By Evelyne Priestman, Senior Health Economist at Health Tech Enterprise
How do you model waiting list behaviour in a healthcare system?
Waiting times are a hot topic for the UK Healthcare system. The waiting list for NHS hospital treatment rose to a record of nearly 7.8 million patients in September 2023. The 18-week treatment target has not been met since 2016, and in September 2023, after an urgent GP referral, 59% of patients waited under 62 days for cancer treatment compared with a target of 85%. Of those attending A&E, 50% experienced a waiting time of greater than four hours and the number of patients waiting over twelve hours for admission to hospital, after the decision to admit has been taken, has increased substantially.
In a single-payer system such as the NHS, where demand is not modulated by willingness to pay, the need to ration access to care is inevitable. However, there is a fine line between using waiting lists to modulate demand in an economic system and excessive delays in treatment causing harm to the patients which the system is trying to help.
Much hope is pinned on innovations in the digital health space to help reduce waiting times by increasing system efficiencies. But how can innovators substantiate their claims of reduced waiting times without convincing those in charge of delivering care to pilot the innovation within an already strained system. What can innovators do early on to convince decision-makers and get access to the necessary funding to demonstrate what they can achieve?
Queuing Analytics and Discrete Event Simulation
One solution is to use a mathematical approach often employed to maximize efficiencies in resource-constrained systems such as inventory management and urban freight logistics; Discrete Event Simulation (DES), with queuing analytics (QA), can be used to simulate the journey of each patient, taking into account prioritization, system capacity, patient inflow and patient outflow. These so-called dynamic models allow for interactions between data parameters; the time an individual spends on the waiting list depends on the underlying capacity of the system, the total number of people on the waiting list itself, and the duration of time that this specific individual has already spent on the waiting list (i.e. a specific patient’s history). This modelling approach allows for the calculation of the average waiting time of a patient on the waiting list, the expected number of patients on the waiting list and the evolution of the waiting list over time.
This can offer valuable insights into the efficiency of current operations and how these can be further improved with the help of innovation.
Figure 1 – Example of a DES approach to simulating waiting lists
By using discrete event simulation, the impact of an innovation on waiting lists can be modelled in a controlled and data-driven environment. Benefits such as, for example, shortening the average consultation time can increase the capacity of the system and thus yield results on waiting times. This modelling approach can help innovators gain valuable insight into the potential for their innovation to create a positive change on the performance of the system as a whole and can be used to convince gatekeepers and healthcare decision-makers of the value of an innovation in helping to reduce patient waiting times.