CURRENT POSITION

Distinguished Professor
Graduate Institute of Biomedical Informatics
​Taipei Medical University
Dermatologist
Department of Dermatology
Taipei Medical University - Wanfang Hospital
Editor-in-Chief
BMJ Health & Care Informatics journal (the official journal of FCI and published by BMJ)
President-Elect / President
IMIA plays a major global role in the application of information science and technology in the fields of healthcare and research in medical, health and bio-informatics.

EDUCATION

M.D.
Medicine

Taipei Medical University
​1984-1991

Ph.D
​Medical Informatics

University of Utah School of Medicine
​1991-1994

EXPERTISE

AWARDS

2019年度第十六屆國家新創獎
​臨床新創組
​​痣能達人-皮膚痣人工智能風險辨識系統

SELECTED TALKs

停止後見之明！以行動支持台灣防疫的「薩利機長們」

李友專專欄
未來城市＠天下雜誌
​Jun 16, 2021

別焦慮！看懂這四件事，就明白台灣新冠疫情其實「短空長多」

李友專專欄
未來城市＠天下雜誌
​Jun 02, 2021

AI領袖對話峰會​

生策會、國際半導體產業協會(SEMI)
2020.12.04

How does Taiwan cope successfully with COVID-19 Crisis

5th German-Asian Business Dialogue
2020.11.12

​Leveraging Medical Resources in Taiwan​

HealthForAll
​Oct 22, 2020

AI for the Future of Healthcare

天下雜誌
2020.10.16

AI for the Future of Healthcare

Global Virtual Healthcare Summit 2020
Webinar | 2020.09.04

​Dr. Yu-Chuan Li on post-pandemic era: the application of AI in precision diagnosis and treatment

GASE
​Taiwan | 2020.07.31​​

AI AND SMART HEALTHCARE

BioASIA 2020
 Taiwan Bio Industry Organization
Taiwan | 2020.07.24

DATA MINING LARGE INTERNET NETWORKS TO PREDICT COVID-19 HOTSPOTS

Harvard Global Health Institute, Novartis Foundation and partners
Webinar | 2020.05.12

AI FOR SAFER AND EARLIER MEDICINE

QS-APPLE 2019
Japan | 2019.11.27

AI for Earlier and Safer Medicine

RESI Taipei
Taiwan | 2019.11.14

真正的上醫要醫未病之病

TEDxTaipei 2019 年會：Come．Unity 我，我們
TEDxTaipei
Taiwan | 2019.10.06

科創講堂
​智慧醫療-先覺醫療

科學人雜誌​
Taiwan | 2019.08.11

探索人類疾病的宇宙​

TEDxXinyi
Taiwan | 2014.03.04

探索手機力量！
​北醫推播「史」命必達疾病的宇宙​

華人健康網
Taiwan | 2012.09.19

PUBLICATION

​How Artificial Intelligence can make medicine more pre-emptive?

Artificial Intelligence (AI) is ushering in a new era of “Earlier Medicine”. The burgeoning healthcare technological landscape is showing great potential; from diagnosis and prescribing automation to early detection of disease through efficient and cost-effective patient data screening which benefit from the predictive capabilities of AI. Monitoring prognosis of both in and outpatients has proven a task AI can perform to a reliable degree, saving lives of critical care patients and helping manage cases based upon early predictions.​

“Earlier Medicine” refers to a temporally predictive and proactive approach to individualized health enabled by innovative AI modeling plus longitudinal/personal health big data. It calls on medical practice to not just react or manage present situations but also medical events of the foreseeable future. Such an approach will save money, lives, and our health care ecosystem from inefficiencies and disintegration.

​Full Text
Usman Iqbal, Leo Anthony Celi, Yu-Chuan Jack Li, Journal of Medical Internet Research

​Evaluation of Appropriateness of Overridden Alerts in Computerized Physician Order Entry: A Systematic Review

Clinical decision support system (CDSS) has been appeared as an indispensable tool for reducing medication errors and adverse drug events. A significant amount of studies reported that medication-related alerts in the current CDSS were often inappropriately overridden. Increased overridden rate has already raised questions about the appropriateness of CDSS application and raised a concern about the patient’s safety and quality of care. We, therefore, aim to conduct a systematic review to determine the overridden rate, the reasons for the alerts override and evaluate the appropriateness of overrides reasons.

A total of 23 articles were included in our systematic review. The rage of average override alerts was 46.2% to 96.2%. However, the rage of the overall appropriateness rate for override was 29.4%~100%. Although, the rage of appropriateness was varied by alert types (Drug-allergy: 63.4%~100%, drug-drug interaction: 0%~95%, dose: 43.9%~88.8%, geriatric:14.3%~57%, renal: 27%~87.5%. Indeed, the interrater reliability for the assessment of override alerts appropriateness was excellent (the range of κ was 0.79~0.97). The most common reasons for override were “will monitor”, “patients have tolerated before”.

The findings of our study show that CDSS overridden rate was often high and most of the cases overrides were identified as appropriate but the rate of appropriateness varied widely by different types of alerts. However, inappropriate overrides were associated with a higher rate of potential ADEs when compared with appropriate overrides. Future efforts should try to focus on reducing alert fatigue by providing alerts that are clinically relevant, information related alerts are clear and unambiguous.
​
Full Text
TN Poly, MM Islam, HC Yang, YC Li, JMIR Medical Informatics

​Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data–driven tools exist for more accurate prediction. To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information.

This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018.

The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.
​
Full Text
HH Wang, YH Wang, CW Liang, YC Li, JAMA Dermatol, 2019;155(11):1277-1283

A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan

The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization.

Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another.

The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states.

​Full Text
CW Huang, S Syed-Abdul, WS Jian, U Iqbal, PA Nguyen, PS Lee, SH Lin, WD Hsu, MSWu, CF Wang, KL Ma, YC Li, Journal of the American Medical Informatics Association, Vol 22, Iss 2, Mar 2015, P290–298

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data

Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data.

We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness.

One hundred twenty-one results of 2,400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology.

​Full Text

CY Huang, PA Nguyen, HCYang, MM Islam, CW Liang, FP Lee, YC Li, Computer Methods and Programs in Biomedicine, Vol 170, Mar 2019, P31-38

​Facebook use leads to health-care reform in Taiwan

​Social networking services are transforming the delivery of health care. Facebook, for example, is now commonly used by medical students, patients, and other stakeholders in the health-care system.1, 2 We describe how Facebook enabled collaboration between stakeholders in emergency-medicine policy in Taiwan, which led to reforms.

The Taiwan Society of Emergency Medicine3 has been in slow-moving negotiation with the Department of Health for the past several years over an appropriate solution to emergency-room overcrowding. A turning point was reached on Feb 8, 2011, when an emergency physician who was an active social network user and popular blogger among the emergency-room staff created a Facebook group called “Rescue the emergency room”. Within a week about 1500 people—most of the emergency department staff around Taiwan—became members of this group and started discussing actively and sharing their experiences. One of the members then posted the group's concerns and problems on the Facebook profile of the Taiwanese Minister of Health. This caused the minister to join the group and get engaged in the discussion. A multiparty dialogue involving many different stakeholders and perspectives was suddenly possible.

​Full Text
S Syed-Abdul, CW Lin, J Scholl, L Fernandez-Luque, WS Jian, MH Hsu, DM Liou, YC Li, The Lancet, Vol 377, Iss 9783, 18–24 June 2011, P2083-2084

BOOKS

AI醫療大未來

作者：李友專
好人出版
2018
ISBN：9789-8692-7513-2

健康醫療資訊科技
​發展政策建言

作者：李友專等人
國家衛生研究院
2018
ISBN：9789-8605-5133-4

建立學習型健康系統之大數據基礎

作者：李友專等人
國家衛生研究院
2018
ISBN：9789-8605-6237-8

醫學資訊管理學
​3版

作者：張慧朗、邱文達、李友專等
華杏出版股份有限公司
2018
ISBN：9789-8619-4503-3

醫學資訊管理學
​2版

作者：張慧朗、邱文達、李友專等
華杏出版股份有限公司
2013
ISBN：9789-8619-4293-3

醫療資訊管理概論

作者：龐婷文, 李友專等
華格那出版有限公司
2010
ISBN：9789-8663-3562-4

醫學資訊管理學

作者：張慧朗、邱文達、李友專等
華杏出版股份有限公司
2006
ISBN：9861-9400-06

國際醫院評鑑
​JCI實戰經驗分享

副總編校：李友專等
華杏出版股份有限公司
2006
ISBN：9572-9228-15

病人安全-理論與實務

主編：邱文達, 李友專等
台灣醫務管理學會
2004
​ISBN：9570-1725-09

醫療資訊管理學

作者：吳昭新, 李友專等
偉華書局有限公司
2002
ISBN：9576-4050-68

簡明醫學資訊學

總審閱：李友專
合記圖書出版社
2002
​ISBN：9576-6688-75

PATENT

Method And System For Analyzing Skin Texture And Skin Lesion Using Artificial Intelligence Cloud Based Platform​

#202044271
Yu Chuan Li, Yen Po Chin, Ze Yu Hou, Yu Ting Lin, Hsiao Han Wang, Lung Chen Li

Intelligent Personalize Medication Reminding System

#I499995
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang, Hsien Chang Li

Computing Device And Portable Device For Predicting Major Adverse Cardiovascular Events​

#M603615 ​
Chieh Chen Wu, Yu Chuan Li
​

Ultrasonic and Ozone Cleaning Device Without Water Sink

#M444228
Wen Shan Jian, Yu Chuan Li, Wei Jung Chang
​

Mobile Personal Electronic Health Record System

#M409487
Shabbir-Syed Abdul, Wen-Shan Jian, Yu-Chuan Li ​

System For Analyzing Skin Texture And Skin Lesion Using Artificial Intelligence Cloud Based Platform

#​M586599
Yu Chuan Li, Yen Po Chin, Ze Yu Hou
​

NEWS

Study finds success in using Taiwanese medication safety AI model for US' EHR systems

MobiHealthNews | May 24, 2021
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.

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....more

全球醫療資訊權威組織（IMIA ）支持台灣加入世界衛生大會

自由時報 | Jun 03, 2021
全球醫療資訊權威組織 — 國際醫療資訊協會（IMIA）於五月初親自向WHO發函表示，很榮幸支持台灣加入世界衛生大會（WHA），並肯定台灣對於COVID-19的應變能力，以及提供世界各國在醫療資訊方面的協助與貢獻。
台灣醫學資訊學會（TAMI，以下簡稱本會）日前獲得IMIA通知，說明其已向WHO表態支持台灣，在此，本會特別感謝IMIA主席 Sabin Koch 和執行長 Elaine Huesing 在國際上的支持，IMIA並向WHO說明IMIA過去也曾因為過時的成員結構，以及越來越無法區分...more

眾志成城  再守一次

自由時報 | May 23, 2021
建議台灣防疫政策不要以「清零」為短期目標，而是以「COVID-19流感化」為目標，提高疫苗接種率、提早介入降低死亡率，如此恐慌將自然消失，經濟衝擊也會降到最低。
​
筆者目前人正在美國哈佛大學進行學術訪問，美國現在疫情的實際狀況，與我們在台灣接收的資訊相異頗大。美國現在雖然每天新增病例仍達三萬例，但這已經是從幾個月前一天新增二十五萬例降到目前這個數字，整體死亡率也已經降得很低，尤其老年人的死亡率也已經降低到與其他年齡層接近，跟疫情剛開始時的高死亡率、恐慌與醫療資源崩潰狀態迥然不同。
 
筆者幾位在美國加護病房工作的友人表示，儘管加護病房需要的人力比以前多，但已不像先前那麼忙亂，整體運作已經找到新節奏，可以正常收治病人。
 
現在波士頓的的餐飲業也幾乎都發展出新營業型態...more

​是痣還是皮膚癌？一張照片先篩檢

彭博士觀風向｜Apr 12, 2021

​防疫遍全球日常生活
​凸顯隨選醫療(Healthcare On-Demand)重要性

錢線百分百｜Mar 26, 2021

​科技醫療異業結合
​台灣醫材行銷全球

錢線百分百｜Mar 26, 2021

科技優化精準醫療 預防診斷超前部署

科學人雜誌｜Feb 09, 2021
​精準醫療近年來已經成為顯學，臺北醫學大學醫學科技學院特聘教授李友專為其定義為個人化的醫療，包括有基因體、環境、行為、表現型等四大要素，其中基因體方面得知的分析內容，大約提供了整體精準醫學分析的20%，環境則包含大環境與微環境，微環境例如在空間中抽菸造成的細懸浮微粒PM2.5、人體腸道內的菌種等。表現型為包括病歷等個人的健康紀錄，我國自1995年以來實施全民健保，擁有全球最大的表現型資料庫，是發展精準醫療的...more
​

遠距醫療vs.遠距諮詢 台灣數位醫療怎麼走？

DIGITIMES｜Feb 01, 2021
​台灣遠距醫療產業已有不少新創團隊從皮膚科、家醫科、身心科等等領域推出B2B與B2C的服務，而因為從不同科別應用出發，也面臨不同的法規、技術、藥品、個管的配套，針對不同科別的不同流程與配套，台灣遠距照護服務產業協會(TIAT)理事暨標準認證組召集人李友專表示，定義與區分遠距諮詢和遠距醫療、遠距會診的兩端人員訓練、重塑科技產業對於數位醫療的價值體系與思維都是推展遠距醫療的關鍵...more
​

智慧醫療給大學教育的啟示
​2021精準教育元年，從「深度學習」開始

李友專專欄｜未來城市＠天下雜誌｜Jan 06, 2021
​最近因為AI與醫療科技的興起，大家常聽到「深度學習」（Deep Learning）與「精準醫療」（Precision Medicine）二詞。深度學習，指的是利用多層次類神經網路驅動機器學習的演算法；精準醫療則是強調個人化的疾病預防、診斷與治療概念——有別於以往忽略個體差異，任何人只要得到同樣疾病，就採用同樣治療方法的以偏概全型（one-size-fits-all）醫療。這兩個詞不禁使我聯想到...more
​

​肺炎扮催化劑
​零接觸加速醫療數位轉型

錢線百分百｜Nov 27, 2020

​台灣兩大強項
醫療結合科技  點亮商機

錢線百分百｜Nov 27, 2020

​15分鐘收費近千元，北醫大線上諮詢為何在海外爆紅？

天下雜誌｜Oct 09, 2020
新冠疫情讓病人不敢出門就醫，北醫大新創團隊推出由正牌醫生待命的遠距諮詢服務，15分鐘29.99美元的收費，意外在歐美台灣人圈子大受歡迎。這會是台灣網路醫療服務進軍海外市場的起點嗎？9月某天晚上，台北醫學大學醫學科技學院教授、萬芳醫院皮膚科主任李友專坐在辦公室內，等著一場視訊連線。視訊彼端，是名...more

Thank you to Prof Jack Li

ISQua | Apr 6, 2020
For the past 6 years Professor Yu-Chuan (Jack) Li has been the Editor-in-Chief of the International Journal for Quality and Safety in Health Care (IJQHC). Professor Li is Dean of the College of Medical Science and Technology in Taipei Medical University, and a Professor at the Graduate Institute of Biomedical Informatics. He has enabled IJQHC to maintain its position within the Quality Improvement and Patient Safety field as one of the leading journals...more

​全台用藥分析！醫師揭用藥安全的五十道陰影

三立新聞網｜Sep 17, 2020
你的用藥觀念正確嗎？響應9月17日「世界病人安全日」，台北醫學大學李友專教授與醫守科技共同發表全台灣用藥安全分析，呼籲民眾重視自己與家人的用藥安全，尤其是長輩以及常見的降血壓藥和降血糖藥...more

​北醫李友專：AI開創「早覺醫療」新時代 登上《JMIR》

環球生技月刊｜Aug 14, 2020
近(11)日，由臺北醫學大學公衛學院、健康資訊科技國際研究中心的團隊，聯合麻省理工學院(MIT)的研究員，在醫學資訊頂尖期刊《JMIR》中發表論文，提出以人工智慧(AI)開創「早覺醫療」(Earlier Medicine)新時代的概念，該由醫學資訊研究所教授李友專領導......more

​突破WHO封鎖！ 台AI權威當「主席」

中天新聞　新神秘52區 ｜May 24, 2020
台灣屢屢被大陸打壓，排除在WHO之外，但是有一個人，卻在去年突破重圍，而且WHO旗下的這個正式組織，還以過半數，把中國大陸除名，台灣甚至以40票對4票，贏得下屆的主席寶座，台灣這位傳奇人物是誰？怎麼辦到.…more

​​台北醫學大學醫學資訊研究所特聘教授李友專 AI醫療科技防疫先鋒​

能力雜誌 ｜May 05,2020
​新冠肺炎(COVID-19) 全球大流行，戴口罩、社交距離、在家上班等措施，徹底顛覆過去的日常生活想像，更多人思索著：「疫情過後，還能一切回到從前嗎？」而研究AI 醫療近30 年的李友專，則提供了一個醫療大未來的想像.…more

​​Taiwan dermatologists create Facebook page for foreigners seeking medical help

The China Post ｜Apr 27,2020
​With the coronavirus pandemic taking a toll on frontline medical workers worldwide, many patients with pre-existing medical conditions are struggling to seek medical help.…more

​​武漢肺炎》李友專：疫苗至少還要等1年，但科技能做4件事降恐慌​

未來城市｜Apr 23,2020
​2019年底起自武漢的一聲咳嗽，不只咳去了16萬條珍貴的生命，也咳掉了我們對正常生活的勇氣；而連帶飄出的飛沫，甚至蒸發全世界6%GDP，帶領全人類迎向1930 年代大蕭條後最慘澹的一年。…more

關於癌症，你願花大錢治療，還是花小錢預防？

未來城市＠天下｜Apr 20,2020
​現今AI與醫療的應用，大多是使用於影像辨識；但比起發展醫療影像判讀，將AI應用於「疾病預防」可能來得更重要。
尤其，台灣擁有兩大醫療優勢：健保資料庫與長年累計的電子病歷醫療大數據。若能妥善的運用這些資料，將能擴大AI預防醫學的應用層面。…more

​​Taiwan Can Help！台皮膚科醫師發起「線上諮詢」 全球防疫零距離

健康2.0｜Apr 15,2020
​新冠肺炎（COVID-19）疫情蔓延全球，不少國家紛紛對國民祭出「居家禁足令」，或是呼籲民眾減少非必要的外出。在這樣全球醫護人力吃緊的狀況下，有一群台灣皮膚科醫師選擇貢獻一己之力，提供海內外民眾免費線上諮詢，用實際行動告訴全世界：「Taiwan can help！」…more

​Taipei Medical University Professor Yu-Chuan Jack Li honored as the incoming president of the International Medical Informatics

QS WowNews｜Feb 18, 2020
Prof. Yu-Chuan Jack Li, the Dean of College of Medical Science and Technology at Taipei Medical University was elected as the new president of International Medical Informatics Association (IMIA), starting from 2021.
Pro. Li has been active travelling around the world to promote international collaborations, and has been dedicated to the development of next-generation medical AI, the promotion of patient safety, and the concept of Earlier Medicine. He was former chairperson of Taiwan.......more

北醫李友專：沒有導入AI，醫療沒有未來

DIGITIMES｜Mar 27, 2018
人工智慧(AI)應用在醫學領域早已不是新聞，但是有部分的人會認為台灣醫療技術深厚，是否真的有導入AI技術的重要性？「現在已經不是重要性的問題，而是沒有導入AI，醫療根本就沒有未來。」台北醫學大學醫學科技學院院長李友專直言，這並不是危言聳聽，現行的醫療做法幾乎都是「只要是這個病，大多都是這樣治療。」經驗法則乍聽之下並無問題，但是李友專認為，這是一件相當有疑慮的做法.......more

FREE RESOURCE

The Disease Map
疾病地圖

The introduced disease maps visualise how human disorders are 'positioned' against each other in a population with respect to an individual biological organism. It is well known that many human disorders are risk factors for or consequences of other disorders. While many of such positive associations between disorders are well known and understood, many remain either entirely unnoticed or not properly recognised. Moreover, it is reasonable to assume that certain human disorders can be associated negatively, that is, a person being affected with A, has decreased chances to get affected with B (can be, but not necessary, vice versa). The presented visualisation is a novel tool for validating already known as well as discovering new, never noticed, empirical association patterns between human disorders. While some of such patters can reflect specifics of a healthcare system, many can reveal interesting biological phenomena relevant practically.
1.S Syed-Abdul, M Moldovan, PA Nguyen, R Enikeev, WS Jian, U Iqbal, MH Hsu, YC Li, Profiling phenome-wide associations: a population-based observational study, 2015
2.M Moldovan, R Enikeev, S Syed-Abdul, PA Nguyen, YC Chang, YC Li, Disease universe: Visualisation of population-wide disease-wide associations, 2014

Phenome-wide Associations Database
疾病關聯資料庫

​To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way.In this population-based observational study, we analyzed 782 million outpatient visits and 15,394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million individuals. Our data was obtained from Taiwan’s National Health Insurance Research Database.
S Syed-Abdul, M Moldovan, PA Nguyen, R Enikeev,WS Jian, U Iqbal, MH Hsu, YC Li, Profiling phenome-wide associations: a population-based observational study, Journal of the American Medical Informatics Association, Volume 22, Issue 4, 1 

PROJECT

AI-enhanced Safety of Prescription and clinical application

Ministry of Science and Technology, 2019.07~2020.06
A recent study reported more than 250,000 people in the U.S. to die every year from medical errors. Medical errors are the third-leading cause of death after heart disease and cancer. Unsafe medication practices and medication errors are a leading cause of injury and avoidable harm in health care systems across the world. According to the WHO report, the cost associated with medication errors has been estimated at $42 billion USD annually. WHO launches global effort to salve medication-related errors in 5 years.

In order to decrease medication errors or near miss, a lot of hospitals used “Rule-based” to create a clinical decision support system. However, there are some limitations to practice. For example, there is about 50-90% overridden rate for alerts, not cost-effective, without Randomized Controlled Trial (RCT) and it could not learn to learning.
Our research team has been investing in AI - enhanced Safety of Prescription for a long time. Based on more than 1.3 billion past electronic medical records, we develop software which included unsupervised learning and reinforcement learning. AI-enhanced Safety of Prescriptions (AESOP) is able to simulate real-world physicians’ behaviors by the treatment patterns. It could apply to detect appropriateness of prescription. Now, there are more than 200 physicians are using our system in 250 K patients/year. Physicians accept rate is more than 50% and AESOP agreed rate reaches 85% based on expert reviews

Based on this project, we are expected to upgrade our system (Protection＆ prevention, Proactive Recommendations), develop reinforcement learning model, create hospital’s report、start clinical trial in US medical institutions, and promote startup business. Then, it can build brand products, increase entry barriers to, expand the market, and build the first AI medical unicorn in Taiwan.

Artificial Intelligence for Earlier and Safer Medicine Worldwide

Ministry of Science and Technology, 2018~2019
Aging population in Taiwan is growing rapidly. 12.5 percent of the population is aged 65 or older. Due to the incidence of chronic diseases and degenerative illnesses in the aging population, it has increased the chances of polypharmacy. On average, Taiwanese visits hospital about 14 times in a year and gets at least 4-5 medicines every time. Therefore, the total number of prescription per year is about 345 million. Research has shown that the rate of medication error is 0.5% to 5% which means approximately 0.75 - 7.5 million of dispensed prescriptions in Taiwan are inappropriate. The current clinical decision support system can not prevent medication error efficiently. It is also claimed that 193 tons of medicine have been wasted annually because of poor patient compliance in Taiwan.

Artificial Intelligence (AI) has been used extensively and is expected that machine learning, deep learning and natural language processing will become the major technology in next 5 to 10 years. AI, machine learning especially, is showing its potential in medicine field due to the availability of big data. As Taiwan has the world-class medical electronic data such as electronic medical records and health insurance database, it is a great opportunity to apply big data to develop AI in medicine.

In this project, we aim to develop artificial intelligence for earlier and safer medicine in Taiwan as well as worldwide which includes (1) Create model and system for earlier and safer medicine; (2) Develop artificial empathy; (3) Randomized controlled trial and (4) Medical institutions used and reinforcement learning. It will exhibit Taiwan's strengths to promote the wisdom of medical safety and enhance patient compliance. We will connect industry, government, academia, research and medical between Taiwan and other countries to build a AI with Humanity, for Humanity.
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In the same time, this project will provide medical databases containing disease-disease, disease-medication and medication-medication association. By sharing medical resource, It will help to improve the application of medical treatments, ensure the safety and quality for healthcare.

Building MedGuard for Drug Safety

Ministry of Science and Technology, 2017.05~2017.12
Medication error is the most common medical error. Due to the complexity of drug treatment process, medication error could happen anytime when writing the prescription, manufacturing the formulation, dispensing the formulation, taking the medicine, and so on. The rule-based medication safety system is widely used to identify the accuracy of the medication information. However, there is about 50-90% overridden rate for alerts, it expressed that rule-based medication safety system is not useful. More, it is difficult to determine its feasibility and usefulness without Randomized Controlled Trial (RCT). With heightened concern about medication-use safety and medication errors, information technology (IT) could be a means to effectively lower the risk and the chance of medication errors.
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In this project, we develop an AI model for advanced electronic safety of prescriptions. The prototype is based on Probabilistic method. We built up a knowledge base with “Big data” analyzing the associations between Disease-Medication (DM) and Medication-Medication (MM) and we further developed AOP model (Appropriateness of Prescription). The core technique has been applied in western prescriptions and Chinese prescriptions and also published in International Journals (PLOS One、pharmacoepidemiology and drug safety). We also get a U.S. patent (Patent US20150356272). According to Technology Readiness Level(TRL), MedGuard achieves TRL 6 (System/subsystem model or prototype demonstration in a relevant environment). 

This project will create project team and expert consultants team to help MedGuard achieve TRL 7-8 (product evaluation. MedGuard is a software module and it can be customized as APP, server, and Cloud. The main characters in the industry value chain are Health Administration or hospitals and pharmaceutical companies and insurance companies. Many statistics indicate that medication-use safety has great potential in business and value in terms of the patient safety, healthcare expenditure, medical audit fee, etc. Commercializing MedGuard not only reduce the risk of medication errors, but also improve healthcare quality and significantly decrease the medical waste and expense.

Enhancing Intelligent Healthcare with Usage of International Cooperative Research in Health Informatics

Ministry of Health and Welfare 2017.08~2018.04
The gradual aging of the Taiwanese population has strained the government’s ability to provide healthcare for such a large number of elderly citizens. This challenge has caused various healthcare policies to be enacted urgently. People have become more aware of the advances in medical technology that can improve their lives and now have higher expectations for how their own personal healthcare data should be leveraged to meet these expectations. Personalized healthcare, information technology, and cloud computing have opened up all kinds of new healthcare applications and possibilities. Information and communication technology as well as ehealth may lower costs and unite once disparate industries. Electronic health records are expected also to empower patients, increase healthcare literacy, increase safety, and encourage patient compliance. Patients can also participate in their own health diagnoses and make informed decisions.

Establishing an ehealth hub where various parties can share data is the main topic of this research proposal. This ehealth hub follows the Institute of Medicine’s 2007 promotion of Learning Health Systems. Charles P Friedman, a pioneer and professor with extensive experience in designing Learning Health Systems, was invited to be a mentor in this project. Through this international cooperation, sharing ideas, holding workshops, meetings, and incorporating the perspectives from different domains can be possible within this ehealth hub.

​Smoking is the largest avoidable cause of preventable morbidity and premature mortality worldwide. The prevalence of smoking worldwide is estimated at about one billion smokers, half of which will die prematurely as a consequence of their addiction. Smoking causes approximately 85% of the 3 cases of lung cancer and chronic obstructive pulmonary disease (COPD) and contributes to the development of many other lung diseases, such as Cancer of the trachea, bronchus and lung. We've passed a European Union’s Horizon 2020 research about smoke free brain. Using the wearable technology plus a mobile App that is programmed to push tailored messages for health concern and readiness to quit, tips or sustaining abstinence, use of interactive self-assessments, helpful cessation information, and access to physical activity charts.

​These database will be collected to become knowledge, through LHS system to mechanical learning. With the advancement in the medical sciences, cancers are now considered as chronic disease rather than fatal illness. This moves the focus in the fight against cancer from sustaining life towards maximizing functional capacity and Quality Of Life (QOL). A critical element in this shift has been the rise of active rehabilitation in the management of cancer. We will cooperate with CATCH project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 722012.

Furthermore, overall physical activity and health behavioural change is increasingly becoming important in cancer management. Technology advances such as gamification based on biofeedback, and neuromuscular electrical stimulation, can help address some of these information and communication technology into LHS cycle. To improve cancer patients physical activity, health behavioural and QOL. Keywords: Information and Communication Technology (ICT), eHealth, Learning Health System, Electronic Health Record, wearable technology, International Collaboration. 
Yu-Chuan (Jack) Li
College of Medical Science and Technology
​Taipei Medical University
       

​© Yu-Chuan Jack Li 2021