Computer models can help design specialist health clinics, giving patients the better care and using clinic resources efficiently.

Ellen Gibbs created a simulation for a clinic dedicated to treating lymphoedema.

Lymphoedema causes limbs to swell up with lymph fluid and reduces patients’ quality of life. The blockage is usually caused by damage done to the lymphatic system during surgery to remove cancer. It is commonly associated with breast cancer.

Patients with lymphoedema need stringent treatment and surveillance, and often need urgent access to healthcare.

Simple computer modelling might assume that patients who make appointments first get the first available time slots, but lymphoedema patients – who have regular appointments during treatment – often book their next appointment well in advance.

Patients at risk of getting the disease should visit a lymphoedema clinic for a half-hour observation within one month of surgery and visit again every one-to-two months for the first year, then once a year if they don’t develop lymphoedema.

Patients who develop the disease need regular appointments for treatment, massages and education. These appointments are usually an hour long and are scheduled every month for the first six months, then every two-to-three months until the disease eases.

The simulation can make this process more efficient by doing education sessions in groups to serve several patients at once.

The simulation also addresses staff rostering. It allocates staff so the appropriate expertise is always available for patients who need it, and minimises the amount of time any staff member is idle.

It does this using a complex queuing model based on historical data. The model assigns each staff member on duty to a patient and considers staff members’ expertise when allocating them. It maximises employee satisfaction while minimising costs.

Each virtual patient in the simulation has the same risk of developing lymphoedema, and is allocated random variables such as the amount of time between appointments and the length of appointments.

Each patient has a series of steps they follow in the simulation. Some may enter the system after surgery, while others might begin their treatment at another facility and then transfer. Some patients visit for observation and some for treatment. Someone can change from an observation patient to a treatment patient and vice versa, or leave the system when they no longer need surveillance.

Ellen ran a series of 40-year simulations. The data show the rate of new arrivals, number of appointments per week, rate of patients leaving the system, and other information. These can be used to predict when a clinic will need new staff.

The simulation can be compared with real life data and tweaked accordingly. It can also be easily adapted to match clinics and hospitals with different staff numbers.

Ellen suggests future work on this simulation could include the level of swelling in patients’ limbs at appointments, which would indicate what stage of treatment they are at; and assigning patients different probabilities of developing lymphoedema based on how severe their cancer and surgery were.

Ellen Gibbs is one of 10 students who took part in the Summer of Research programme funded by Precision Driven Health. The research is at an early “proof of concept” stage. The projects offer fresh insights into what healthcare will look like when precision medicine is widely used.