No Queue Vaccination

Monday, January 4, 2021

Vaccinating millions of vulnerable people in the middle of winter requires a safe, efficient and effective process.

It is not safe to have queues of people waiting outside in the freezing cold.  It is not safe to have queues of people packed into an indoor waiting area.

It is not safe to have queues full stop.

And let us face it, the NHS is not brilliant at avoiding queues.

My experience is that the commonest cause of queues in health care processes something called the Flaw of Averages.

This is where patients are booked to arrive at an interval equal to the average rate they can be done.

For example, suppose I can complete 20 vaccinations in an hour … that is one every 3 minutes on average … so common sense tells me it that the optimum way to book patients for their jab is one every three minutes.  Yes?

Actually, No.  That is the perfect design for generating a queue – and the reason is because, in reality, patients d=won’t arrive exactly on time, and they won’t arrive at exactly one every three minutes, and  there will be variation in exactly how long it takes me to do each jab, and unexpected things will happen.  In short, there are lots of sources of variation.  Some random and some not.  And just that variation is enough to generate a predictably unpredictable (chaotic) queue.

The Laws of Physics decree it.


So, to illustrate the principles of creating a No Queue design here are some videos of a simulated mass vaccination process.

The process is quite simple – there are three steps that every patient must complete in sequence:

1) Pre-Jab Covid Check + Identity Check + Clinical Check.
2) The Jab.
3) Post-Jab Safety Check (15 minutes of observation … just-in-case).

And the simplest layout of a sequential process is a linear one with the three steps in sequence.

So, let’s see what happens.

Notice where the queue develops … this tells us that we have a flow design problem.  A queue is sign that points to the cause.

The first step is to created which is called a “balanced load, resilient flow” design.

Hurrah! The upstream queue has disappeared and we finish earlier.

OK. Let’s scale up and have multiple parallel lanes running our balanced load, resilient flow design with an upstream FIFO buffer and a “round robin” stream allocation policy (the sorting hat in the video).  And can we see some process performance metrics too please.

Good, still no queues.  We are making progress.  Our average utilisation is less than 90% (the Accountants won’t be happy with that) and the Staff are grumbling that they don’t get rest breaks.

Let’s add a Flow Coordinator to help move things along.

Oops!  Adding a Flow Coordinator seems to make things worse rather than better!  And we’ve increased costs too (so the Accountants will be even less happy) and the Staff are still grumbling because they still don’t get any regular rest breaks.  And the Flow Coordinator is grumbling because they are running around like a blue a***d fly.  Everyone is unhappy now!

OK. To keep everyone happy take the Flow Coordinator out and give the Staff regular rest breaks.

H’mm.  We still seem to have queues.  Maybe we just have to live with the fact that patients have to queue so long as the Accountants are happy and the Staff  get their breaks.

But … what if we flex the Flow Coordinator to fill staggered Staff rest breaks and keep the flow moving calmly and smoothly all day without queues?

At last! Everyone is happy. Patients don’t wait. Staff are comfortably busy and also get regular rest breaks. And we actually have the most productive (value for money) design.

This is health care systems engineering (HCSE) in action.

PS. The Flaw of Averages error is a consequence of two widely held but invalid assumptions:

  1. That time is money. It isn’t.
  2. That utilisation and efficiency are the same thing.  They aren’t.