Prioritises a hospital’s waiting lists based on clinical guidance from the Royal College of Surgeons into different priority categories

Prioritiser takes local waiting lists and uses machine learning to map these to Royal College guidance for post-Covid-19 case prioritisation. This provides an initial cut of waiting list activity into different priority categories (1a, 1b, 2, 3, and 4).
Prioritiser+ takes additional information from local systems (e.g. patient history from PAS) to create more nuanced prioritisation, such as taking account of patient history and likely impact of interventions. It can also provide an additional categorisation of cases according to their potential Covid-19 risk.

Example problem solved:
• Prioritise backlog waiting list to make sure people with more urgent need are prioritised
• Identify groups of people on the waiting list where the biggest impact could be made to their outcomes from earlier intervention.
• Understand the capacity required to deliver backlog demand

• Produces a summary list of cases prioritised according to national guidance
• Prioritiser can be run weekly or more frequently if required.
• Initial outputs are produced using existing and available information without requiring input.

• Once set up, Prioritiser runs quickly to produce consistent prioritisation lists.
• Additional information can be included in Prioritiser to enhance the quality of the lists produced
• Prioritiser learns over time from clinical input as outputs are checked and assessed.


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