Ask JARVIS – The Personalised AI Agent for DHL Care

Prototype developed for AI in Health Grand challenge helps pave the way for predictive care, personalised care and patient empowerment

What is the likelihood of a Diabetes, Hypertension and hyperLipidemia (DHL) patient developing complications over the next five years, and what are the factors that contribute to this risk? A JARVIS-DHL prototype developed for the AI in Health Grand Challenge has the answers.

Launched in June 2018, the AI in Health Grand Challenge seeks to explore how AI technologies and innovations can help solve important problems faced by Singapore and the world. The focus was on healthcare, and the challenge was on how AI can be used to help primary care teams stop or slow disease progression and complication development in 3H (hyperlipidemia, hyperglycemia, hypertension) patients by 20 percent in five years.

It is estimated that 3H is present in up to 20 percent of the adult population in Singapore and will rise with an ageing population, leading to an increase in healthcare spending and impacting the quality of life of those who are affected.

About JARVIS-DHL

JARVIS-DHL is one of three proposals that have been awarded. It is led by the researchers from the Institute of Data Science NUS (IDS), in collaboration with SingHealth Health Services Research Centre (HSRC), Singapore National Eye Centre (SNEC), National Heart Centre Singapore (NHCS) and Duke-NUS.  JARVIS-DHL aims to build a consolidated AI platform which can be used to improve the 3H care delivery process by facilitating practice of evidence-based personalised care and shared-decision making.

The researchers’ focus was on transforming local DHL primary care through the following three-pronged approach:

  • From reactive to predictive care by enabling accurate predictive stratification of DHL patients
  • From “one-size-fits-all” to personalised care by enabling customised care based on local and individual contexts
  • From passive to active patients by enabling patient education, self-care and monitoring

Benefits to Primary Care Teams

For primary care teams, early screening and risk stratification enables them to right-site care for 3H patients instead of relying on the reactive event-driven sequential referral model. This allows patients to spend less time in healthcare institutions, and also enables healthcare resources to be put to optimal use.

By facilitating evidence-based personalised care and shared-decision making, JARVIS-DHL also enables primary care physicians to work with patients to increase treatment adherence. For example, the system is able to recommend evidence-based treatment options, quantify personalised treatment benefits and the risk of complications, and adapt the treatment regimen based on the patient’s lifestyle. This helps alleviate the patient’s anxiety over perceived side effects and support holistic clinical decision-making.

Benefits to Users

Through the use of technologies for patient education, self-care and monitoring, patients are empowered to take ownership of their healthcare journey beyond their visits to the clinic, supporting a shared decision making with primary care physicians.

12-month report card

The team obtained access to local clinical datasets pertinent to their research and went on to develop the prototype for JARVIS-DHL, a consolidated AI platform which can be used to improve the care delivery process by facilitating evidence-based personalised care and shared-decision making.

The prototype incorporates a diabetes risk calculator that can compute the risk profiles of DHL patients that are likely to develop complications over a five-year period. The system gathers local primary care data as well as healthcare and lifestyle tracking data to create AI algorithms and models that can help identify at-risk patients. It identifies the specific factors that contribute to their risk and stratifies patients into various risk groups for the delivery of predictive care.

Next Steps

Whilst advancing AI research is a key goal of the AI Grand Challenge, one of the important takeaways for the team was the need to balance the aspirations for cutting-edge AI research against its practical impact in clinical applications.

With this in mind and as they approach Stage 2 of the development, the team has adopted a balanced approach that will deliver practical real-world impact as it validates and refines its AI model for deployment in clinics.

For more details, please visit https://www.aisingapore.org/grand-challenges/awardees-jarvis-dhl/

 

About the Team

Lead Principal Investigator: Prof Wynne Hsu (NUS)

Co-Principal Investigators:

  • Professor Ng See-Kiong (NUS)
  • Professor Lee Mong Li (NUS)
  • Associate Professor Chee Yong Chan (NUS)
  • Professor Wong Tien-Yin (SingHealth)
  • Professor Marcus Ong Eng Hock (SingHealth)
  • Associate Professor Tan Ngiap Chuan (SingHealth)
  • Dr Teh Ming Ming (SingHealth)
  • Adjunct Associate Professor Yeo Khung Keong (SingHealth)

Host Institution: National University of Singapore (NUS)

Partner Institution(s): SingHealth Group (SingHealth)

In 2019, the team published papers for top international AI platforms such as the Conference on Computer Vision and Pattern Recognition (CVPR), the IEEE International Conference on Image Processing (ICIP), and the IEEE International Conference on Tools with Artificial Intelligence (ICTAI). It has also received a request from the American Diabetes Association (ADA) to feature JARVIS-DHL in the association’s Thought Leadership Film Series.

The AI in Health Grand Challenge

The AI in Health Grand Challenge is a five-year, two-stage programme with a total funding quantum of $35 million. AI Singapore, together with an International Review Panel, selected three projects to be awarded Stage 1 funding of $5million per project for the first two years. The projects focused on applying AI technologies in innovative ways across the continuum of 3H (hyperlipidemia, hyperglycemia, hypertension) care.

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