23rd April 2024 – (Hong Kong) Hong Kong’s public healthcare system, renowned for its robustness, is nevertheless plagued by daunting challenges, chief among them being the inefficiencies of Accident and Emergency (A&E) departments. Patients often endure lengthy waiting times, sometimes stretching into hours, which not only strains patient satisfaction but also puts immense pressure on the healthcare infrastructure.

As of 2023, the waiting times in A&E departments across Hong Kong’s public hospitals have been critically high. This not only affects patient outcomes negatively but also reflects inefficiencies that could potentially be mitigated through innovative approaches. The traditional methods employed—increasing healthcare fees, augmenting the number of doctors, or sourcing medical professionals from overseas—have provided limited relief. These approaches do not address the root cause of the delays, which often lie in systemic inefficiencies and outdated administrative processes.

The integration of Artificial Intelligence (AI) and advanced technological solutions presents a promising avenue to revolutionise how A&E departments operate. AI can streamline patient intake processes, predict patient inflow, and optimise resource allocation effectively. By adopting such technologies, Hong Kong’s public hospitals could significantly reduce waiting times and enhance patient care delivery.

Implementing AI-driven queue management systems could transform the patient experience in A&E departments. These systems can provide real-time data on waiting times and patient queues, allowing hospital staff to manage patient flow more efficiently. Moreover, AI can triage patients based on the urgency of medical attention needed, ensuring that critical cases receive immediate care, thereby optimising the overall efficiency of medical responses.

AI can be utilised to predict peak times and potential patient influx through historical data analysis, enabling hospitals to prepare and allocate resources accordingly. Predictive analytics can also identify potential bottlenecks and suggest optimal pathways for patient care, thus reducing idle times and speeding up the treatment process.

Incorporating telemedicine platforms can help decongest A&E departments. AI-enhanced telemedicine can provide preliminary medical consultations remotely, filtering out cases that require immediate physical medical attention from those that can be managed remotely. This not only reduces physical footfall in hospitals but also allows patients to receive timely medical advice without the need for an A&E visit.

Globally, several healthcare systems have successfully integrated AI to enhance their emergency services. For instance, some European and North American hospitals have adopted AI for symptoms checking and preliminary diagnostics, which has effectively reduced unnecessary A&E visits. Hong Kong can take cues from these international best practices, tailoring AI solutions to fit local contexts and specific challenges.

Adopting AI in public healthcare, particularly in emergency services, is not devoid of challenges. Issues such as data privacy, the need for substantial initial investment, and potential resistance from traditional healthcare practitioners need to be addressed. Moreover, the AI systems must be impeccably reliable, as inaccuracies in emergency healthcare can have dire consequences.

To successfully implement AI in A&E departments, a robust policy framework must be developed. This framework should ensure strict data protection laws, clear guidelines on AI usage in healthcare, and continuous monitoring of AI systems for efficacy and ethics. Additionally, engaging with healthcare professionals and the public to gain their trust and buy-in is crucial for the smooth adoption of AI technologies.