Creating an optimal schedule is like looking for a needle in a haystack.

There are 1020 different scheduling combinations in a department of a healthcare facility with 50–100 workers. AI tells which one is the friendliest to employees.  

When compiling a new worker schedule for a company with 50 – 100 workers there can be as many as 1020 different scheduling combinations. Sometimes there is only one solution or even no solution. Most commonly, there are more than a million suitable solutions. It is impossible to expect the person making the schedule to consider all the options and choose the most optimal one from the set.

This is precisely what artificial intelligence can do. WoShi is a healthcare innovation that enables automated planning, scheduling, staffing and reporting. Therefore, it contributes significantly to solving staff challenges in the health sector.

Artificial intelligence that works like the evolution

Evolution succeeds through changes in the genetic code. Successful genetic combinations that are well adapted to the environment survive, the rest do not. This enabled even the most complex creatures to adapt to the circumstances.

Evolutionary algorithms function according to the same principle: they are trial-and-error optimisation procedures following the example of biological evolution. The evolutionary algorithm has been known since at least the time of Darwin and Wallace. All previous technological implementations of evolutionary algorithms were aimed at the development of simple structures, since the algorithms were still too demanding for human mathematical abilities. Algorithms, which are the basis of any software, have so far been invented or improved by humans. This area has changed dramatically with the onset of artificial intelligence.

Evolutionary artificial intelligence (EAI) is a method that we designed based on evolutionary algorithms. This is how the WoShi worker scheduling program was developed, and is the first and currently the only program for worker schedueling that operates on the basis of evolutionary artificial intelligence.

As such it addresses the most demanding calculations for scheduling medical teams. The result is a schedule that complies with the majority of wishes and is compiled with the least “violations” of the requirements.

The kind that cannot be compiled or by algorithms that are not based on evolutionary artificial intelligence.