AI is changing how we approach healthcare. In a world where digital transformation is more of a necessity than an option, the spotlight turns to creative ways to improve patient outcomes and streamline healthcare processes.
Synapxe, Singapore’s healthtech agency, is navigating this shift by harnessing AI and emerging technologies. With a focus on predictive analytics and disease detection, Synapxe aims to elevate the standard of care and operational efficiency within the healthcare sector.
Frontier Enterprise recently had the opportunity to speak with Andy Ta, Synapxe’s Director Data aNalytics and AI (DNA) and Chief Data Officer. Ta delves into the challenges and achievements of implementing AI in healthcare, Synapxe’s plans for expanding its AI initiatives, and the potential impact of emerging technologies on the future of healthcare in Singapore.
What are the key AI initiatives that Synapxe is currently focused on and how they’re set to transform healthcare?
As Singapore’s national healthtech agency, Synapxe is actively using advanced deep learning and AI technologies to improve medical analyses, readings, and detection processes for patients in healthcare. Here are some key AI initiatives which Synapxe is currently focusing on:
- Assisted Chronic Disease Explanation using AI (ACE-AI): Developed to support general practitioners (GPs) in obtaining personalised health insights for their patients, this tool employs a deep learning algorithm to identify risk factors, automate risk calculations, and detect early signs and risks of chronic diseases. It has been introduced in a pilot program with 18 GPs at the start of 2024, aimed at enhancing chronic disease management by healthcare professionals.
- AimSG – AI-Powered Medical Imaging Diagnostics: AimSG is a platform designed to foster the adoption of AI in medical imaging diagnostics. Developed collaboratively by Synapxe, SingHealth, and NTT Data, this vendor-neutral platform seeks to streamline the development, testing, and deployment of imaging AI models. It incorporates AI models from various sources for different imaging modalities, streamlining the analysis of medical images. The platform has undergone pilot testing at Changi General Hospital and Singapore General Hospital, and is considered for further implementation in other healthcare institutions. Its design supports the efficient triaging of patients with urgent care needs and aids radiologists in creating more precise and efficient radiology reports, which could improve the quality of clinician diagnosis and minimise the frequency of unnecessary tests and procedures.
- Augmented Video Analytics for Medication Adherence (AV-MED): Developed in collaboration with the National Healthcare Group Polyclinics, the AV-MED prototype is designed to enhance medication adherence among patients. Currently in an early experimental phase, it utilises object detection AI and video analytics to monitor medication intake. Synapxe plans to further refine this technology for broader applications, including home care and the management of chronic or infectious diseases.
Can you discuss how national data is stored, and used, to accelerate these AI initiatives?
Synapxe has developed new systems to support AI initiatives and, more broadly, to tech-enable a healthier Singapore.
Synapxe contributed to the National Health Information Grid (NHIG) by creating the Healthier SG (HSG) Admin Repository for storing administrative and non-medical data of patients. This repository, along with other data sources, provides a comprehensive view of patient records. The establishment of the Healthier SG Gateway, an API gateway, facilitates secure data exchange and access to services across public and private healthcare providers. Additionally, to assist the Ministry of Health and public healthcare clusters with enrolment operations, we introduced the Healthier SG Cluster Resident Relationship Management systems. These developments also bolster the AI initiatives previously mentioned.
The NHIG promotes interoperability and data sharing between public and private healthcare sectors. This capability is vital for HSG, which relies on extensive information flow and data exchange among various ecosystem entities to deliver comprehensive care.
As a result, GPs can access a wide range of HSG services, including patient information, immunisation, and screening records, through their own CMS. This access is crucial for enabling GPs to offer holistic care efficiently without having to navigate through multiple systems to gather necessary patient details.
What are the most significant challenges Synapxe is facing in implementing AI solutions, and how do you overcome them?
Businesses across various industries are starting to see how they can improve efficiencies and save costs through AI. Although still in the early stages of unlocking its full potential, AI is increasingly being integrated into workflows, with a growing understanding of its practices, ethics, governance, and deployment. Currently, generative AI has the potential to automate many tasks, with businesses and users starting to see initial results, such as the personalisation of medication based on individual genetic profiles.
However, AI is not a silver bullet. Because it is still so new, business leaders must have a clear strategy on how to use AI to truly transform their operations.
In the healthcare industry, it is critical to consider an institution’s load, capacity, and users (e.g., clinicians and healthcare providers) in the implementation of AI. We actively engage our partners, and while this requires significant patience and effort, it helps us understand their concerns and needs, enabling us to deploy AI solutions that add value to their experiences.
Another challenge is the lack of expert knowledge among both users and leaders. At Synapxe, we bridge this gap by mobilising talent through partnerships and actively involving our employees and potential hires in our digitalisation journey, encouraging them to explore new technologies like AI, in line with our vision and the ultimate goal of adopting such technology.
What are Synapxe’s plans for expanding its AI initiatives? Are there other specific emerging technologies which you’d like to share here?
Currently, our focus is on conducting successful pilot trials for the initiatives previously mentioned: ACE-AI, AimSG, and AV-MED.
Regarding emerging technologies, Synapxe is collaborating with the Health Sciences Authority of Singapore (HSA) on the national spontaneous adverse event (AE) monitoring programme, to ensure the safety of health products in the country. We have co-developed the Active Surveillance System for Adverse Reactions to Medicines and Vaccines (ASAR), a pioneering nationwide application that analyses structured healthcare data and unstructured clinical notes from all public acute hospitals to detect and validate drug safety signals.
HSA and Synapxe will continue to refine ASAR to improve AE monitoring and to prepare for potential future health threats, including the impending Disease X (a hypothetical pathogen that may cause an epidemic or pandemic in the future). The system’s infrastructure, technology, and operations will be continuously enhanced, allowing HSA to use real-world data for AE risk detection and determine the overall benefit-risk balance of medicines and vaccines, ensuring public safety.
Additionally, Synapxe has developed the Community Acquired Pneumonia and COVID-19 Artificial Intelligence Predictive Engine (CAPE), an AI-enabled tool specifically designed to predict the severity of pneumonia in patients, including those with COVID-19, based on chest X-ray images. Pneumonia is one of the leading causes of death worldwide and plays a significant role in the deterioration of COVID-19 patients.
This AI predictive engine also enables closer monitoring and treatment of patients with severe pneumonia, aiming for improved outcomes through timely triaging and treatment. CAPE was co-developed by Synapxe and a multi-disciplinary team at Changi General Hospital (CGH), which sought to fortify their preparedness and response strategies as COVID-19 first reached Singapore.
Separately, CAPE has the potential for broader application beyond Singapore, offering a method to identify and predict the severity of respiratory infections worldwide. The CGH team is currently working to validate the model across other public health institutions in Singapore, enhancing its reliability. Additionally, they are exploring collaborative approaches, such as making it available as freeware on a research platform for interested researchers.
