How UCARE.AI is making healthcare more efficient with AI

Neal Liu, CTO and co-founder of the Singaporean startup, explains how UCARE.AI’s solution is helping Parkway Pantai group improve its bill estimates by 60%

Neal Liu, CTO of UCARE.AI
UCARE.AI

A recent study by the consulting firm Solidiance forecasts that the ASEAN-6 countries - Indonesia, Malaysia, Philippines, Thailand, Singapore and Vietnam - have a multi-billion dollar healthcare challenge ahead resulting from the combination of an ageing society and unhealthy lifestyles.

To alleviate the burden on the health system, the report suggests a number of practical steps, including the streamlining of redundant processes and functions and the improvement of overall resource allocation.

Enter UCARE.AI, a Singapore-based healthtech startup founded in 2016 by Google veteran Neal Liu and the former CEO of investment firm Catpital and Mint Media, Christina Teo.

UCARE.AI’s mission centres around the early detection of preventable diseases and effective prioritisation of healthcare resources to help patients, payers, and providers alike. The startup employs a series of artificial intelligence (AI) and machine learning (ML) algorithms, deployed in a cloud-based microservices architecture, to provide sustainable and customisable healthcare solutions for doctors, hospitals, patients, insurers and pharmaceutical companies.

Helping Parkway Pantai optimise bill estimates

The first healthcare group to have implemented UCARE.AI’s solution is Parkway Pantai, Southeast Asia’s largest healthcare provider.

In November 2018, four of the group’s hospitals in Singapore - Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East - started generating personalised bill estimates for their patients based on their medical condition and medical practices.

According to Neal Liu, CTO and co-founder of the startup, Parkway Pantai’s goals were to “provide more accurate estimates to enhance price transparency of hospital charges, empowering patients to make more well-informed decisions on the medical treatment options available.” The key stakeholders participating in the project with the startup were the business, admission and IT teams.

Liu explains that his team worked closely with the hospitals to determine the project requirements. Hospitals usually estimate patients’ final bills based on statistical averages of historical bill sizes from past admissions, which may be up to two years old. The issues with this method are the inability to account for dynamically changing factors such as disease aggravation and unexpected complications resulting in a longer length of stay or additional unplanned surgeries.

The startup’s technology addresses these deficiencies by also taking into account the patient’s medical records and health history, such as suffering from diabetes or high blood pressure, to generate accurate bill predictions.

For Parkway Pantai, UCARE.AI’s solution did not require any user training or additional hardware, and minimal people and process changes were necessary when integrating it into the hospital’s front-end tool, according to Liu.

Beating the deadline

Although Liu’s estimated that the project duration would last three months since signing the agreement, UCARE.AI was able to implement it in just six weeks. He explains that the healthcare group was eager for change, so the organisational obstacles were slight.

There was transparency throughout the process, with stakeholders kept in the loop at all times. The accuracy of the billing system was checked during system integration and user acceptance testing.

Liu explains that UCARE.AI’s clients typically expect a 10% return on investment. But “in the first few months of the system being up and running, UCARE.AI’s system provided a significant 60% improvement over the prior bill estimation system,” he says.

During the first month of implementation, the estimation system also made more than 10,000 predictions. The accuracy of the predictions is expected to improve over time as the AI collects and references more data through a process of self-learning.

A recognised AI case

UCARE.AI has been recognised by Singapore’s government as the first successful case to align with the five principles espoused in the new Model AI Governance Framework set out in a white paper published by Personal Data Protection Commission (PDPC), the data protection arm of the city-state’s government. The five principles are: verifiability, accountability, transparency, fairness and human-centricity.

To keep up with the development of the healthcare and healthtech industries, Liu explains that UCARE.AI engages with healthcare professionals, actively participating in conferences and trade shows such as ConnecTech Asia 2019 to share the startup’s expertise and case studies and to help set standards for AI technology.

Although the startup is just three years old and only concluded its Series A funding in 2018, it’s not difficult to imagine more successful implementation cases for other healthcare providers in Southeast Asia.

Copyright © 2019 IDG Communications, Inc.

Survey says! Share your insights in our 19th annual State of the CIO study