Advancing our technology Product differentiation and...

Advancing our technology

Product differentiation and technological evolution will remain a key priority for Alcidion – either through continual internal R&D or acquisition. Alcidion talked about being agile and able to capitalise on the ‘big data’ era in health care via product differentiation and a highly compelling value proposition.

I previously noted that some of the structural changes in government funding policy and etc may assist in facilitating a faster transition to the increased introduction of technological solutions in healthcare. For example, the imposition of financial penalties on hospital-acquired complications as opposed to the old approach of providing extra funding to address the complication. It is clear is that there are firm efforts to reduce healthcare-associated complications through pricing and financial penalties for such events, and equally firm efforts to make every complication count.

The more I reflect, the more complex our healthcare system appears to me with multiple moving parts – public hospitals and the increasing number of private hospitals. In effect, this complex system with medical data stored all over the entire system in different forms and disparate legacy IT system is posing some fundamental challenges. When they do not work smoothly together, it all falls apart leading to sub-optimal outcomes for both the patient and the funding system (via government taxes and private health insurance premiums).

It was also an important and positive move by Alcidion to stress during the Chairman’s address and CEO presentation that the Miya platform and AI are both ultimately tools and inform decision-making (via detecting early warning signs) and are not meant to replace medical diagnosis. In my view, this is a very important point to stress as some people whom I spoke to were under the impression that AI would replace doctors – potentially underpinning the general unfounded reluctance towards the introduction of AI in healthcare.

The simple example below is how I would see things work in practice moving forward:

1. New medical notes based on visible symptoms would be recorded once patient is admitted into hospital or the like
2. Using that newly recorded medical information in conjunction with past medical records (specific to the individual) plus additional medical data feed from the general population, the AI platform would produce a medical report to identify potential complications and early warning symptoms - for example, leaky, fragile blood vessels that occur at the back of the eye in poorly controlled diabetes.
3. The patient experience with the GP or specialist would remain unchanged as human clinical diagnosis is still required, whether in person or video.

The key advantages are that such a practice would be much cheaper on a per diagnosis basis and reduce the waiting list considerably. With an internet connection, imagine the cost savings of providing healthcare to patients in regional areas and etc.

Medical artificial intelligence could lead to accessible, affordable health care for everyone.

As always, there would be some challenges. Nevertheless, it is important that we acknowledge these challenges to promote an informed debate. For example, data security breaches, ownership of the data and whether some medical data are inherently identifiable.