A summary of all the key stats associated with a waiting list
Value
A data.frame of key waiting list summary statistics based on queueing theory:
- mean_demand
- Numeric. Mean number of additions to the waiting list per week. 
- mean_capacity
- Numeric. Mean number of removals from the waiting list per week. 
- load
- Numeric. Ratio between demand and capacity. 
- load_too_big
- Logical. Whether the load is greater than or equal to 1, indicating whether the waiting list is unstable and expected to grow. 
- count_demand
- Numeric. Total demand (i.e., number of referrals) over the full time period. 
- queue_size
- Numeric. Number of patients on the waiting list at the end of the time period. 
- target_queue_size
- Numeric. The recommended size of the waiting list to achieve approximately 98.2% of patients being treated within their target wait time. This is based on Little’s Law, assuming the system is in equilibrium, with the average waiting time set to one-quarter of the - target_wait.
- queue_too_big
- Logical. Whether - queue_sizeis more than twice the- target_queue_size. A value of- TRUEindicates the queue is at risk of missing its targets.
- mean_wait
- Numeric. Mean waiting time in weeks. 
- cv_arrival
- Numeric. Coefficient of variation in the time between additions to the waiting list. 
- cv_removal
- Numeric. Coefficient of variation in the time between removals from the waiting list. 
- target_capacity
- Numeric. The weekly treatment capacity required to maintain the waiting list at its target equilibrium, assuming the target queue size has been reached. 
- relief_capacity
- Numeric. The temporary weekly capacity required to reduce the waiting list to its - target_queue_sizewithin 26 weeks, assuming current demand remains steady. Calculated only if- queue_too_bigis- TRUE; otherwise returns- NA.
- pressure
- Numeric. A measure of pressure on the system, defined as - 2 × mean_wait / target_wait. Values greater than 1 suggest the system is unlikely to meet its waiting time targets.
Examples
referrals <- c.Date("2024-01-01", "2024-01-04", "2024-01-10", "2024-01-16")
removals <- c.Date("2024-01-08", NA, NA, NA)
waiting_list <- data.frame("referral" = referrals, "removal" = removals)
waiting_list_stats <- wl_stats(waiting_list)