In this example we'll be looking at a service that accepts around 3000 referrals each year. Each referral has an average of 2 appointments during their pathway, and the service is able to provide 40 appointments each week. There is a target to see 90% of these referrals within 12 weeks (84 days).
Due to the coronavirus pandemic the service is in a dire situation.
There is a backlog of 1200 referrals.
The service is able to reduce the average points of contact to 1 by doing the second appointment over the phone.
But at the moment they only have the ability to deliver 6 appointments a week due to lack of venues.
We can use CReST to help us manage this backlog.
Using these values the model tells you the service would need to increase available appointments to a minimum of 24 a week in order to meet the 90% seen within 84 days target.
At 24 appointments we will not be anticipated to have any breaches, and each referral will be waiting an average of 6 days for an appointment. We will be utilising 96.2% of our available resources.
What we can also see is that if we drop the available appointments down any lower than 24 we start to get a % Utilisation of over 100%, meaning we’re using more resource than is physically available. Because of this the model cannot calculate an average wait figure at these appointment numbers – it is not possible to meet the target. We also see that 100% of referrals would be expected to breach.
This also highlights the massive difference adding even one extra appointment each week can make - by increasing from 23 to 24 appointments we have moved from an impossible scenario, to one in which every referral will be seen within the 12 week waiting target.