A new way to work out demand

by Rob Findlay

What is the “demand” for a waiting list service?

We could define demand as being the same as additions to the waiting list: then it would match referrals if we were looking at outpatients, or decisions to admit if we were looking at elective admitted patients. But would that be a useful definition? A lot of patients who are added to the waiting list never get treated, because instead they end up being removed from the waiting list for other reasons (for example, they change their mind, or are removed because they DNA). If we were to lay on enough activity to match additions to the list, then our waiting list would actually shrink because of the removals, which would be nice, but not necessarily what we intended if we merely wanted to keep up with demand. So defining demand as being the same as additions isn’t necessarily the most useful approach.

Instead, Gooroo Planner defines “demand” as the number of patients added to the waiting list who will eventually end up as activity. This means that demand and activity can be compared directly: recurring activity is whatever is needed to keep up with demand, and non-recurring activity is anything extra.

The next question, then, is how should we measure demand? Traditionally we have calculated historical demand as being historical activity plus the growth in waiting list, adjusted for removals. This was reasonable in the days when the size of waiting list was scrutinised centrally and agonized over locally, when historical list size data was readily available, and when changes in list size usually reflected reality. In those days, additions data was not closely watched, and was usually less accurate than changes in the list size. However, none of this can be taken for granted now.

Nowadays, all stages of the patient pathway are linked up to track referral-to-treatment waiting times, and this has helpfully improved the accuracy of additions data. At the same time, investments in IT have sometimes meant that changes in list size reflect administrative actions, not an imbalance between activity and demand; new IT systems can lead to short-term errors in counting, and then one-off waiting list validation exercises can cause dramatic apparent cuts in the list size. There is another problem too: if waiting list snapshots are not regularly archived over time, it can be difficult to recreate this data afterwards. Counting the patients on the waiting list today is much easier than working out how many were on the list a year ago.

What is the solution? An good alternative method for calculating demand might ignore past changes in the size of waiting list, and instead calculate demand as additions less removals. We know that additions are always balanced by activity, removals and the change in list size, so the maths must be fairly straighforward. Indeed it is, and if you want the details they are all laid out in the link below.

Starting in a few weeks time, users of Gooroo Planner will have a choice between these two methods for calculating demand: either based on activity plus growth in list size (if list size data is more reliable), or on additions less removals (if additions data is more reliable).

If you want to make a permanent choice of method and use it always, then you will be able to set that up in your profile under Dataset Settings. Alternatively you can choose between the two methods on a case by case basis, and even use different methods for different services within the same model.

Before you ask, yes the change will be “backwards compatible” and your existing datasets will still work fine. That’s the beauty of Gooroo Planner’s flexible design: we can add the new without overturning the old.

Follow this link for the maths: Demand calculation method explained

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