem-solve. We definitely saw this in our founders."

"We recognized early on that startup founders would be concerned about their ability to fundraise while COVID-19 continues to wreak havoc on the economy. To ensure our startups would have a leg up, we doubled down and have been working even more closely with them to ensure that they are getting strong access to top tier investors that care about what they are building," said Chon Tang, Founding Partner of the Berkeley SkyDeck Fund.

AESOP Technology

AESOP Technology reduces medication errors using machine learning run on more than 2 billion prescriptions. They do this by providing decision support and reporting that flags inappropriate prescriptions, where the medications prescribed do not match the diagnosis, age, and gender of the patient. They also supply a medication and diagnosis suggestion engine that is being used to support intelligent workflow tools inside Electronic Patient Record systems.

Referred from: yahoo finance

TAITRA launched the first #InnoVEXOnlineDemo through COMPUTEX Facebook and YouTube channels today to show the achievements of tech startups in Taiwan. Partnering with MOST (The Ministry of Science and Technology) of Taiwan, TAITRA invited five outstanding startups to demonstrate their innovative solutions of healthcare, AI, image analysis and blockchain at #InnoVEXOnlineDemo.

The COVID-19 pandemic sparked off crisis to the world, but it also acts as a massive disruptor that precipitates new opportunities for tech startups to thrive. According to Numbeo, Taiwan ranks #1 in the world’s health care index and has built a solid value chain in the biomedical industry. #InnoVEXOnlineDemo hence presents three medical startups in Taiwan with different solutions.

• AESOP Technology provides medical prescription suggestions and alerting software via MedGuard, an AI system powered by machine learning and big data that prevents medication errors using knowledge of 1.3 billion prescriptions from the National Taiwan Healthcare System. MedGuard was granted SaMD clearance by FDA in September, 2019.

 

• WiseLongCare gives introduction of AccuFitness, an APP featuring smart AI prediction and digital solutions for the health industry. AccuFitness is the very first patented “Digital Twin” solution that takes 3D body measurements effectively through deep learning. It has been adopted by hospitals, healthcare centers and gyms to help users achieve their diet and exercise goals.

 

• iWEECARE presents Temp Pal, a smart thermometer to detect the temperature of the individuals and give insights on body function in the application platform connected via bluetooth. The APP allows users to track their medication usage and temperature changes and thus to manage their health conditions. It also enables users to share their medical records and health data with their healthcare professionals.

 
Moreover, tech startups focusing on unmanned systems and touchless economies are booming amid the COVID-19 pandemic as well.
​

• All Aspect System is one of the early developers for the open source UAV and has developed unmanned systems, including air, land, surface and under surface vehicles, for infrastructure and environmental inspection. The company is the first and the only one that is able to quantify the photos taken from UAVs with less than 1mm and accuracy of 5%.

 

• Dapp Pocket presents Cappuu, a smart wallet APP that enables the simplest way to buy and earn Stablecoin based on Ethereum blockchain.

By hosting online events such as #COMPUTEXOnlineTalks and #InnoVEXOnlineDemo, TAITRA devotes to build an O2O platform to connect the tech industries and to engage with all stakeholders despite the impact of the COVID-19 pandemic. For more information about next edition of COMPUTEX and the upcoming online events:

COMPUTEX Facebook
COMPUTEX YouTube channel
COMPUTEX website
InnoVEX website
COMPUTEX Twitter

Reference: COMPUTEX

​​Taipei, Jan. 6 (CNA) A delegation of 28 Taiwanese startups organized by the Ministry of Science and Technology (MOST) will attend the 2020 Consumer Electronics Show (CES) in Las Vegas to gain exposure for their brands, the ministry said Monday.

The 28 startups are largely involved in providing solutions in health care, internet security and smart city development, and their presence at the upcoming CES will demonstrate Taiwan's capability for innovation, the ministry said in a statement. The trade show, to be held from Jan. 7 to Jan. 10, will welcome about 4,500 tech exhibitors from all over the world.

Among the Taiwanese startups are sink care product vendor AESOP Technology, health care services providers FaceHeart Inc. and Ahead, smart transportation control solutions provider 3 Drens, and Language Hero, which specializes in artificial intelligence of things (AIoT) technologies. The ministry said the 28 start-ups will largely come from its Taiwan Tech Arena (TTA) initiative, which is aimed at cultivating tech startups by helping them raise funds to grow.

Before attending the CES, the delegation held a 2020 Taiwan Demo Day in Silicon Valley on Jan. 3, and the TTA program invited potential investors to meet with the startups, the ministry said. Deputy Science Minister Hsu Yu-chin said in the statement that the 2020 Taiwan Demo Day focused on innovation and matchmaking strategies in the United States and Taiwan and strengthened the startups' chances of extending their reach globally.

The 28 startups also took advantage of the event in Silicon Valley to demonstrate their technologies as a warm-up for CES, Hsu said. The government-sponsored Industrial Technology Research Institute (ITRI) said, meanwhile, that it will also attend the 2020 CES and exhibit its latest artificial intelligence applications for use in health care, smart robots and smart displays.

In December, the National Development Council (NDC), the top economic planning agency in Taiwan, said the country's national logo, Startup Island TAIWAN, will be displayed globally for the first time at the 2020 CES. Taiwanese startup companies showcasing their innovations at the CES will unanimously carry the logo in a bid to promote Taiwan, the NDC said.

(By Wu Po-wei, Chang Chien-chung and Frances Huang)

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Reference: FOCUS TAIWAN

​Medical errors are the 3rd leading cause of death. One in four patients are harmed while receiving healthcare. Medication errors cost a lot and kill a lot. They are a costly but preventable problem. MedGuard, a system developed using 1.3 billion medical big data and artificial intelligence, helps to prevent medication errors. MedGuard delivers proactive protection and recommendations and has been tested in a live clinical trial at three teaching hospitals.

​Medical errors were the third leading cause of death after heart disease and cancer in 2013, and the eighth leading cause of death in 1999. Unsafe medication practices and medication errors are leading causes of injury and avoidable harm in health care systems around the world. According to a WHO report, the estimated annual cost associated with medication errors is US$42 billion. One in four patients are harmed while receiving healthcare. Prescription errors happen every two seconds. The World Health Organization launched a global effort to halve medication-related errors in 5 years, 51% of which are generated by prescription. Computerized physician order entry systems (CPOE) have been widely implemented in Taiwan and in most developed countries. A CPOE is the most critical and useful point to prevent medication errors.

​For example, diabetic patients should be prescribed Euglucon (to lower blood sugar) but Euclidan (to dilate blood vessels) was mistakenly prescribed more than 1,000 times in one hospital within one month; this error has potentially happened in other hospitals. In order to decrease medication errors or near misses, numerous hospitals have developed a rule-based function on CPOE to clinically support physicians when they submit prescriptions. However, there are limitations to its use. For example, there is a 50-90% override rate for alerts, and it is not cost-effective; in other words, it is entirely useless to physicians.

​Prof. Yu-Chuan Jack Li, the Dean of the College of Medical Science and Technology, Taipei Medical University, has dedicated himself to improving patient safety and quality for almost 30 years. He has started a Medical AI company, AESOP Technology, and has developed MedGuard, a system based on 1.3 billion medical big data and artificial intelligence (we call it appropriateness of prescription or AOP and have many publications in known journals), as an optimal solution for medication errors. It has been tested in a live clinical trial at three teaching hospitals.

​Besides direct cost results from errors, hospitals always suffer from punitive fees from the National Health Insurance Bureau (NHI), which is the single payer insurance agency in Taiwan, with an annual average of NT$5 million. For example, a young girl with a cervical problem was prescribed Filgrastim, which was defined as inappropriate by NHI, resulting in a NT$6.6 million fine for a regional hospital. With MedGuard, such errors can be reduced by highlighting inappropriate medication and potential missing diagnoses, and thus the quality of the medical records can be improved, or errors prevented and costs saved. Simulation based on real reminder analysis shows that MedGuard saves costs from NT$3-6 million on average per year by improvement of the quality of prescriptions. 

​Taiwan has the highest ranked healthcare system and high quality infrastructure in the world. 100% electronic medical records brings Taiwan strength to export digital health technology. AESOP aims to utilize Taiwan’s strength in dealing with healthcare problems. As a pioneer of Al in Medicine, Prof. Yu-Chuan Jack Li believes that next-generation medical Al will be the optimal solution for patient safety and prevention ("Earlier Medicine").

About AESOP Technology
AESOP Technology, a spin-off from Taipei Medical University, was founded by experts in medical informatics, physicians and pharmacists since 2019. Their mission is to increase patient safety and quality by bringing next-generation medical AI to prescriptions.

Reference: Taiwan Research Highlight