About CReST
NHS England initially developed the CReST demand and capacity tool in 2014 with Healthcare Decisions Ltd to support with the planning and commissioning of children and young people’s mental health services.
Since 2019, the tool has been further developed on a web-based platform by NHS South, Central and West, based on user feedback and testing to ensure that users’ demand and capacity requirements are met for their pathways and services.
In 2022, the CReST tool was re-launched with the following benefits and enhanced functionalities for users to adopt to their demand and capacity modelling:• Deliver demand and capacity models and scenarios for services and pathways simultaneously.
• Modelling can be directly populated from a simple data-file which is downloadable from the tool.
• Generate options for the user to download charts and data tables / images, which can be used for reporting business case purposes.
• Deliver a 12-month forward view of demand and capacity, considering any potential emerging backlog.
• Deliver portable digital outputs to users and customisable management reports.
The CReST team is proud to be the winner of the ‘Innovation in Mental Health’ award at the Health Business Awards 2022. The NHS South, Central and West team are honoured to be recognised for the achievements and progress made in improving the overall experience and care of mental health patients.
The CReST team continue to work with a range of health and care services and provide bespoke demos and one-to-one support as well as help resolve any data queries for input into the tool. If you have any queries on CReST, please reach the team on: This email address is being protected from spambots. You need JavaScript enabled to view it.
CReST is based on ERLANG Theory
The CReST tool is designed on the principles of ‘queuing theory’, which is a mathematical study of waiting lines and queues. It uses a measure called 'erlang' to describe utilisation of an available capacity in a queue. Erlang is described as a 'family' of equations that are useful for different aspects of standard queue planning such as call handling or network traffic. These same equations can also be applied across many parts of healthcare planning too!
Think of Erlang as a translator box, where you put in values like calls per hour, handle times, and number of agents available. These are “deterministic” values, in that they are easily measured. The outputs from the translator box provides answers to “probabilistic” questions such as, “What are the odds a call will have to wait in a queue?” or “What are the odds a call will wait more than 30 seconds?”. This is the 'engine' of the CREST.
When applying Erlang calculations to healthcare settings, we are translating 'calls per hour', 'handling times' and 'number of Agents' to 'referrals per week', 'points of contact' and 'number of appointments/beds available'.
Think of Erlang as a translator box, where you put in values like calls per hour, handle times, and number of agents available. These are “deterministic” values, in that they are easily measured. The outputs from the translator box provides answers to “probabilistic” questions such as, “What are the odds a call will have to wait in a queue?” or “What are the odds a call will wait more than 30 seconds?”. This is the 'engine' of the CREST.
When applying Erlang calculations to healthcare settings, we are translating 'calls per hour', 'handling times' and 'number of Agents' to 'referrals per week', 'points of contact' and 'number of appointments/beds available'.
