CASE STUDY potential weekly surgical capacity from 267 procedures to 331 – a 27 % increase – without adding operating rooms, staff or other resources.
That result is interesting in its own right. But for the quantum-computing industry, the more important achievement may be the model itself.
The scheduling problem hiding in plain sight
A surgical schedule sounds like a straightforward spreadsheet exercise.
Projects like this are essential to understanding where quantum technologies can create real value in optimization tasks.
There are patients waiting for procedures. There are operating rooms. There are surgeons and other clinical staff. Put the patients into the available slots and make the most of the week.
The result is a combinatorial problem: changing one decision can alter the feasibility and value of dozens of others.
Hospital del Mar already had a well-established manual planning process, designed around the robustness required in real clinical operations. The Qilimanjaro model did not simply attempt to automate that process. Instead, it expanded the number of possible combinations planners could consider.
For decades, hospitals have dealt with this kind of problem through a combination of expertise, rules and careful planning.
Now Hospital del Mar is asking whether a machine can do something humans struggle to do: consider thousands of interacting decisions at once.
As part of Q-CARE, a project funded by CDTI, Spain ' s public business entity for promoting innovation and technological development, the hospital has spent the past year working with Qilimanjaro Quantum Tech on surgical scheduling. The immediate goal was not to put a quantum computer in the operating theatre.
It was to find out whether the problem could be formulated precisely enough to become a candidate for quantum optimisation in the future.
So far, yes.
Using hospital data and simulated scenarios, the team built an optimisation model that increased
Rather than focusing only on patients at the top of the waiting list, the model could evaluate a wider pool of candidates while simultaneously considering clinical, staffing, operating-room and recovery constraints.
That is where optimisation can expose opportunities that are difficult to see from inside a spreadsheet – or even from years of operational experience.
First, forget quantum
The team had to define the problem mathematically, assemble usable data and demonstrate that solving it could produce a meaningful result.
Working with data covering 24 types of surgery, including procedure duration, recovery requirements and waiting-list guarantees, the researchers built a stochastic optimisation model capable of planning an entire week.
It accounts for uncertainty in operation and recovery times while coordinating patient selection, room allocation and start times. Most importantly, it keeps the recovery unit within capacity throughout the schedule. www. intelligentcio. com
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