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Patients to get more accurate bill estimates at Parkway Pantai hospitals

SINGAPORE — Patients at four hospitals under private healthcare group Parkway Pantai can now better gauge how much they have to pay for their hospital stays, through a predictive system powered by artificial intelligence.

At Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East hospitals, patients are getting more accurate bill estimates that vary from their actual bills by 18 per cent on average.

At Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East hospitals, patients are getting more accurate bill estimates that vary from their actual bills by 18 per cent on average.

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SINGAPORE — Patients at four hospitals under private healthcare group Parkway Pantai can now better gauge how much they have to pay for their hospital stays, through a predictive system powered by artificial intelligence.

Over at Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East hospitals, patients are getting more accurate bill estimates that vary from their actual bills by 18 per cent on average, the group said on Thursday (Dec 13).

Parkway Pantai is the first hospital group in Singapore to implement the system, which it said beats conventional bill estimation methods by 60 per cent. Usually, hospitals estimate patients’ final bills based on statistical averages of historical hospital bill sizes from past admissions, which may be up to two years old.

The new predictive system, which has been in use since November this year, was developed by Singapore-based healthcare start-up UCARE.AI. While Parkway Pantai was unable to say how many patients it has benefited, it has made more than 10,000 predictions so far.

The accuracy of its predictions is expected to improve as its AI collects and “learns” from more data.

It works by analysing parameters specific to individual patients, including their pre-existing medical conditions, age and frequency of re-visits. The information is then used to predict patients’ bill sizes at different points from pre-admission to their eventual recovery.

Conventional methods are unable to account for “dynamically changing factors”, such as disease aggravation and unexpected complications resulting in a longer stay, said Parkway Pantai.

Mr Phua Tien Beng, the chief executive officer of Parkway Pantai’s Singapore operations division, added: “Our investment in this new AI-powered system… empowers patients to make more well-informed decisions on the medical treatment options available. More importantly, it allows patients to have greater peace of mind over their healthcare expenditure so that they can focus on getting well.”

Ms Christina Teo, who co-founded UCARE.AI with Mr Neal Liu in 2016, said the company’s aim is to apply its predictive system to all aspects of the healthcare industry.

“(This will) help patients, providers, caregivers and payers manage patients throughout the life cycle of their diseases at lower costs,” she added.

The platform can also correctly identify the risk of rehospitalisation for certain Singaporeans, a function that some other hospitals in Singapore have expressed interest in, Ms Teo noted. She declined to reveal which hospitals they are.

Should they also be interested in the predictive bill estimation system, the system has to be tailored to individual hospitals as they have unique features, she said.

 

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