Technology show-stoppers

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Photo by nappy on Pexels.com

Wikipedia defines it as “Showstopper (originally a performance or segment of a theatrical production that induces a positive audience reaction strong enough to pause the production)”.

This was my initial reaction when I read the Financial Times puff piece on big tech making “advances” for the medical field.

Here’s a blurb:

Apple has used data collected from wearers of its Watch for the most ambitious research study yet undertaken with “wearable” technology, tracking heart irregularities across a large population in real time. Google has drawn on both expert knowledge and extensive patient data to produce a specialist health search engine. And Microsoft, through its cloud computing platform Azure, has taken on expansive projects including one with pharmaceutical group Novartis that covers everything from manufacturing and finance operations to drug discovery.

Apple Watch is seriously overrated. Its iOS is a monolithic kernel that’s bursting at seams with pointless menus and a stunted ecosystem of applications (in the name of privacy).

Microsoft Azure is overrated for its cloud platform wherein the Linux dockers are most popular because Windows, by itself, is pretty useless 🙂 Oh well, the general consensus is that it is the choice of platform for those CIO’s who have refused to see alternatives. What can go wrong with Microsoft? (I am biased towards AWS, even if they have questionable business practices).

The snake oil comes here:

Combining visual information about how the 41 operating theatres at Beth Israel Deaconess Medical Center in Boston are being used, together with data about patients and other information, the healthcare provider improved its operating efficiency by 30 per cent, Kass-Hout says.

  1. What kind of efficiencies? What was wrong before the introduction of AI and what AI corrected?
  2. How did AI help to fine-tune the processes?
  3. What kind of processes led to the poor utilisation that could be remedied without the use of AI?
  4. What algorithms helped to achieve those aims?
  5. What was the run time or the cost to the organisation?
  6. What is the return on investment?

Here’s another wrench:

One is ensuring the powerful new capabilities of today’s big data and AI technologies work with the existing processes in the health system. The use of Apple’s Watch to monitor for heart problems in real time, for instance, could throw off more patient data than systems can handle.

It is not the data problem. It is a filtering problem!

We need to go beyond the outliers and actually question the nonsense being peddled.