The physical and mental health effects of Covid, and Long-Covid, continue to consume significant healthcare capacity to treat urgent and acute phases of care. While this is critical to population health, this consumption of limited capacity has disrupted other, equally important planned care. Lower referral rates for long-term conditions caused by fewer patients accessing GPs and cancelled elective surgery due to a lack of ICU beds, for example, contribute to a backlog of delayed care which can lead to more severe cases or even mortality.
In order to understand the extent to which this backlog has occurred, and establish the capacity required to reduce it to an ‘acceptable’ level, we have created a model which examines elective medical and surgical specialty waiting lists through weekly changes from referral patterns and capacity availability. The model runs over three years from January 2019 so that periods of pre-Covid (to March 2020) and Covid (to March 2021) can be marked, the model outputs validated against actual data and potential suppression and extension from expected performance identified. From April 2021, the forecast period allows scenarios to be run which simulate planned capacity (assuming surgery places per week) or a defined maximum number of patients which can be on the waiting list at one time.
A priority system is embedded in the model (currently 1-4 where 1 is the most urgent) which ensures that cases go for surgery based on clinical urgency but are time-stamped at referral so that after a time limit the case is escalated to the front of the queue. The model also allows referral patterns to adjusted in the forecast period if suppression continues into the third Covid wave.
The principal benefit to clients, namely commissioners and those planning care capacity, is to dynamically understand the number of patients waiting for elective surgery, and their time on the waiting list, based on clinical capacity available. Inputting capacity constraints over time and the ability to calculate additional capacity required to return to pre-Covid (or an accepted level of) waiting times and volume are additional benefits. For example, the graph below shows the average waiting list size at each week from the model inception with the backlog increasing at the start of the first Covid lockdown but which has not yet been given additional capacity to reduce it.
Early interpretation of the modelled outputs show that further clinician input to capacity planning across specialties (for example specialties compete for limited clinical space) and for changes to waiting list dynamics through mortality rates.