How to plan against all eight RTT waiting time targets

by Rob Findlay

Lucky, lucky NHS: eight referral-to-treatment waiting time targets when just one would do a better job. All over England, Trusts are complaining about the irrelevance and perversity of the target regime, but their pleas are (mainly) falling on deaf ears. Higher up the system, performance managers want green boxes, only green boxes on the RAG (red-amber-green) ratings. You shall achieve the targets, they insist: all eight of them.

Here, then, is a little helping hand with that negotiation. You can offer them their green boxes, with pleasure. But it’s going to cost them, and we’re going to show how you can work out the bill.

1) 95th centile RTT waiting time for incomplete pathways (target: 28 weeks)

Let’s start with the only target that is actually sensible: the 95th centile referral-to-treatment (RTT) waiting time for incomplete pathways (or, in plainer English, the waiting time that the top 5 per cent of the waiting list has exceeded). It’s sensible because it delivers the third of the four key principles of good waiting list management, and (with good planning and monitoring) is relatively straightforward to implement without undermining the others.

The four principles are:

  1. treat patients with higher clinical priority first
  2. treat patients with similar clinical priority in turn
  3. treat the least-urgent patients within a reasonable time
  4. don’t waste capacity

But how do we actually work out the activity needed to achieve this particular target? It isn’t easy, and it took many years of research to find a good solution to this problem. The first step in the calculation is the hardest: working out the size of waiting list that is consistent with the waiting times target. When that’s done, the remaining steps aren’t too bad. For the sake of this post, we’ll assume you have a well-researched model that does all this for you.

Once you have a suitable model, the calculation is easy: you just specify the target and let the model take care of everything. In Gooroo Planner, for instance, you can either load up targets for every service separately, or not bother and just set up default values like this:

Setting default values in Gooroo Planner: 95th centile incomplete pathways
Setting default values in Gooroo Planner: 95th centile incomplete pathways

To give an indication of how tough each of the targets is, we will use a benchmark waiting list that is well-managed according to the four principles above, and show how it has to get smaller and smaller as each new target is applied (keeping all other attributes of this benchmark list constant: addition rate, cancellations, urgency, etc).

To achieve 95 per cent of incomplete pathways within 28 weeks, sustainably and safely, without taking any of the other targets into account at this stage, our benchmark list starts out with 200 patients on it. We need two copies of this benchmark list now, one for admitted and one for non-admitted pathways, and we will track the fates of those two benchmark lists below.

2) 95th centile RTT waiting time for admitted patients (target: 23 weeks)

The next six targets we are going to look at are all based on those patients who were lucky enough to be treated or discharged over the chosen time period, as opposed to those patients who are still waiting. The trouble with these targets is that Trusts can achieve them by being selective about which patients they choose to treat.

For instance, any Trust could achieve 95 per cent of admissions within 23 weeks, cost-free, simply by picking 19 short-waiting patients for admission before picking an over-23-week waiter. This would violate the second principle: that patients with similar clinical priority should be treated in turn.

But on the assumption that you want to do the job properly, by actually achieving short waits on the waiting list as well as in your admissions profile, this target is easy to model in Gooroo Planner. Just set the data up like this:

Setting default values in Gooroo Planner: 95th centile admitted pathways
Setting default values in Gooroo Planner: 95th centile admitted pathways

It turns out that this target is more challenging to achieve sustainably than the incomplete pathways target above, and our benchmark waiting list (applied now to admitted patient pathways) must have no more than 155 patients on it.

That means we can now ignore the incomplete pathways target above because, if we achieve this admitted patient target while following the principles of good waiting list management, then we will have a small enough waiting list to automatically achieve the incomplete pathways target too.

3) Percentage admitted within 18 weeks RTT, adjusted basis (target: 90 per cent)

This is the best-known of all the RTT waiting time targets, though it too suffers from the problem that it is easy to achieve if you abandon patients who have already exceeded 18 weeks.

If you want to achieve it safely and sustainably, it is similarly easy to model:

Setting default values in Gooroo Planner: 90 per cent of admissions within 18 weeks
Setting default values in Gooroo Planner: 90 per cent of admissions within 18 weeks

Now our benchmark admitted-pathway list must not exceed 132 patients, if well-managed, and we can forget about the previous target too as it will automatically be met if we achieve this one.

4) 95th centile RTT waiting time for non-admitted patients (target: 18.3 weeks)

This target has all the same perverse incentives as the admitted patient targets above. If, again, we assume that we will do the job properly and manage the waiting list well, it is easy to model safely and sustainably:

Setting default values in Gooroo Planner: 95 per cent of non-admissions within 18 weeks
Setting default values in Gooroo Planner: 95 per cent of non-admissions within 18 weeks

If our benchmark list is now a non-admitted pathway, it must not exceed 129 patients.

5) Percentage non-admitted within 18 weeks RTT (target: 95 per cent)

This target duplicates the target above, but with a slightly tougher limit of 95 per cent within 18 weeks instead of 18.3 weeks. Originally this target (together with the percentage admitted within 18 weeks) was going to be dropped, but they had to be reinstated as they are both laid down in law.

You can model this target easily, just like the previous target but with 18 weeks instead of 18.3. Our benchmark non-admitted list now must shrink a little further to 127 patients.

6) Median RTT waiting time for admitted patients (target: 11.1 weeks)

The median targets make things a little more complicated to model, and to understand. But we are looking for ways to have a sensible discussion about the costs of achieving green boxes right across our 8 RTT targets, so let’s dive in and find a way to do it.

What is meant by this median target? If we look at the waiting times experienced by patients admitted over a period of time, the median admitted waiting time is the waiting time that half of them exceeded. If we were managing our waiting list well, according to the four principles, what would the median be then?

In all the main surgical specialties, only a minority of patients are clinically urgent. The remaining majority, who are non-urgent, should be admitted in turn and therefore all of them should experience roughly the same waiting time. The median patient and the 95th centile patient are both among this majority, and should therefore experience similar waiting times; so it follows that the median waiting time should be close to the 95th centile waiting time if we are managing our waiting list well.

But, for admitted patients, the targets are asking for a 95th centile of 23 weeks, and a median of only 11.1 weeks. How can we achieve that? Quite easily, as it turns out, although it does require us to violate the principles of good waiting list management. All we need to do is pick a lot of non-urgent (i.e. routine) patients and expedite them, for no other reason than to meet the target. Yes, that is brutally unfair on the other routine patients, who will wait longer as a result, and we can put that argument to the people who enforce the targets. But if they want all their boxes to be green, that is what they are going to get.

To model this target, we can pretend that half our patients are urgent and need admission within 11.1 weeks. They aren’t, but that is what the target demands. We just leave our long-wait target at the most demanding level we discovered above (for admitted patients, 90 per cent of admissions within 18 weeks). So that means we set our model up like this (we’ll show the data in data entry style this time):

Data code Data description Value
FutPCWaiting1 Future percent waiting at time 1 50%
FutWaitTime1 Future waiting time for time-limited patients 1 11.1
TgtMaxWait Future target waiting time 18
TgtMaxWaitPC Future percentage within future target waiting time 90%
TgtMaxWaitType Flag whether target max wait is flux or snapshot based f

With 50 per cent of admissions within 11.1 weeks, and 90 per cent of admissions within 18 weeks, our benchmark waiting list for an admitted pathway must not exceed 112 patients. That is 15 per cent smaller than before we introduced the median waiting time target, and that is the extra financial cost of the median target.

(To model this more precisely, it would be better to specify two levels of urgency, with the first one being the true clinical urgency of the service; if urgency rates are significant then the waiting list will need to be even smaller than this.)

7) Median RTT waiting time for non-admitted patients (target: 6.6 weeks)

Exactly the same process applies to the median for non-admitted patients, except that now 50 per cent are non-admitted within 6.6 weeks, and our target is specified as 95% within 18 weeks. Now our benchmark waiting list for non-admission must not exceed 97 patients, which is 24 per cent smaller than the well-managed non-admitted list when no median target was applied.

8) Median RTT waiting time for incomplete pathways (target: 7.2 weeks)

This last target is the trickiest of all. To be honest, we have not worked out a way of incorporating it directly into the model. Nor can we think of any purpose to this target that is not already achieved much better by the 95th centile for incomplete pathways.

However we have done some side calculations to work out whether, in a well-managed waiting list, this target would be more or less challenging than the “median + longwait” targets we have just considered. If it’s less challenging, that is good news because we know that, if we met the admitted and non-admitted targets above, then the median incomplete pathway would be met too. If it’s more challenging, then we need to work it out specially. So which is it?

Good news: it turns out to be less challenging, and that conclusion holds under all reasonable scenarios for surgical clinical priorities and for the management of expedited routine patients. That means we can neglect this target, knowing that in a well-managed list everything should turn out alright for our median incomplete pathways, so long as our waiting list is small enough for the other targets to be met.


So, in summary, this is how we should set up our planning models to achieve eight green boxes on our RAG ratings.

For admitted patient pathways, we should specify the level of clinical urgency in the casemix, and then add a second level of urgency so that only 50 per cent of patients remain on the list at 11.1 weeks. The waiting time target is 90 per cent within 18 weeks on a flux basis.

For non-admitted patient pathways, we specify the level of clinical urgency, and then our second level of urgency has 50 per cent of patients remaining at 6.6 weeks. The waiting time target is 95 per cent within 18 weeks on a flux basis (as opposed to a waiting list snapshot basis).

Given those inputs, the model will work out the activity, capacity and money needed to deliver the specified targets, provided we manage the waiting list well. If we achieve all that, then the other targets should simply fall into place, as they are all less demanding and would be achieved even with a larger waiting list.

In practice we would want to work out some other things too. Firstly, we might repeat the calculation without the median targets, just to show the extra costs that are pointlessly incurred in achieving a less-fair waiting list. Secondly, and particularly for the admitted patient pathway, we would want to model the pathway stages separately in order to work out capacity and money.

It’s been a long slog, but worth it. Now we know how to offer eight green boxes. Who knows, one day we might get a sensible target regime that means we don’t have to?

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