Reducing the waiting time for a surgery

The company

The emergence of COVID-19 led to the postponement of surgeries and caused an increase in the patient backlog.

In Portugal, in May 2020, there were 242k patients on the waiting list for surgery, of which more than 100k surpassed the recommended waiting time and 45k were waiting for more than a year for their operation (Source: ACSS, May 2020).

This poses a great challenge for 21st century medical management. Currently, decision- makers have no visibility over the workload on the waiting list. Typically, hospital resources - rooms, beds, and doctors - are not flexible on adjusting their schedule to the needs of the waiting list. A paradigm shift brings profits to everyone – patients get a better and quicker service, while resource utilisation is maximised by decreasing current idle times due to demand fluctuation.

The challenge

Any hospital centre is composed of a big and complex workforce - from teams of doctors of distinct specialities as well as nurses and the administrative staff.

Planning everyone’s availability and schedule is already a challenging task. However, the surgery appointment allocation problem is much more complex than that since the required rooms and beds must also be considered. On top of this, rooms for certain surgeries have specific mandatory materials and machines which create incompatibilities and further restrictions.

So, how can a hospital, team or specialty manager use the available information to make data-driven decisions?

• What is the best allocation of my resources based on the current mix of demand on the waiting list?
• Should I dedicate more beds for a certain specialty, for example, vascular surgery, instead of pneumology, since the demand is currently higher?
• If I increase the number of working hours of my doctors, how many extra surgeries will I perform?

These are crucial questions that support any decision-maker. It enables the simulation of different scenarios for various levels of capacity and demand, all based on real data and numbers.

The approach

First, we start by compiling historical data to estimate the average surgery time and the inpatients after surgery hospitalisation time. This will be needed to calculate the occupation of rooms as well as the necessary number of beds.

Second, we cross-check this information with current rooms available, pre-defined allocation by medical specialty and the doctors’ scheduled availability.

Now, we are ready to start the allocation process. The simulator will take the surgery patient waiting list, order it by entry date (FIFO) and by the priority level associated to each medical case, which was previously assessed by another doctor. The first surgery is allocated only when the three main resources – doctor, room, and bed - are available at the same time slot and if the load-capacity ratio of each allows it.

To assess the solution and impact of different scenarios, a visual dashboard was built to monitor the main metrics and identify bottleneck resources, day by day.

Achievements

Any hospital manager or team leader, who needs to better estimate and make real-time decisions about its workforce, must have such a tool. It supports analysis for setting the schedules of a team, the allocation of rooms and adjusts the number of beds needed.

It provides visibility on critical operational KPI, such as the total resources cost, the average waiting time and the response rate on the surgery waiting list.

This model is a flexible ‘turn-key’ tool that can be applied, not only to any medical centre, but also to any business area whenever there are teams and resources with different key responsibilities and skills that must be coordinated to achieve a certain task or finish a project.

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