UCARE.AI is a technology enabling startup based in Singapore. The suite of proprietary deep learning and neural network algorithms developed by the company, help prioritize healthcare resources to reduce preventable hospitalization, potentially resulting in significant annual savings in the industry. The solutions also boast a highly accurate predictive capability by correctly identifying the risk of re-hospitalization for a segment of Singaporeans.
The company was founded in 2016, by Neal Liu, a former Google employee and Christina Teo, who was the Chief Executive Officer of Singapore tycoon and philanthropist Peter Lim’s investment unit. Together with a group of data scientist and technologists, they thought they could use data ethically to solve real-world problems and improve lives. By leveraging on artificial intelligence (AI), UCARE.AI’s predictive engine was
Last December, UCARE.AI partnered with Parkway Pantai, one of Asia’s largest integrated private healthcare groups to launch an AI-powered predative hospital bill estimation system. This will enable patients to receive the most accurate bill, which estimates to fall within an 18% margin from the final bill figure. The system will not only enhance price transparency of hospital charges but also gives patients a considerable peace of mind over their healthcare expenses as they receive their treatments.
AIMed was in touch with Christina Teo, co-Founder and Chief Executive Officer of UCARE.AI earlier, to find out more about the AI landscape in Asia as well as UCARE.AI’s latest development.
AIMed: It has been six months since UCARE.AI’s collaboration with Parkway Pantai. Thus far, what are some of the feedback you have got from users? How do you think the collaboration can be improved or bring forward?
Christina: Since the implementation of the system for Parkway Pantai, we are proud to say that there has been zero downtime and zero complaints. The system is very easy to use and they have seen clear positive results since the implementation.
Our platform can be used for a variety of predictive health insights. First and foremost, UCARE.AI is a technology enabling company. Our core competency is our proprietary (i) data acquisition model, (ii) infrastructure design and architecture, and (iii) growing knowledge base of insights generated from our AI (artificial intelligence) engine.
Over time, we plan to implement all these solutions into Parkway Pantai’s system. The AlgoPacks are designed to work together to help the patient throughout their whole journey while helping the client gain a better understanding of the lifetime value of each patient.
AIMed: Based on your knowledge about the industry and your experiences with UCARE.AI, do you think Asia has a very different technology or MedTech landscape as compared to the US or Europe?
Christina: Yes, it is very different. The markets in the West are better developed and there are a lot more established companies. The rules and regulations are also very different from that in Asia, which affects the way companies operate.
The demographics, lifestyles,
UCARE.AI started out in Asia and our models are trained on more than six million Asians, which account for diverse differences in patient profiles, disease patterns, genetic make-up, dietary habits and localized operating procedures of hospital providers and insurers.
AIMed: Is UCARE.AI planning to venture into other areas of healthcare? If so, what are some of the plans?
Christina: We started in healthcare because we believe that it has one of the most sensitive regulations around data privacy. It is also one of the most challenging verticals due to fragmented practices and legacy systems. However, it is the industry where innovative technologies could lead to the highest social impact.
Having found success in healthcare, we believe our accumulated experience from working with this data would make it much easier to expand into other verticals. We aspire to transform the landscape of additional verticals for the better through continually innovating in our application of machine learning (ML). We are currently exploring other verticals
AIMed: AI is promising but some healthcare professionals are still s
Christina: Part of the education process is helping healthcare professionals understand how AI works. A common misconception is that AI will take over jobs and replace manpower in the future. However, AI is here to help workflow and bolster productivity. By helping healthcare professionals understand the benefits of AI, they will learn to embrace the technology rather than fear it.
Another pain point in encouraging healthcare professionals to
AIMed: You and your co-Founder, Neal Liu, do not have formal medical/healthcare training, how challenging it is for the both of you to break into this industry?
Christina: Prior to founding UCARE.AI, my previous role also invested controlling stakes in healthcare and industries. It was then that I experienced first-hand the pain points and tech gaps as an owner and operator of healthcare assets. As Neal and I were conceptualizing our business idea, we received our first validation even before we incorporated the company when 2 prominent healthcare leaders bought into our idea and seeded UCARE.AI during our development phase. Till today, our early investors remain as our medical advisors and opened several doors for us.
AIMed: There are many startups these days proclaiming the use of AI or machine learning to develop medical/healthcare solutions. The competition can be rather steep, so do you think this is a healthy phenomenon for the industry and also for our healthcare professionals and patients?
Christina: AI/ML has become one of the most over-used and over-hyped value proposition. The acid test is whether startups can implement AI systems to solve real-world complex problems at a
Quoting from Kathy McGroddy, VP of IBM Watson Health, “No one company is big enough to transform an industry on its own. It takes a village to change,” I believe this healthcare ecosystem is still at its infancy and we should see a lot of M&A activities in the coming 5-10 years as big players either build or buy technologies/customers.
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