DAIM: Inside The Algorithm | Hybrid Simulation, Model Reuse and AI in NHS Planning

And then specifically, more recently, you've been working on a very contemporary topic of healthcare waiting lists in the NHS.

And there isn't almost a month goes by without some discussion of NHS waiting lists for some operation or procedure or other.

So this is really interesting.

important topic but also quite a quite quite a complex one as well do you want to give us a little bit more on on what your work's been in that area

i'd say that at the strategy unit i kind of got into the waiting list area because a colleague of mine professor muhammad muhammad um kind of when i first joined the unit realized oh i've got a bit of background in operational research and simulation and queuing so he brought me into um to basically learn a little bit more about what he was already working on, which was to kind of orchestrate different efforts that were going on across the country in waiting lists.

I got into it through that and then started working with academics at Durham University and other NHS staff.

down in Bristol and we were looking at the problem of how many people do you need to remove from the waiting list say every month or every year to actually achieve the waiting list target that's been set which is 18 weeks basically so 92% of the people waiting on the waiting list will have waited 18 weeks by the end of this parliament so I mean, we're nowhere near that at the moment, but we are moving in the right direction.

So, yeah, it was a great starter problem, but it's not the only problem in waiting lists.

And it ended up being, well, you could look at it from a national viewpoint, like as a national waiting list or down to specialty level.

So there's complexity there from there being...

many, many different problems to solve across many specialties, across many trusts, etc.

So again, it's a problem of scale.

So queuing theory, which you're using to model these essentially very large virtual queues, they're not actually people standing in line.

There are people just waiting at home, of course, and sometimes waiting many, many months, even years to get to the top of the queue and get their operation.

A really powerful technique for capturing these challenges in any complex organization do you find there's a do you find there's a bit of a gap between you know using a theory like that to inform uh inform the process that or inform a hospital or um you know it's just around how many people should be removed from from a list and then how they actually then go about executing it

Yeah, so I think what was done was a really important first step and really important question to answer.

I mean, people want to know how to achieve the target.

So the first thing you need to do is tell them how to get there.

So how much relief capacity they need to... give the system in order to move towards the target.

So that is an important first question.

Secondary to that is the almost operational question of then if I need to take 20 people off my waiting list this month, who on the waiting list do I need to take off? And that kind of brings in questions of prioritization and deterioration whilst you're on the waiting list because some people have been waiting a very long time.

And it's not as simple as just taking those that have waited the longest off the list.

There's some dynamics in there that mean that, you know, there's definitely better ways to approach it than that.

So that's where I feel like AI could really make some differences or AI techniques, let's say, could be really powerful to not just look at how many people to take off, but How do we actually approach that on a daily basis or weekly basis, scheduling who should come in and when?

I'd like to give a sense of what your levers are here when you're making these suggestions or adjustments.