19 February 2021
ACV40: The Doctor Who Built An AI Ecosystem (Dr Ngiam Kee Yuan, Group Chief Technological Officer, NUHS)
Dr Ngiam Kee Yuan is the Group Chief Technology Officer of the National University Health System (NUHS) Singapore overseeing technology deployment in Western Healthcare Cluster of Singapore. He is concurrently the Deputy Chief Medical Informatics Officer at the National University Hospital of Singapore and has a special focus on artificial intelligence research and implementation in healthcare. He has certification training by the American Medical Informatics Association and has published in computing and medical journals on topics related to healthcare AI applications and technology.
Dr Ngiam is a Consultant Thyroid and Endocrine Surgeon and Associate Professor at the Department of Surgery, National University Hospital, Singapore specializing in thyroid oncology and minimally invasive endoscopic and robotic thyroid surgery. Dr. Ngiam also engages in research into endocrine and metabolic surgery as well as artificial intelligence applications in healthcare.He promotes interdisciplinary collaboration throughout the NUS campus, particularly between the schools of medicine, engineering, and computer science for various healthcare applications. He has been awarded the ExxonMobil-NUS Research Fellowship for Clinicians and numerous teaching awards for his work in research and education.
In this episode, we talk about how Dr Ngiam built Singapore’s largest AI ecosystem for healthcare with no prior coding experience.
3 THINGS I LEARNED
- Take a year out if you would like to explore an area that you want to go into. But be clear about what you want to explore and achieve. Keep in close contact with your mentor to ensure that you are not forgotten or lost. Speak to your employers to ensure that the additional skills you come back with are valued by the organisation and are rewarded accordingly.
- You don’t necessarily need to learn how to code to get started on a project – Dr Ngiam certainly did not know how to when he started. Instead crucially he surrounded himself with experts and became good at translating clinical questions into language computer scientists could understand. He picked up computer science concepts along the way – a much more practical way of learning.
- Interesting spaces to look at for the future: (1) Automation of basic mundane processes in healthcare (2) Amalgamating data and giving clinicians tools to build better models and improve patient care
- The origins of Dr Ngiam’s interest in AI and machine learning
- How it became apparent to him that a lot of the problems that he was facing in his own research projects were replicated across the entire organisation
- The size of this problem and how Dr Ngiam set out to tackle it
- Gathering a team and building a community to tackle this problem
- Being the person who translated the needs of healthcare professionals into language that computer scientists could understand and problems they could address
- Acquiring skill on the job is a better way of learning
- Getting senior management buy in and support for the project
- Other opportunities, including Medilot, that opened up
- The need for clinicians to do more than just their clinical practice
- Starting a new track for clinician innovators
- Dr Ngiam finding ways to fund his passion – with grant funding and then a job title that gave him time to build this aspect of his career
- Taking a year out to pursue other interests and not getting lost along the way
- Interesting technological advances that will reshape the future of medicine
- Dr Ngiam Kee Yuan https://sph.nus.edu.sg/faculty-directory/ngiam-kee-yuan/
- National University Healthcare Cluster (NUHS) https://www.nuhs.edu.sg/Pages/Home.aspx
- Artificial intelligence in healthcare https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare
- Discovery AI platform https://www.nuhs.edu.sg/sites/nuhs/NUHS%20Assets/News%20Documents/NUHS%20Corp/Media%20Releases/2018/060718%20Media%20release_Glimpse%20into%20hospitals%20of%20the%20future.pdf
- Python (coding language) https://www.python.org/
- MEDILOT https://medilot.com/
- FTE = full time equivalent https://en.wikipedia.org/wiki/Full-time_equivalent
- Datathon https://www.datasciencesociety.net/what-is-the-datathon/
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