Last week, Parkway Pantai, one of the largest integrated private healthcare groups in Asia and UCARE.AI, a Singapore-based AI healthcare startup, announced that they have been using Artificial Intelligence (AI) to dynamically generate personalised, more accurate hospital bill estimates that vary from the actual bill by a high 82 per cent accuracy rate on average, a significant 60-percentage-point improvement over the current bill estimation system. This means that eventually, all patients would receive highly accurate bill estimates that fall within an 18 percent margin from the final bill figure.

Using an advanced suite of AI and machine learning algorithms from UCARE.AI, Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East hospitals in Singapore will dynamically generate personalised bill estimates based on relevant parameters such as the patient’s medical condition and medical practices. It also takes into account the patient’s current age, revisit frequency and existing co-morbidities like high blood pressure or diabetes.

“Parkway Pantai has always been committed to enhancing price transparency of hospital charges. Our investment in this new AI-powered system gives patients more accurate hospital bill estimates and empowers them 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,” said Mr Phua Tien Beng, Chief Executive Officer, Singapore Operations Division, Parkway Pantai.

The new estimation system, which has been in use since November 2018, has made more than 10,000 predictions so far. In its first two weeks of going live, the AI system has already closed the average gap between the estimated and actual bills by 60 percent. The accuracy of its predictions is expected to improve over time as the AI collects and references more data through a process of self-learning.

The system analyses a multitude of dynamically changing parameters specific to the individual patient. The information is then used to automatically and quickly predict the patients’ bill size at different touchpoints, from pre-admission till their eventual recovery. As such, patients are better informed and empowered to seek the treatment option that is most cost efficient and effective for recovery.

In contrast, conventional bills estimation methods are based on statistical calculation of historical hospital bill sizes from past admissions up to two years ago. They are unable to account for dynamically changing factors such as disease aggravation and unexpected complications resulting in longer length of stay or additional unplanned surgeries.

Mr. Neal Liu, Founder and CTO of UCARE.AI said, “UCARE.AI was selected by Parkway Pantai for providing (1) the most accurate and precise predictions based on blind-testing, and (2) a cloud-based microservices architecture solution that offers flexibility, scalability, ease and speed in implementation. We are thrilled to work with Parkway Pantai, one of the largest global healthcare players who pride themselves on innovation and quality patient care, to debut UCARE.AI’s first revolutionary AI-powered system. Together, we seek to ride the wave of healthcare disruption and roll out more AI systems and services to benefit patients globally.”