Working towards greater data integration in healthcare – origins of NUHS’s DISCOVERY AI platform

Healthcare IT News Asia Pacific speaks to Prof Ngiam Kee Yuan, Group Chief Technology Officer, National University Health System about the origins and developments of its DISCOVERY AI platform.
By Dean Koh
09:57 PM
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Prof Ngiam (left) doing a demonstration of the DISCOVERY AI platform at the inaugural Healthcare AI Datathon and Expo held in July 2018. Credit: NUHS

“I think the biggest trend (in healthcare) is towards greater integration. Traditionally, healthcare has been very fragmented, where many different groups serve specific clinical needs without necessarily coordinating with each other. But going forward, the trend is towards integration – not just of things like databases and systems, but integration of the way we process the data and how this influences the clinical workflow,” said Prof Ngiam Kee Yuan, Group Chief Technology Officer of National University Health System (NUHS) in Singapore, in response to what he thought would be the key trends that will impact healthcare systems in future.

It was with the same motivation and mission to best use the healthcare data for research at NUHS that led to the building and development of the DISCOVERY AI platform, which started about four years ago and the platform was officially announced in July 2018. The platform is what Prof Ngiam describes as a ‘sandbox’ that allows the staff at NUHS to develop AI tools in a safe and equitable way – the platform is scalable and can be applied to more than one system within the organisation.

“We saw the opportunity because we had datasets which were large enough to support the development of these AI tools. And one of the advantages we have at NUHS is that we have clinicians and allied health professionals who understand, and are willing to develop these tools. I cannot emphasise enough the importance of having the clinicians onboard throughout the development process.”

Currently, a randomised control trial of a system as part of the platform is a free-text diagnosis machine at the Accident & Emergency (A & E) department. When doctors input a certain set of findings as part of clinical documentation, the machine would automatically provide a suggestion for a diagnosis. The team is exploring diagnosing appendicitis for a start. The trial is slightly under halfway through and Prof Ngiam hopes by the later part of 2019, they would be able to have results, which would be the basis for them to operationalise the AI tool.

One of the early milestones for the NUHS DISCOVERY AI platform is its ability to sustain multiple proof-of-concept projects. With the platform, individual projects are no longer fragmented and there is the ability to aggregate, link and share large data sets.

“It took us four years to get to this point and the next milestone for us is to finish our trials and to actually launch them as “software as medical devices”. Again there are some hurdles to get through before we get there but seeing where we are right now, it is very likely we should be able to get through them.”

Prof Ngiam also pointed out that as the platform is unlike anything they had before and behaves like an advanced form of clinical decision support system (CDSS), which are not based on a set of rules but based on a set of complex trained weights and multiple factors that affect a certain outcome. Despite its complexity, the AI tools need to be thoroughly trialed before it can be used in routine clinical practice.

“This is why we are running it as a trial now in the hospital. In essence, the platform is run under the ambit of a trial to mature the workflow and test the system under real world conditions. Operationally, what the doctors are doing during the trial is no different from what they would have done, except that we collect the data on the basis of the trial,” he added.

Looking to the future, Prof Ngiam and his team is working towards completing the trials in 2019 and hopefully heading towards registration in terms of the platform being used as a medical advisory device.