Walkthrough of waiting list functions

Setup

First, we’ll add the initial data we need, taken from the white paper.

# Queue size (patients)
queue_size <- 1200

# Waiting time target (weeks)
waiting_time_target <- 52

# Average waiting time in the queue (weeks)
avg_waiting_time <- 63

# Proportion of waiting list who have missed the 52 week target (%)
perc_missing_target <- 0.51

# Demand (patients per week)
demand <- 30

# Capacity (procedures per week)
capacity <- 27

# Standard deviation of number of operations per week
std_dev_procedures <- 160

Demand, capacity, and load

Fact 1: Capacity must be larger than demand, otherwise the waiting list size will grow indefinitely.

load <- calc_queue_load(demand, capacity)
load
#> [1] 1.111111

We see that the load is 1.11, which is greater than 1. The queue will therefore grow in size indefinitely.

Fact 2: If the load is greater than 1, then the queue is unstable, and the waiting list will grow indefinitely. If the load is less than 1, then the queue will be stable and the load is the proportion of the time that that waiting list is non-empty.

Waiting list targets

Fact 3: If the load on a queue is less than 1 then the chance of missing the target halves each time we increase the target by some fixed number of days.

Fact 4: If we want to have a chance between 1.8%-0.2% of not achieving a waiting time target, then the average patient should have a waiting time between a quarter and a sixth of the target.

In the case of a P4 waiting list, the target wait is 52 weeks. Thus, we should expect the average patient being operated on to have waited between 9 and 13 weeks. In the case of P2 customers, the target is 4 weeks. Thus, the mean wait of a typical patient should be under one week.

target_mean_wait <- calc_target_mean_wait(waiting_time_target)
target_mean_wait
#> [1] 13

We see that the target mean wait is 13 weeks.

Target queue length

Fact 5: Little’s Law. Assuming capacity exceeds demand, the average queue size is demand multiplied by average waiting time.

If, as given in Fact 4 above, we want the average waiting time to be a quarter of the target, then Little’s Law leads to fact 6.

Target queue size

Fact 6: Target queue size is demand multiplied by target wait, divided by 4.

target_queue_size <- calc_target_queue_size(demand, waiting_time_target)
target_queue_size
#> [1] 390

queue_ratio <- queue_size / target_queue_size
queue_ratio
#> [1] 3.076923

In this example, the target queue size is 390, and the actual queue is 1200. The queue ratio is 3.1, meaning that the queue is 3.1 times its target size.

If the waiting list size is over twice the target queue size, then we consider that special measures are needed to increase capacity, and reduce waiting list size.

Relief capacity

Fact 7: If the actual queue size is more than double the target queue size, then decide on a target date by which the queue will be brought down, and apply the necessary relief capacity.

weeks_until_target_acheived <- 26

relief_capacity <- calc_relief_capacity(
  demand = demand,
  queue_size = queue_size,
  target_queue_size = target_queue_size,
  time_to_target = weeks_until_target_acheived
)
relief_capacity
#> [1] 61.15385

In this example, we decide that the target should be achieved by the start of the summer, 26 weeks away. To do this, the capacity needed is 61.2 procedures per week, compared to 27 procedures per week currently being performed.

Target capacity

As discussed above if the queue size is more than double its target then capacity should be increased temporarily. However, once the queue size is within an acceptable range, we can maintain the waiting time target with what is potentially a much smaller capacity allocation to the waiting list.

We know the mean waiting time (13 weeks) and queue size (390 patients) of a waiting list operating at its target equilibrium. Now we calculate a capacity allocation that will maintain this equilibrium in the long run.

Fact 8: Target capacity formula, based on the Pollaczek-Khinchine formula. The target capacity depends on demand, plus an additional capacity which is based on service variability, and the waiting time target.

The parameter “F” depends on the variability of the service. If we do not know F, we can assume F = 1. Values less than 1 are good. Higher values represent more variability, which in turn will increase the capacity required to maintain equilibrium.

# set the "F" variability parameter
f_1 <- 1

target_capacity_1 <- calc_target_capacity(
  demand = demand,
  target_wait = waiting_time_target,
  factor = f_1
)
target_capacity_1
#> [1] 30.01923

If F is 1, we can see that the capacity required is 30.02, or 0.02 more than the demand.

f_2 <- 6.58

target_capacity_2 <- calc_target_capacity(
  demand = demand,
  target_wait = waiting_time_target,
  factor = f_2
)
target_capacity_2
#> [1] 30.12654

If F is 6.58, we can see that the capacity required is 30.13, or 0.13 more than the demand.

So, decreasing variability of service (for example by stabilising operating theatre schedules from day to day and week to week) has the effect of reducing the capacity required to achieve a given service waiting standard.

Waiting list pressure

Fact 9: Waiting list pressure. For a waiting list with target waiting time, the pressure on the waiting list is twice the mean waiting time divided by the target waiting time. The pressure of any given waiting list should be less than 1. If the pressure is greater than 1 then the waiting list is most likely going to miss its target.

Measuring waiting list pressure can give a comparative measure with which to compare waiting lists, and help make resource allocation decisions.

For the P4 ENT example we have been following:

waiting_list_pressure_p4 <-
  calc_waiting_list_pressure(
    avg_waiting_time,
    waiting_time_target
  )
waiting_list_pressure_p4
#> [1] 2.423077

The queue size is large, with 1200 patients waiting. The waiting time target is 52 weeks, and the average waiting time being experienced is 63 weeks. This gives a waiting list pressure of 2.42. NOTE these numbers are slightly different to the white paper, which changes the average waiting time from 63 weeks to 61 weeks during the example.

If we look at the P2 ENT example:

queue_size_p2 <- 220
avg_waiting_time_p2 <- 24
waiting_time_target_p2 <- 4

waiting_list_pressure_p2 <-
  calc_waiting_list_pressure(
    avg_waiting_time_p2,
    waiting_time_target_p2
  )
waiting_list_pressure_p2
#> [1] 12

The queue size is smaller, with 220 patients waiting. The waiting time target is 4 weeks, and the average waiting time being experienced is 24. This gives a waiting list pressure of 12.

In these two examples the pressure on the much shorter P2 waiting list is 5 times higher than that on the P4 list. Closer attention should be paid to P2 procedures.

Summary

This worked example aims to demonstrate the functions available in this package. In chronological order of application they were:

Further reading

For examples of practical applications, and other considerations, see the helpful “Case Studies” section towards the end of the white paper.

END